User login
Detecting and treating delirium—key interventions you may be missing
• Nonpharmacologic interventions are the mainstay of treatment for delirium. B
• When medication is needed, atypical antipsychotics are as effective as typical antipsychotics for treating delirium in elderly patients, and have fewer side effects. B
• Benzodiazepines should be avoided in elderly patients with delirium that is not associated with alcohol withdrawal. A
Strength of recommendation (SOR)
A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series
CASE Mr. D, a 75-year-old patient with a history of hypertension and congestive heart failure, sustained a femoral neck fracture and was admitted to the hospital for surgery. He underwent open reduction and internal fixation and was doing well postoperatively, until Day 2—when his primary care physician made morning rounds and noted that Mr. D was somnolent. The nurse on duty assured the physician that Mr. D was fine and “was awake and alert earlier,” and attributed his somnolence to the oxycodone (10 mg) the patient was taking for pain. The physician ordered a reduction in dosage.
If Mr. D had been your patient, would you have considered other possible causes of his somnolence? Or do you think the physician’s action was sufficient?
Derived from Latin, the word delirium literally means “off the [ploughed] track.”1 Dozens of terms have been used to describe delirium, with acute confusion state, organic brain syndrome, acute brain syndrome, and toxic psychosis among them.
Delirium has been reported to occur in 15% to 30% of patients on general medical units,2 about 40% of postoperative patients, and up to 70% of terminally ill patients.3 The true prevalence is hard to determine, as up to 66% of cases may be missed.4
Delirium is being diagnosed more frequently, however—a likely result of a growing geriatric population, increased longevity, and greater awareness of the condition. Each year, an estimated 2.3 million US residents are affected, leading to prolonged hospitalization; poor functional outcomes; the development or worsening of dementia; increased nursing home placement; and a significant burden for families and the US health care system.5
Delirium is also associated with an increase in mortality.6,7 The mortality rate among hospitalized patients who develop delirium is reported to be 18%, rising to an estimated 47% within the first 3 months after discharge.6 Greater awareness of risk factors, rapid recognition of signs and symptoms of delirium, and early intervention—detailed in the text and tables that follow—will lead to better outcomes.
Assessing risk, evaluating mental status
In addition to advanced age, risk factors for delirium (TABLE 1)8-14 include alcohol use, brain dysfunction, comorbidities, hypertension, malignancy, anticholinergic medications, anemia, metabolic abnormalities, and male sex. In patients who, like Mr. D, have numerous risk factors, early—and frequent—evaluation of mental status is needed. One way to do this is to treat mental status as a vital sign, to be included in the assessment of every elderly patient.15
The Confusion Assessment Method, a quick and easy-to-use delirium screening tool (TABLE 2), has a sensitivity of 94% to 100% and a specificity of 90% to 95%.16,17 There are a number of other screening tools, including the widely used Mini-Mental State Exam (MMSE), as well as the Delirium Rating Scale, Delirium Symptom Interview, and Delirium Severity Scale.
TABLE 1
Risk factors for delirium8-14
Advanced age Alcohol use Brain dysfunction (dementia, epilepsy) Hypertension Male sex Malignancy Medications (mainly anticholinergic) Metabolic abnormalities:
Old age Preoperative anemia Preoperative metabolic abnormalities |
| BUN, blood urea nitrogen; Cr, creatinine; Na, sodium. |
TABLE 2
Screening for delirium: The Confusion Assessment Method*16,17
| Criteria | Evidence Yes to questions 1, 2, and 3 plus 4 or 5 (or both) suggests a delirium diagnosis |
|---|---|
| 1. Acute onset | Is there evidence of an acute change in mental status from the patient’s baseline? |
| 2. Fluctuating course | Did the abnormal behavior fluctuate during the day—ie, tend to come and go or increase and decrease in severity? |
| 3. Inattention | Did the patient have difficulty focusing attention, eg, being easily distractible or having difficulty keeping track of what was being said? |
| PLUS | |
| 4. Disorganized thinking | Was the patient’s thinking disorganized or incoherent, such as rambling or irrelevant conversation, unclear or illogical flow of ideas, or unpredictable switching from subject to subject? |
| 5. Altered level of consciousness | Would you rate the patient’s level of consciousness as (any of the following): – Vigilant (hyperalert) – Lethargic (drowsy, easily aroused) – Stupor (difficult to arouse) – Coma (unarousable) |
| *CAM shortened version worksheet. Adapted from: Inouye SK et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948; Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. Copyright 2003, Hospital Elder Life Program, LLC. | |
Arriving at a delirium diagnosis
The clinical presentation of delirium is characterized by acute—and reversible—impairment of cognition, attention, orientation, and memory, and disruption of the normal sleep/wake cycle. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria for a delirium diagnosis include all of the following:
- disturbance of consciousness, with a reduced ability to focus, sustain, or shift attention
- change in cognition, or a perceptual disturbance, that is not accounted for by a preexisting or developing dementia
- rapid onset of cognitive impairment, with fluctuation likely during the course of the day
- evidence from the history, physical exam, or laboratory findings that the disturbed consciousness is a direct physiological consequence of a general medical condition.17
There are 3 basic types of delirium, each associated with a different psychomotor disturbance.
- Hyperactive delirium—the least common—is characterized by restlessness and agitation, and is therefore the easiest to diagnose.
- Hypoactive delirium is characterized by psychomotor retardation and hypoalertness. It is often misdiagnosed as depression, and has the poorest prognosis.
- Mixed delirium—the most common—is characterized by symptoms that fluctuate between hyper- and hypoactivity.18
CASE By lunchtime, Mr. D had awakened; however, he needed help with his meal. After eating, he slept for the rest of the day. At night, a nurse paged the resident to report that the patient’s blood pressure was 82/60 mm Hg and his heart rate was 115. The physician ordered an intravenous fluid bolus, which corrected the patient’s hypotension, but only temporarily.
The fluctuating nature of delirium—most notably, in patients’ level of alertness—is helpful in establishing a diagnosis. The history and physical exam are the gold standard tools, both for diagnosing delirium and identifying the underlying cause (TABLE 3).19,20 A review of the patient’s medications should be a key component of the medical history, as drugs—particularly those with anticholinergic properties—are often associated with delirium. Environmental shifts, including hospitalization and a disruption of the normal sleep/wake cycle, endocrine disorders, infection, and nutritional deficiencies are also potential causes of delirium, among others.
If history and physical exam fail to identify the underlying cause, laboratory testing, including complete blood count, complete metabolic profile, and urinalysis, should be done. Brain imaging is usually not needed for individuals with symptoms of delirium, but computed tomography (CT) may be indicated if a patient’s condition continues to deteriorate while the underlying cause remains unidentified.21 Electroencephalography (EEG) may be used to confirm a delirium diagnosis that’s uncertain, in a patient with underlying dementia, for instance. (In more than 16% of cases of delirium, the cause is unknown.22)
The most common structural abnormalities found in patients with delirium are brain atrophy and increased white matter lesions, as well as basal ganglia lesions.23 Single-photon emission CT (SPECT) shows a reduction of regional cerebral perfusion by 50%,24 while EEG shows slowing of the posterior dominant rhythm and increased generalized slow-wave activity.25
TABLE 3
A DELIRIUM mnemonic to get to the heart of the problem19,20
| Cause | Comment |
|---|---|
| Drugs | Drug classes: Anesthesia, anticholinergics, anticonvulsants, antiemetics, antihistamines, antihypertensives, antimicrobials, antipsychotics, benzodiazepines, corticosteroids, hypnotics, H2 blockers, muscle relaxants, NSAIDs, opioids, SSRIs, tricyclic antidepressants Drugs: digoxin, levodopa, lithium, theophylline OTCs: henbane, Jimson weed, mandrake, Atropa belladonna extract |
| Environmental | Change of environment, sensory deprivation, sleep deprivation |
| Endocrine | Hyperparathyroidism, hyper-/hypothyroidism |
| Low perfusion | MI, pulmonary embolism, CVA |
| Infection | Pneumonia, sepsis, systemic infection, UTI |
| Retention | Fecal impaction, urinary retention |
| Intoxication | Alcohol, illegal drugs/drug overdose |
| Undernutrition | Malnutrition, thiamin deficiency, vitamin B12 deficiency |
| Metabolic | Acid-base disturbances, fluid and electrolyte abnormalities, hepatic or uremic encephalopathy, hypercarbia, hyper-/hypoglycemia, hyperosmolality, hypoxia |
| Subdural | History of falls |
| CVA, cerebrovascular accident; MI, myocardial infarction; NSAIDs, nonsteroidal anti-inflammatory drugs; OTCs, over-the-counter agents; SSRIs, selective serotonin reuptake inhibitors; UTI, urinary tract infection. | |
Treating (or preventing) delirium: Start with these steps
Nonpharmacologic interventions are the mainstay of treatment for patients with delirium, and may also help to prevent the development of delirium in patients at risk. One key measure is to correct, or avoid, disruptions in the patient’s normal sleep/wake cycle—eg, restoring circadian rhythm by avoiding,
to the extent possible, awakening the patient at night for medication or vital signs. Preventing sensory deprivation, by ensuring that the patient’s eyeglasses and hearing aid are nearby and that there is a clock and calendar nearby and adequate light, is also helpful. Other key interventions (TABLE 4)26-28 include:
- limiting medications associated with delirium (and eliminating any nonessential medication)
- improving nutrition and ambulation
- correcting electrolyte and fluid disturbances
- treating infection
- involving family members in patient care
- ensuring that patients receive adequate pain management
- avoiding transfers (if the patient is hospitalized) and trying to secure a single room.
Several studies have evaluated the effectiveness of nonpharmacologic interventions in preventing or lowering the incidence of delirium. A large multicomponent delirium prevention study of patients >70 years on general medical units focused on managing risk factors. The interventions studied included (1) avoidance of sensory deprivation, (2) early mobilization, (3) treating dehydration, (4) implementing noise reduction strategies and sleep enhancement programs, and (5) avoiding the use of sleep medications. These interventions proved to be effective not only in lowering the incidence of delirium, but in shortening the duration of delirium in affected patients (NNT=20).27
One study found that proactively using a geriatric consultation model (ie, implementing standardized protocols for the management of 6 risk factors) for elderly hospitalized patients led to a reduction in the incidence of delirium by more than a third.26 Admission to a specialized geriatric unit is associated with a lower incidence of delirium compared with being hospitalized on a general medical unit.29
Reducing the incidence of postoperative delirium. Bright light therapy (a light intensity of 5000 lux with a distance from the light source of 100 cm), implemented postoperatively, may play a role in reducing the incidence of delirium, research suggests.30 Music may be helpful, as well. An RCT involving patients (>65 years) undergoing elective knee or hip surgery found that those who listened to classical music postoperatively had a lower incidence of delirium.31 Similarly, playing music in nursing homes has been shown to decrease aggressive behavior and agitation.32
TABLE 4
Helpful interventions in the hospital or at home26-28
|
When medication is needed, proceed with caution
None of the medications currently used to treat delirium are approved by the US Food and Drug Administration for this indication, and many of them have substantial side effects. Nonetheless, palliative or symptomatic treatment requires some form of sedation for agitated patients with delirium. Thus, it is necessary to strike a balance in order to manage the symptoms of delirium and avoid potential side effects (primarily, sedation). Overly sedating patients can confuse the clinical picture of delirium and make it difficult to differentiate between ongoing delirium and medication side effects. Medication should be started at a low, but frequent, dose to achieve an effective therapeutic level, after which a lower maintenance dose can be used until the cause of delirium is resolved.
Antipsychotics are the cornerstone of drug treatment
Haloperidol has traditionally been used to treat delirium33 and has proven effectiveness. However, it is associated with increased risk of extrapyramidal manifestations compared with atypical antipsychotics.
Atypical antipsychotics (olanzapine, risperidone, quetiapine) are increasingly being used to treat delirium because they have fewer extrapyramidal side effects.34 With the exception of olanzapine (available in intramuscular and oral disintegrating form), atypical antipsychotics are available only in oral form, which may limit their usefulness as a treatment for agitated, delirious patients.
Risperidone (at a dose ranging from 0.25 to 1 mg/d) and olanzapine (1.25 to 2.5 mg/d) have shown similar efficacy to haloperidol (0.75 to 1.5 mg/d) in both the prevention and treatment of delirium, but with fewer extrapyramidal side effects.35-39 Quetiapine, a second-generation antipsychotic, is widely used to treat inpatient delirium, although there are no large RCTs comparing it with placebo. One pilot study and another open-label trial found the drug to be beneficial for patients with delirium, with fewer extrapyramidal side effects than haloperidol.40,41
Do a risk-benefit analysis. The use of antipsychotics in elderly patients with delirium has been associated with increased morbidity and mortality. The incidence of stroke and death were higher for community-dwelling patients (NNH=100) and patients in long-term care (N=67) who received typical or atypical antipsychotics for 6 months compared with that of patients who did not receive any antipsychotics.42,43 Thus, a risk-benefit analysis should be done before prescribing antipsychotics for elderly patients. Both typical and atypical antipsychotics carry black box warnings of increased mortality rates in the elderly.
Other drugs for delirium? More research is needed
Cholinesterase inhibitors. Procholinergic agents would be expected to be helpful in treating delirium, as cholinergic deficiency has been implicated as a predisposing factor for delirium and medications with anticholinergic effects have been shown to induce delirium. However, several studies of cholinesterase inhibitors have not found this to be the case.44-47
Benzodiazepines. There is no evidence to support the use of benzodiazepines in the treatment of delirium, except when the delirium is related to alcohol withdrawal.48 When indicated, the use of a short-acting benzodiazepine such as lorazepam is preferred for elderly patients (vs long-acting agents like diazepam) because of its shorter half-life and better side effect profile.2 Drowsiness, ataxia, and disinhibition are common side effects of benzodiazepines.
Gabapentin. A pilot study conducted to assess the efficacy of gabapentin (900 mg/d) for the prevention of postoperative delirium found a significantly lower incidence of delirium among patients who received gabapentin compared with placebo. This may be associated with gabapentin’s opioid-sparing effect.49 Larger studies are needed to recommend for or against the use of gabapentin in patients receiving opiates.
Further study of the pathophysiology of delirium is needed, as well, to increase our ability to prevent and treat it.
CASE After receiving the IV fluid bolus, Mr. D became increasingly short of breath and required more oxygen to keep his oxygen saturation in the 90s. Labs were ordered during morning rounds, and the patient was found to have urosepsis. He was admitted to the ICU in septic shock, and was intubated and died several days later.
In retrospect, it was determined that Mr. D had developed hypoactive delirium brought on by the infection—and that his somnolence on the second postoperative day was not a sign of overmedication. Had this been recognized early on through the use of an appropriate screening tool, the outcome would likely have been more favorable.
CORRESPONDENCE Abdulraouf Ghandour, MD, Green Meadows Clinic University Physicians, 3217 Providence Road, Columbia, MO 65203; Ghandour78@gmail.com
1. Casselman WG. Dictionary of Medical Derivations. The Real Meaning of Medical Terms. New York, NY: Informa Healthcare; 1998.
2. Kiely DK, Bergmann MA, Murphy KM, et al. Delirium among newly admitted postacute facility patients, prevalence, symptoms, and severity. J Gerontol Biol Sci Med Sci. 2003;58:M441-M445.
3. Inouye SK, Charpentier PA. Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability. JAMA. 1996;275:852-857.
4. Inouye SK. The dilemma of delirium: clinical and research controversies regarding diagnosis and evaluation of delirium in hospitalized elderly medical patients. Am J Med. 1994;97:278-288.
5. Pompei P, Foreman M, Rudberg M, et al. Delirium in hospitalized older persons: outcomes and predictors. J Am Geriatr Soc. 1994;42:809-815.
6. Kolbeinsson H, Jonsson A. Delirium and dementia in acute medical admissions of elderly patients in Iceland. Acta Psychiatr Scand. 1993;87:123-127.
7. Cole MG, Primeau FJ. Prognosis of delirium in elderly hospital patients. CMAJ. 1993;149:41-46.
8. Rahkonen T, Eloniemi-Sulkava U, Halonen P, et al. Delirium in the non-demented oldest old in the general population: risk factors and prognosis. Int J Geriatr Psychiatry. 2001;16:415-421.
9. Edlund A, Lundstrom M, Brannstrom B, et al. Delirium before and after operation for femoral neck fracture. J Am Geriatr Soc. 2001;49:1335-1340.
10. Andersson EM, Gustafson L, Hallberg IR. Acute confusional state in elderly orthopaedic patients: factors of importance for detection in nursing care. Int J Geriatr Psychiatry. 2001;16:7-17.
11. Inouye SK, Viscoli CM, Horwitz RI, et al. A predictive model for delirium in hospitalized elderly medical patients based on admission characteristics. Ann Intern Med. 1993;119:474-481.
12. Marcantonio ER, Juarez G, Goldman L, et al. The relationship of postoperative delirium with psychoactive medications. JAMA. 1994;272:1518-1522.
13. Marcantonio ER, Goldman L, Orav EJ, et al. The association of intraoperative factors with the development of postoperative delirium. Am J Med. 1998;105:380-384.
14. Tune L, Carr S, Hoag E, et al. Anticholinergic effects of drugs commonly prescribed for the elderly: potential means for assessing risk of delirium. Am J Psychiatry. 1992;149:1393-1394.
15. Flaherty JH, Shay K, Weir C, et al. The development of a mental status vital sign for use across the spectrum of care . J Am Med Dir Assoc. 2009;10:379-380.
16. Inouye SK, Van Dyck CH, Alessi CA, et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948.
17. Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. New Haven, Conn: Yale University School of Medicine; 2003.
18. Halter J, Ouslander J, Tinetti M, et al. Hazzard’s Geriatric Medicine and Gerontology. 6th ed. New York, NY: McGraw-Hill; 2009;648-658.
19. Eriksson S. Social and environmental contributants to delirium in the elderly. Dement Geriatr Cogn Disord. 1999;10:350-352.
20. Francis J, Martin D, Kapoor WN. A prospective study of delirium in hospitalized elderly. JAMA. 1990;263:1097-1101.
21. Francis J, Hilko EM, Kapoor WN. Acute mental change: when are head scans needed? Clin Res. 1991;39:103.-
22. Rudberg MA, Pompei P, Foreman MD, et al. The natural history of delirium in older hospitalized patients: a syndrome of heterogeneity. Age Ageing. 1997;26:169-174.
23. Soiza RL, Sharma V, Ferguson K, et al. Neuroimaging studies of delirium: a systematic review. J Psychosom Res. 2008;65:239-248.
24. Fong TG, Bogardus ST Jr, Daftary A, et al. Cerebral perfusion changes in older delirious patients using 99mTc HMPAO SPECT. J Gerontol A Biol Sci Med Sci. 2006;61:1294-1299.
25. Jacobson SA, Leuchter AF, Walter DO. Conventional and quantitative EEG in the diagnosis of delirium among the elderly. J Neurol Neurosurg Psychiatry. 1993;56:153-158.
26. Marcantonio ER, Flacker JM, Wright RJ, et al. Reducing delirium after hip fracture: a randomized trial. J Am Geriatr Soc. 2001;49:516-522.
27. Inouye SK, Bogardus ST Jr, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340:669-676.
28. Weber JB, Coverdale JH, Kunik ME. Delirium: current trends in prevention and treatment. Intern Med J. 2004;34:115-121.
29. Bo M, Martini B, Ruatta C, et al. Geriatric ward hospitalization reduced incidence delirium among older medical inpatients. Am J Geriatr Psychiatry. 2009;17:760-768.
30. Taguchi T, Yano M, Kido Y. Influence of bright light therapy on postoperative patients: a pilot study. Intensive Crit Care Nurs. 2007;23:289-297.
31. McCaffrey R, Locsin R. The effect of music listening on acute confusion and delirium in elders undergoing elective hip and knee surgery. J Clin Nurs. 2004;13:91-96.
32. Remington R. Calming music and hand massage with agitated elderly. Nurs Res. 2004;51:317-323.
33. Seitz DP, Gill SS, van Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:11-21.
34. Schwartz T, Masand PS. The role of atypical antipsychotics in the treatment of delirium. Psychosomatics. 2002;43:171-174.
35. Lonergan E, Britton AM, Luxenberg J, et al. Antipsychotics for delirium. Cochrane Database Syst Rev. 2007;(2):CD005594.-
36. Hu H, Deng W, Yang H. A prospective random control study comparison of olanzapine and haloperidol in senile delirium. Chongqing Med J. 2004;8:1234-1237.
37. Han CS, Kim YK. A double-blind trial of risperidone and haloperidol for the treatment of delirium. Psychosomatics. 2004;45:297-301.
38. Kim SW, Yoo JA, Lee SY, et al. Risperidone versus olanzapine for the treatment of delirium. Hum Psychopharmacol. 2010;25:298-302.
39. Prakanrattana U, Prapaitrakool S. Efficacy of risperidone for prevention of postoperative delirium in cardiac surgery. Anaesth Intensive Care. 2007;35:714-719.
40. Maneeton B, Maneeton N, Srisurapanont M. An open-label study of quetiapine for delirium. J Med Assoc Thai. 2007;90:2158-2163.
41. Devlin JW, Roberts RJ, Fong JJ, et al. Efficacy and safety of quetiapine in critically ill patients with delirium: a prospective, multicenter, randomized, double-blind, placebo-controlled pilot study. Crit Care Med. 2010;38:419-427.
42. Gill SS, Bronskill SE, Normand SL, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775-786.
43. Wang PS, Schneeweiss S, Avorn J, et al. Death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:2335-2341.
44. Liptzin B, Laki A, Garb JL, et al. Donepezil in the prevention and treatment of post-surgical delirium. Am J Geriatr Psychiatry. 2005;13:1100-1106.
45. Sampson EL, Raven PR, Ndhlovu PN, et al. A randomized, double-blind, placebo-controlled trial of donepezil hydrochloride (Aricept) for reducing the incidence of postoperative delirium after elective total hip replacement. Int J Geriatr Psychiatry. 2007;22:343-349.
46. Gamberini M, Bolliger D, Lurati Buse GA, et al. Rivastigmine for the prevention of postoperative delirium in elderly patients undergoing elective cardiac surgery—a randomized controlled trial. Crit Care Med. 2009;37:1762-1768.
47. Overshott R, Vernon M, Morris J, et al. Rivastigmine in the treatment of delirium in older people: a pilot study. Int Psychogeriatr. 2010;22:812-818.
48. Lonergan E, Luxenberg J, Areosa Sastre A. Benzodiazepines for delirium. Cochrane Database Syst Rev. 2009;(4):CD006379.-
49. Leung JM, Sands LP, Rico M, et al. Pilot clinical trial of gabapentin to decrease postoperative delirium in older patients. Neurology. 2006;67:1251-1253.
• Nonpharmacologic interventions are the mainstay of treatment for delirium. B
• When medication is needed, atypical antipsychotics are as effective as typical antipsychotics for treating delirium in elderly patients, and have fewer side effects. B
• Benzodiazepines should be avoided in elderly patients with delirium that is not associated with alcohol withdrawal. A
Strength of recommendation (SOR)
A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series
CASE Mr. D, a 75-year-old patient with a history of hypertension and congestive heart failure, sustained a femoral neck fracture and was admitted to the hospital for surgery. He underwent open reduction and internal fixation and was doing well postoperatively, until Day 2—when his primary care physician made morning rounds and noted that Mr. D was somnolent. The nurse on duty assured the physician that Mr. D was fine and “was awake and alert earlier,” and attributed his somnolence to the oxycodone (10 mg) the patient was taking for pain. The physician ordered a reduction in dosage.
If Mr. D had been your patient, would you have considered other possible causes of his somnolence? Or do you think the physician’s action was sufficient?
Derived from Latin, the word delirium literally means “off the [ploughed] track.”1 Dozens of terms have been used to describe delirium, with acute confusion state, organic brain syndrome, acute brain syndrome, and toxic psychosis among them.
Delirium has been reported to occur in 15% to 30% of patients on general medical units,2 about 40% of postoperative patients, and up to 70% of terminally ill patients.3 The true prevalence is hard to determine, as up to 66% of cases may be missed.4
Delirium is being diagnosed more frequently, however—a likely result of a growing geriatric population, increased longevity, and greater awareness of the condition. Each year, an estimated 2.3 million US residents are affected, leading to prolonged hospitalization; poor functional outcomes; the development or worsening of dementia; increased nursing home placement; and a significant burden for families and the US health care system.5
Delirium is also associated with an increase in mortality.6,7 The mortality rate among hospitalized patients who develop delirium is reported to be 18%, rising to an estimated 47% within the first 3 months after discharge.6 Greater awareness of risk factors, rapid recognition of signs and symptoms of delirium, and early intervention—detailed in the text and tables that follow—will lead to better outcomes.
Assessing risk, evaluating mental status
In addition to advanced age, risk factors for delirium (TABLE 1)8-14 include alcohol use, brain dysfunction, comorbidities, hypertension, malignancy, anticholinergic medications, anemia, metabolic abnormalities, and male sex. In patients who, like Mr. D, have numerous risk factors, early—and frequent—evaluation of mental status is needed. One way to do this is to treat mental status as a vital sign, to be included in the assessment of every elderly patient.15
The Confusion Assessment Method, a quick and easy-to-use delirium screening tool (TABLE 2), has a sensitivity of 94% to 100% and a specificity of 90% to 95%.16,17 There are a number of other screening tools, including the widely used Mini-Mental State Exam (MMSE), as well as the Delirium Rating Scale, Delirium Symptom Interview, and Delirium Severity Scale.
TABLE 1
Risk factors for delirium8-14
Advanced age Alcohol use Brain dysfunction (dementia, epilepsy) Hypertension Male sex Malignancy Medications (mainly anticholinergic) Metabolic abnormalities:
Old age Preoperative anemia Preoperative metabolic abnormalities |
| BUN, blood urea nitrogen; Cr, creatinine; Na, sodium. |
TABLE 2
Screening for delirium: The Confusion Assessment Method*16,17
| Criteria | Evidence Yes to questions 1, 2, and 3 plus 4 or 5 (or both) suggests a delirium diagnosis |
|---|---|
| 1. Acute onset | Is there evidence of an acute change in mental status from the patient’s baseline? |
| 2. Fluctuating course | Did the abnormal behavior fluctuate during the day—ie, tend to come and go or increase and decrease in severity? |
| 3. Inattention | Did the patient have difficulty focusing attention, eg, being easily distractible or having difficulty keeping track of what was being said? |
| PLUS | |
| 4. Disorganized thinking | Was the patient’s thinking disorganized or incoherent, such as rambling or irrelevant conversation, unclear or illogical flow of ideas, or unpredictable switching from subject to subject? |
| 5. Altered level of consciousness | Would you rate the patient’s level of consciousness as (any of the following): – Vigilant (hyperalert) – Lethargic (drowsy, easily aroused) – Stupor (difficult to arouse) – Coma (unarousable) |
| *CAM shortened version worksheet. Adapted from: Inouye SK et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948; Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. Copyright 2003, Hospital Elder Life Program, LLC. | |
Arriving at a delirium diagnosis
The clinical presentation of delirium is characterized by acute—and reversible—impairment of cognition, attention, orientation, and memory, and disruption of the normal sleep/wake cycle. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria for a delirium diagnosis include all of the following:
- disturbance of consciousness, with a reduced ability to focus, sustain, or shift attention
- change in cognition, or a perceptual disturbance, that is not accounted for by a preexisting or developing dementia
- rapid onset of cognitive impairment, with fluctuation likely during the course of the day
- evidence from the history, physical exam, or laboratory findings that the disturbed consciousness is a direct physiological consequence of a general medical condition.17
There are 3 basic types of delirium, each associated with a different psychomotor disturbance.
- Hyperactive delirium—the least common—is characterized by restlessness and agitation, and is therefore the easiest to diagnose.
- Hypoactive delirium is characterized by psychomotor retardation and hypoalertness. It is often misdiagnosed as depression, and has the poorest prognosis.
- Mixed delirium—the most common—is characterized by symptoms that fluctuate between hyper- and hypoactivity.18
CASE By lunchtime, Mr. D had awakened; however, he needed help with his meal. After eating, he slept for the rest of the day. At night, a nurse paged the resident to report that the patient’s blood pressure was 82/60 mm Hg and his heart rate was 115. The physician ordered an intravenous fluid bolus, which corrected the patient’s hypotension, but only temporarily.
The fluctuating nature of delirium—most notably, in patients’ level of alertness—is helpful in establishing a diagnosis. The history and physical exam are the gold standard tools, both for diagnosing delirium and identifying the underlying cause (TABLE 3).19,20 A review of the patient’s medications should be a key component of the medical history, as drugs—particularly those with anticholinergic properties—are often associated with delirium. Environmental shifts, including hospitalization and a disruption of the normal sleep/wake cycle, endocrine disorders, infection, and nutritional deficiencies are also potential causes of delirium, among others.
If history and physical exam fail to identify the underlying cause, laboratory testing, including complete blood count, complete metabolic profile, and urinalysis, should be done. Brain imaging is usually not needed for individuals with symptoms of delirium, but computed tomography (CT) may be indicated if a patient’s condition continues to deteriorate while the underlying cause remains unidentified.21 Electroencephalography (EEG) may be used to confirm a delirium diagnosis that’s uncertain, in a patient with underlying dementia, for instance. (In more than 16% of cases of delirium, the cause is unknown.22)
The most common structural abnormalities found in patients with delirium are brain atrophy and increased white matter lesions, as well as basal ganglia lesions.23 Single-photon emission CT (SPECT) shows a reduction of regional cerebral perfusion by 50%,24 while EEG shows slowing of the posterior dominant rhythm and increased generalized slow-wave activity.25
TABLE 3
A DELIRIUM mnemonic to get to the heart of the problem19,20
| Cause | Comment |
|---|---|
| Drugs | Drug classes: Anesthesia, anticholinergics, anticonvulsants, antiemetics, antihistamines, antihypertensives, antimicrobials, antipsychotics, benzodiazepines, corticosteroids, hypnotics, H2 blockers, muscle relaxants, NSAIDs, opioids, SSRIs, tricyclic antidepressants Drugs: digoxin, levodopa, lithium, theophylline OTCs: henbane, Jimson weed, mandrake, Atropa belladonna extract |
| Environmental | Change of environment, sensory deprivation, sleep deprivation |
| Endocrine | Hyperparathyroidism, hyper-/hypothyroidism |
| Low perfusion | MI, pulmonary embolism, CVA |
| Infection | Pneumonia, sepsis, systemic infection, UTI |
| Retention | Fecal impaction, urinary retention |
| Intoxication | Alcohol, illegal drugs/drug overdose |
| Undernutrition | Malnutrition, thiamin deficiency, vitamin B12 deficiency |
| Metabolic | Acid-base disturbances, fluid and electrolyte abnormalities, hepatic or uremic encephalopathy, hypercarbia, hyper-/hypoglycemia, hyperosmolality, hypoxia |
| Subdural | History of falls |
| CVA, cerebrovascular accident; MI, myocardial infarction; NSAIDs, nonsteroidal anti-inflammatory drugs; OTCs, over-the-counter agents; SSRIs, selective serotonin reuptake inhibitors; UTI, urinary tract infection. | |
Treating (or preventing) delirium: Start with these steps
Nonpharmacologic interventions are the mainstay of treatment for patients with delirium, and may also help to prevent the development of delirium in patients at risk. One key measure is to correct, or avoid, disruptions in the patient’s normal sleep/wake cycle—eg, restoring circadian rhythm by avoiding,
to the extent possible, awakening the patient at night for medication or vital signs. Preventing sensory deprivation, by ensuring that the patient’s eyeglasses and hearing aid are nearby and that there is a clock and calendar nearby and adequate light, is also helpful. Other key interventions (TABLE 4)26-28 include:
- limiting medications associated with delirium (and eliminating any nonessential medication)
- improving nutrition and ambulation
- correcting electrolyte and fluid disturbances
- treating infection
- involving family members in patient care
- ensuring that patients receive adequate pain management
- avoiding transfers (if the patient is hospitalized) and trying to secure a single room.
Several studies have evaluated the effectiveness of nonpharmacologic interventions in preventing or lowering the incidence of delirium. A large multicomponent delirium prevention study of patients >70 years on general medical units focused on managing risk factors. The interventions studied included (1) avoidance of sensory deprivation, (2) early mobilization, (3) treating dehydration, (4) implementing noise reduction strategies and sleep enhancement programs, and (5) avoiding the use of sleep medications. These interventions proved to be effective not only in lowering the incidence of delirium, but in shortening the duration of delirium in affected patients (NNT=20).27
One study found that proactively using a geriatric consultation model (ie, implementing standardized protocols for the management of 6 risk factors) for elderly hospitalized patients led to a reduction in the incidence of delirium by more than a third.26 Admission to a specialized geriatric unit is associated with a lower incidence of delirium compared with being hospitalized on a general medical unit.29
Reducing the incidence of postoperative delirium. Bright light therapy (a light intensity of 5000 lux with a distance from the light source of 100 cm), implemented postoperatively, may play a role in reducing the incidence of delirium, research suggests.30 Music may be helpful, as well. An RCT involving patients (>65 years) undergoing elective knee or hip surgery found that those who listened to classical music postoperatively had a lower incidence of delirium.31 Similarly, playing music in nursing homes has been shown to decrease aggressive behavior and agitation.32
TABLE 4
Helpful interventions in the hospital or at home26-28
|
When medication is needed, proceed with caution
None of the medications currently used to treat delirium are approved by the US Food and Drug Administration for this indication, and many of them have substantial side effects. Nonetheless, palliative or symptomatic treatment requires some form of sedation for agitated patients with delirium. Thus, it is necessary to strike a balance in order to manage the symptoms of delirium and avoid potential side effects (primarily, sedation). Overly sedating patients can confuse the clinical picture of delirium and make it difficult to differentiate between ongoing delirium and medication side effects. Medication should be started at a low, but frequent, dose to achieve an effective therapeutic level, after which a lower maintenance dose can be used until the cause of delirium is resolved.
Antipsychotics are the cornerstone of drug treatment
Haloperidol has traditionally been used to treat delirium33 and has proven effectiveness. However, it is associated with increased risk of extrapyramidal manifestations compared with atypical antipsychotics.
Atypical antipsychotics (olanzapine, risperidone, quetiapine) are increasingly being used to treat delirium because they have fewer extrapyramidal side effects.34 With the exception of olanzapine (available in intramuscular and oral disintegrating form), atypical antipsychotics are available only in oral form, which may limit their usefulness as a treatment for agitated, delirious patients.
Risperidone (at a dose ranging from 0.25 to 1 mg/d) and olanzapine (1.25 to 2.5 mg/d) have shown similar efficacy to haloperidol (0.75 to 1.5 mg/d) in both the prevention and treatment of delirium, but with fewer extrapyramidal side effects.35-39 Quetiapine, a second-generation antipsychotic, is widely used to treat inpatient delirium, although there are no large RCTs comparing it with placebo. One pilot study and another open-label trial found the drug to be beneficial for patients with delirium, with fewer extrapyramidal side effects than haloperidol.40,41
Do a risk-benefit analysis. The use of antipsychotics in elderly patients with delirium has been associated with increased morbidity and mortality. The incidence of stroke and death were higher for community-dwelling patients (NNH=100) and patients in long-term care (N=67) who received typical or atypical antipsychotics for 6 months compared with that of patients who did not receive any antipsychotics.42,43 Thus, a risk-benefit analysis should be done before prescribing antipsychotics for elderly patients. Both typical and atypical antipsychotics carry black box warnings of increased mortality rates in the elderly.
Other drugs for delirium? More research is needed
Cholinesterase inhibitors. Procholinergic agents would be expected to be helpful in treating delirium, as cholinergic deficiency has been implicated as a predisposing factor for delirium and medications with anticholinergic effects have been shown to induce delirium. However, several studies of cholinesterase inhibitors have not found this to be the case.44-47
Benzodiazepines. There is no evidence to support the use of benzodiazepines in the treatment of delirium, except when the delirium is related to alcohol withdrawal.48 When indicated, the use of a short-acting benzodiazepine such as lorazepam is preferred for elderly patients (vs long-acting agents like diazepam) because of its shorter half-life and better side effect profile.2 Drowsiness, ataxia, and disinhibition are common side effects of benzodiazepines.
Gabapentin. A pilot study conducted to assess the efficacy of gabapentin (900 mg/d) for the prevention of postoperative delirium found a significantly lower incidence of delirium among patients who received gabapentin compared with placebo. This may be associated with gabapentin’s opioid-sparing effect.49 Larger studies are needed to recommend for or against the use of gabapentin in patients receiving opiates.
Further study of the pathophysiology of delirium is needed, as well, to increase our ability to prevent and treat it.
CASE After receiving the IV fluid bolus, Mr. D became increasingly short of breath and required more oxygen to keep his oxygen saturation in the 90s. Labs were ordered during morning rounds, and the patient was found to have urosepsis. He was admitted to the ICU in septic shock, and was intubated and died several days later.
In retrospect, it was determined that Mr. D had developed hypoactive delirium brought on by the infection—and that his somnolence on the second postoperative day was not a sign of overmedication. Had this been recognized early on through the use of an appropriate screening tool, the outcome would likely have been more favorable.
CORRESPONDENCE Abdulraouf Ghandour, MD, Green Meadows Clinic University Physicians, 3217 Providence Road, Columbia, MO 65203; Ghandour78@gmail.com
• Nonpharmacologic interventions are the mainstay of treatment for delirium. B
• When medication is needed, atypical antipsychotics are as effective as typical antipsychotics for treating delirium in elderly patients, and have fewer side effects. B
• Benzodiazepines should be avoided in elderly patients with delirium that is not associated with alcohol withdrawal. A
Strength of recommendation (SOR)
A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series
CASE Mr. D, a 75-year-old patient with a history of hypertension and congestive heart failure, sustained a femoral neck fracture and was admitted to the hospital for surgery. He underwent open reduction and internal fixation and was doing well postoperatively, until Day 2—when his primary care physician made morning rounds and noted that Mr. D was somnolent. The nurse on duty assured the physician that Mr. D was fine and “was awake and alert earlier,” and attributed his somnolence to the oxycodone (10 mg) the patient was taking for pain. The physician ordered a reduction in dosage.
If Mr. D had been your patient, would you have considered other possible causes of his somnolence? Or do you think the physician’s action was sufficient?
Derived from Latin, the word delirium literally means “off the [ploughed] track.”1 Dozens of terms have been used to describe delirium, with acute confusion state, organic brain syndrome, acute brain syndrome, and toxic psychosis among them.
Delirium has been reported to occur in 15% to 30% of patients on general medical units,2 about 40% of postoperative patients, and up to 70% of terminally ill patients.3 The true prevalence is hard to determine, as up to 66% of cases may be missed.4
Delirium is being diagnosed more frequently, however—a likely result of a growing geriatric population, increased longevity, and greater awareness of the condition. Each year, an estimated 2.3 million US residents are affected, leading to prolonged hospitalization; poor functional outcomes; the development or worsening of dementia; increased nursing home placement; and a significant burden for families and the US health care system.5
Delirium is also associated with an increase in mortality.6,7 The mortality rate among hospitalized patients who develop delirium is reported to be 18%, rising to an estimated 47% within the first 3 months after discharge.6 Greater awareness of risk factors, rapid recognition of signs and symptoms of delirium, and early intervention—detailed in the text and tables that follow—will lead to better outcomes.
Assessing risk, evaluating mental status
In addition to advanced age, risk factors for delirium (TABLE 1)8-14 include alcohol use, brain dysfunction, comorbidities, hypertension, malignancy, anticholinergic medications, anemia, metabolic abnormalities, and male sex. In patients who, like Mr. D, have numerous risk factors, early—and frequent—evaluation of mental status is needed. One way to do this is to treat mental status as a vital sign, to be included in the assessment of every elderly patient.15
The Confusion Assessment Method, a quick and easy-to-use delirium screening tool (TABLE 2), has a sensitivity of 94% to 100% and a specificity of 90% to 95%.16,17 There are a number of other screening tools, including the widely used Mini-Mental State Exam (MMSE), as well as the Delirium Rating Scale, Delirium Symptom Interview, and Delirium Severity Scale.
TABLE 1
Risk factors for delirium8-14
Advanced age Alcohol use Brain dysfunction (dementia, epilepsy) Hypertension Male sex Malignancy Medications (mainly anticholinergic) Metabolic abnormalities:
Old age Preoperative anemia Preoperative metabolic abnormalities |
| BUN, blood urea nitrogen; Cr, creatinine; Na, sodium. |
TABLE 2
Screening for delirium: The Confusion Assessment Method*16,17
| Criteria | Evidence Yes to questions 1, 2, and 3 plus 4 or 5 (or both) suggests a delirium diagnosis |
|---|---|
| 1. Acute onset | Is there evidence of an acute change in mental status from the patient’s baseline? |
| 2. Fluctuating course | Did the abnormal behavior fluctuate during the day—ie, tend to come and go or increase and decrease in severity? |
| 3. Inattention | Did the patient have difficulty focusing attention, eg, being easily distractible or having difficulty keeping track of what was being said? |
| PLUS | |
| 4. Disorganized thinking | Was the patient’s thinking disorganized or incoherent, such as rambling or irrelevant conversation, unclear or illogical flow of ideas, or unpredictable switching from subject to subject? |
| 5. Altered level of consciousness | Would you rate the patient’s level of consciousness as (any of the following): – Vigilant (hyperalert) – Lethargic (drowsy, easily aroused) – Stupor (difficult to arouse) – Coma (unarousable) |
| *CAM shortened version worksheet. Adapted from: Inouye SK et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948; Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. Copyright 2003, Hospital Elder Life Program, LLC. | |
Arriving at a delirium diagnosis
The clinical presentation of delirium is characterized by acute—and reversible—impairment of cognition, attention, orientation, and memory, and disruption of the normal sleep/wake cycle. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria for a delirium diagnosis include all of the following:
- disturbance of consciousness, with a reduced ability to focus, sustain, or shift attention
- change in cognition, or a perceptual disturbance, that is not accounted for by a preexisting or developing dementia
- rapid onset of cognitive impairment, with fluctuation likely during the course of the day
- evidence from the history, physical exam, or laboratory findings that the disturbed consciousness is a direct physiological consequence of a general medical condition.17
There are 3 basic types of delirium, each associated with a different psychomotor disturbance.
- Hyperactive delirium—the least common—is characterized by restlessness and agitation, and is therefore the easiest to diagnose.
- Hypoactive delirium is characterized by psychomotor retardation and hypoalertness. It is often misdiagnosed as depression, and has the poorest prognosis.
- Mixed delirium—the most common—is characterized by symptoms that fluctuate between hyper- and hypoactivity.18
CASE By lunchtime, Mr. D had awakened; however, he needed help with his meal. After eating, he slept for the rest of the day. At night, a nurse paged the resident to report that the patient’s blood pressure was 82/60 mm Hg and his heart rate was 115. The physician ordered an intravenous fluid bolus, which corrected the patient’s hypotension, but only temporarily.
The fluctuating nature of delirium—most notably, in patients’ level of alertness—is helpful in establishing a diagnosis. The history and physical exam are the gold standard tools, both for diagnosing delirium and identifying the underlying cause (TABLE 3).19,20 A review of the patient’s medications should be a key component of the medical history, as drugs—particularly those with anticholinergic properties—are often associated with delirium. Environmental shifts, including hospitalization and a disruption of the normal sleep/wake cycle, endocrine disorders, infection, and nutritional deficiencies are also potential causes of delirium, among others.
If history and physical exam fail to identify the underlying cause, laboratory testing, including complete blood count, complete metabolic profile, and urinalysis, should be done. Brain imaging is usually not needed for individuals with symptoms of delirium, but computed tomography (CT) may be indicated if a patient’s condition continues to deteriorate while the underlying cause remains unidentified.21 Electroencephalography (EEG) may be used to confirm a delirium diagnosis that’s uncertain, in a patient with underlying dementia, for instance. (In more than 16% of cases of delirium, the cause is unknown.22)
The most common structural abnormalities found in patients with delirium are brain atrophy and increased white matter lesions, as well as basal ganglia lesions.23 Single-photon emission CT (SPECT) shows a reduction of regional cerebral perfusion by 50%,24 while EEG shows slowing of the posterior dominant rhythm and increased generalized slow-wave activity.25
TABLE 3
A DELIRIUM mnemonic to get to the heart of the problem19,20
| Cause | Comment |
|---|---|
| Drugs | Drug classes: Anesthesia, anticholinergics, anticonvulsants, antiemetics, antihistamines, antihypertensives, antimicrobials, antipsychotics, benzodiazepines, corticosteroids, hypnotics, H2 blockers, muscle relaxants, NSAIDs, opioids, SSRIs, tricyclic antidepressants Drugs: digoxin, levodopa, lithium, theophylline OTCs: henbane, Jimson weed, mandrake, Atropa belladonna extract |
| Environmental | Change of environment, sensory deprivation, sleep deprivation |
| Endocrine | Hyperparathyroidism, hyper-/hypothyroidism |
| Low perfusion | MI, pulmonary embolism, CVA |
| Infection | Pneumonia, sepsis, systemic infection, UTI |
| Retention | Fecal impaction, urinary retention |
| Intoxication | Alcohol, illegal drugs/drug overdose |
| Undernutrition | Malnutrition, thiamin deficiency, vitamin B12 deficiency |
| Metabolic | Acid-base disturbances, fluid and electrolyte abnormalities, hepatic or uremic encephalopathy, hypercarbia, hyper-/hypoglycemia, hyperosmolality, hypoxia |
| Subdural | History of falls |
| CVA, cerebrovascular accident; MI, myocardial infarction; NSAIDs, nonsteroidal anti-inflammatory drugs; OTCs, over-the-counter agents; SSRIs, selective serotonin reuptake inhibitors; UTI, urinary tract infection. | |
Treating (or preventing) delirium: Start with these steps
Nonpharmacologic interventions are the mainstay of treatment for patients with delirium, and may also help to prevent the development of delirium in patients at risk. One key measure is to correct, or avoid, disruptions in the patient’s normal sleep/wake cycle—eg, restoring circadian rhythm by avoiding,
to the extent possible, awakening the patient at night for medication or vital signs. Preventing sensory deprivation, by ensuring that the patient’s eyeglasses and hearing aid are nearby and that there is a clock and calendar nearby and adequate light, is also helpful. Other key interventions (TABLE 4)26-28 include:
- limiting medications associated with delirium (and eliminating any nonessential medication)
- improving nutrition and ambulation
- correcting electrolyte and fluid disturbances
- treating infection
- involving family members in patient care
- ensuring that patients receive adequate pain management
- avoiding transfers (if the patient is hospitalized) and trying to secure a single room.
Several studies have evaluated the effectiveness of nonpharmacologic interventions in preventing or lowering the incidence of delirium. A large multicomponent delirium prevention study of patients >70 years on general medical units focused on managing risk factors. The interventions studied included (1) avoidance of sensory deprivation, (2) early mobilization, (3) treating dehydration, (4) implementing noise reduction strategies and sleep enhancement programs, and (5) avoiding the use of sleep medications. These interventions proved to be effective not only in lowering the incidence of delirium, but in shortening the duration of delirium in affected patients (NNT=20).27
One study found that proactively using a geriatric consultation model (ie, implementing standardized protocols for the management of 6 risk factors) for elderly hospitalized patients led to a reduction in the incidence of delirium by more than a third.26 Admission to a specialized geriatric unit is associated with a lower incidence of delirium compared with being hospitalized on a general medical unit.29
Reducing the incidence of postoperative delirium. Bright light therapy (a light intensity of 5000 lux with a distance from the light source of 100 cm), implemented postoperatively, may play a role in reducing the incidence of delirium, research suggests.30 Music may be helpful, as well. An RCT involving patients (>65 years) undergoing elective knee or hip surgery found that those who listened to classical music postoperatively had a lower incidence of delirium.31 Similarly, playing music in nursing homes has been shown to decrease aggressive behavior and agitation.32
TABLE 4
Helpful interventions in the hospital or at home26-28
|
When medication is needed, proceed with caution
None of the medications currently used to treat delirium are approved by the US Food and Drug Administration for this indication, and many of them have substantial side effects. Nonetheless, palliative or symptomatic treatment requires some form of sedation for agitated patients with delirium. Thus, it is necessary to strike a balance in order to manage the symptoms of delirium and avoid potential side effects (primarily, sedation). Overly sedating patients can confuse the clinical picture of delirium and make it difficult to differentiate between ongoing delirium and medication side effects. Medication should be started at a low, but frequent, dose to achieve an effective therapeutic level, after which a lower maintenance dose can be used until the cause of delirium is resolved.
Antipsychotics are the cornerstone of drug treatment
Haloperidol has traditionally been used to treat delirium33 and has proven effectiveness. However, it is associated with increased risk of extrapyramidal manifestations compared with atypical antipsychotics.
Atypical antipsychotics (olanzapine, risperidone, quetiapine) are increasingly being used to treat delirium because they have fewer extrapyramidal side effects.34 With the exception of olanzapine (available in intramuscular and oral disintegrating form), atypical antipsychotics are available only in oral form, which may limit their usefulness as a treatment for agitated, delirious patients.
Risperidone (at a dose ranging from 0.25 to 1 mg/d) and olanzapine (1.25 to 2.5 mg/d) have shown similar efficacy to haloperidol (0.75 to 1.5 mg/d) in both the prevention and treatment of delirium, but with fewer extrapyramidal side effects.35-39 Quetiapine, a second-generation antipsychotic, is widely used to treat inpatient delirium, although there are no large RCTs comparing it with placebo. One pilot study and another open-label trial found the drug to be beneficial for patients with delirium, with fewer extrapyramidal side effects than haloperidol.40,41
Do a risk-benefit analysis. The use of antipsychotics in elderly patients with delirium has been associated with increased morbidity and mortality. The incidence of stroke and death were higher for community-dwelling patients (NNH=100) and patients in long-term care (N=67) who received typical or atypical antipsychotics for 6 months compared with that of patients who did not receive any antipsychotics.42,43 Thus, a risk-benefit analysis should be done before prescribing antipsychotics for elderly patients. Both typical and atypical antipsychotics carry black box warnings of increased mortality rates in the elderly.
Other drugs for delirium? More research is needed
Cholinesterase inhibitors. Procholinergic agents would be expected to be helpful in treating delirium, as cholinergic deficiency has been implicated as a predisposing factor for delirium and medications with anticholinergic effects have been shown to induce delirium. However, several studies of cholinesterase inhibitors have not found this to be the case.44-47
Benzodiazepines. There is no evidence to support the use of benzodiazepines in the treatment of delirium, except when the delirium is related to alcohol withdrawal.48 When indicated, the use of a short-acting benzodiazepine such as lorazepam is preferred for elderly patients (vs long-acting agents like diazepam) because of its shorter half-life and better side effect profile.2 Drowsiness, ataxia, and disinhibition are common side effects of benzodiazepines.
Gabapentin. A pilot study conducted to assess the efficacy of gabapentin (900 mg/d) for the prevention of postoperative delirium found a significantly lower incidence of delirium among patients who received gabapentin compared with placebo. This may be associated with gabapentin’s opioid-sparing effect.49 Larger studies are needed to recommend for or against the use of gabapentin in patients receiving opiates.
Further study of the pathophysiology of delirium is needed, as well, to increase our ability to prevent and treat it.
CASE After receiving the IV fluid bolus, Mr. D became increasingly short of breath and required more oxygen to keep his oxygen saturation in the 90s. Labs were ordered during morning rounds, and the patient was found to have urosepsis. He was admitted to the ICU in septic shock, and was intubated and died several days later.
In retrospect, it was determined that Mr. D had developed hypoactive delirium brought on by the infection—and that his somnolence on the second postoperative day was not a sign of overmedication. Had this been recognized early on through the use of an appropriate screening tool, the outcome would likely have been more favorable.
CORRESPONDENCE Abdulraouf Ghandour, MD, Green Meadows Clinic University Physicians, 3217 Providence Road, Columbia, MO 65203; Ghandour78@gmail.com
1. Casselman WG. Dictionary of Medical Derivations. The Real Meaning of Medical Terms. New York, NY: Informa Healthcare; 1998.
2. Kiely DK, Bergmann MA, Murphy KM, et al. Delirium among newly admitted postacute facility patients, prevalence, symptoms, and severity. J Gerontol Biol Sci Med Sci. 2003;58:M441-M445.
3. Inouye SK, Charpentier PA. Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability. JAMA. 1996;275:852-857.
4. Inouye SK. The dilemma of delirium: clinical and research controversies regarding diagnosis and evaluation of delirium in hospitalized elderly medical patients. Am J Med. 1994;97:278-288.
5. Pompei P, Foreman M, Rudberg M, et al. Delirium in hospitalized older persons: outcomes and predictors. J Am Geriatr Soc. 1994;42:809-815.
6. Kolbeinsson H, Jonsson A. Delirium and dementia in acute medical admissions of elderly patients in Iceland. Acta Psychiatr Scand. 1993;87:123-127.
7. Cole MG, Primeau FJ. Prognosis of delirium in elderly hospital patients. CMAJ. 1993;149:41-46.
8. Rahkonen T, Eloniemi-Sulkava U, Halonen P, et al. Delirium in the non-demented oldest old in the general population: risk factors and prognosis. Int J Geriatr Psychiatry. 2001;16:415-421.
9. Edlund A, Lundstrom M, Brannstrom B, et al. Delirium before and after operation for femoral neck fracture. J Am Geriatr Soc. 2001;49:1335-1340.
10. Andersson EM, Gustafson L, Hallberg IR. Acute confusional state in elderly orthopaedic patients: factors of importance for detection in nursing care. Int J Geriatr Psychiatry. 2001;16:7-17.
11. Inouye SK, Viscoli CM, Horwitz RI, et al. A predictive model for delirium in hospitalized elderly medical patients based on admission characteristics. Ann Intern Med. 1993;119:474-481.
12. Marcantonio ER, Juarez G, Goldman L, et al. The relationship of postoperative delirium with psychoactive medications. JAMA. 1994;272:1518-1522.
13. Marcantonio ER, Goldman L, Orav EJ, et al. The association of intraoperative factors with the development of postoperative delirium. Am J Med. 1998;105:380-384.
14. Tune L, Carr S, Hoag E, et al. Anticholinergic effects of drugs commonly prescribed for the elderly: potential means for assessing risk of delirium. Am J Psychiatry. 1992;149:1393-1394.
15. Flaherty JH, Shay K, Weir C, et al. The development of a mental status vital sign for use across the spectrum of care . J Am Med Dir Assoc. 2009;10:379-380.
16. Inouye SK, Van Dyck CH, Alessi CA, et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948.
17. Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. New Haven, Conn: Yale University School of Medicine; 2003.
18. Halter J, Ouslander J, Tinetti M, et al. Hazzard’s Geriatric Medicine and Gerontology. 6th ed. New York, NY: McGraw-Hill; 2009;648-658.
19. Eriksson S. Social and environmental contributants to delirium in the elderly. Dement Geriatr Cogn Disord. 1999;10:350-352.
20. Francis J, Martin D, Kapoor WN. A prospective study of delirium in hospitalized elderly. JAMA. 1990;263:1097-1101.
21. Francis J, Hilko EM, Kapoor WN. Acute mental change: when are head scans needed? Clin Res. 1991;39:103.-
22. Rudberg MA, Pompei P, Foreman MD, et al. The natural history of delirium in older hospitalized patients: a syndrome of heterogeneity. Age Ageing. 1997;26:169-174.
23. Soiza RL, Sharma V, Ferguson K, et al. Neuroimaging studies of delirium: a systematic review. J Psychosom Res. 2008;65:239-248.
24. Fong TG, Bogardus ST Jr, Daftary A, et al. Cerebral perfusion changes in older delirious patients using 99mTc HMPAO SPECT. J Gerontol A Biol Sci Med Sci. 2006;61:1294-1299.
25. Jacobson SA, Leuchter AF, Walter DO. Conventional and quantitative EEG in the diagnosis of delirium among the elderly. J Neurol Neurosurg Psychiatry. 1993;56:153-158.
26. Marcantonio ER, Flacker JM, Wright RJ, et al. Reducing delirium after hip fracture: a randomized trial. J Am Geriatr Soc. 2001;49:516-522.
27. Inouye SK, Bogardus ST Jr, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340:669-676.
28. Weber JB, Coverdale JH, Kunik ME. Delirium: current trends in prevention and treatment. Intern Med J. 2004;34:115-121.
29. Bo M, Martini B, Ruatta C, et al. Geriatric ward hospitalization reduced incidence delirium among older medical inpatients. Am J Geriatr Psychiatry. 2009;17:760-768.
30. Taguchi T, Yano M, Kido Y. Influence of bright light therapy on postoperative patients: a pilot study. Intensive Crit Care Nurs. 2007;23:289-297.
31. McCaffrey R, Locsin R. The effect of music listening on acute confusion and delirium in elders undergoing elective hip and knee surgery. J Clin Nurs. 2004;13:91-96.
32. Remington R. Calming music and hand massage with agitated elderly. Nurs Res. 2004;51:317-323.
33. Seitz DP, Gill SS, van Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:11-21.
34. Schwartz T, Masand PS. The role of atypical antipsychotics in the treatment of delirium. Psychosomatics. 2002;43:171-174.
35. Lonergan E, Britton AM, Luxenberg J, et al. Antipsychotics for delirium. Cochrane Database Syst Rev. 2007;(2):CD005594.-
36. Hu H, Deng W, Yang H. A prospective random control study comparison of olanzapine and haloperidol in senile delirium. Chongqing Med J. 2004;8:1234-1237.
37. Han CS, Kim YK. A double-blind trial of risperidone and haloperidol for the treatment of delirium. Psychosomatics. 2004;45:297-301.
38. Kim SW, Yoo JA, Lee SY, et al. Risperidone versus olanzapine for the treatment of delirium. Hum Psychopharmacol. 2010;25:298-302.
39. Prakanrattana U, Prapaitrakool S. Efficacy of risperidone for prevention of postoperative delirium in cardiac surgery. Anaesth Intensive Care. 2007;35:714-719.
40. Maneeton B, Maneeton N, Srisurapanont M. An open-label study of quetiapine for delirium. J Med Assoc Thai. 2007;90:2158-2163.
41. Devlin JW, Roberts RJ, Fong JJ, et al. Efficacy and safety of quetiapine in critically ill patients with delirium: a prospective, multicenter, randomized, double-blind, placebo-controlled pilot study. Crit Care Med. 2010;38:419-427.
42. Gill SS, Bronskill SE, Normand SL, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775-786.
43. Wang PS, Schneeweiss S, Avorn J, et al. Death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:2335-2341.
44. Liptzin B, Laki A, Garb JL, et al. Donepezil in the prevention and treatment of post-surgical delirium. Am J Geriatr Psychiatry. 2005;13:1100-1106.
45. Sampson EL, Raven PR, Ndhlovu PN, et al. A randomized, double-blind, placebo-controlled trial of donepezil hydrochloride (Aricept) for reducing the incidence of postoperative delirium after elective total hip replacement. Int J Geriatr Psychiatry. 2007;22:343-349.
46. Gamberini M, Bolliger D, Lurati Buse GA, et al. Rivastigmine for the prevention of postoperative delirium in elderly patients undergoing elective cardiac surgery—a randomized controlled trial. Crit Care Med. 2009;37:1762-1768.
47. Overshott R, Vernon M, Morris J, et al. Rivastigmine in the treatment of delirium in older people: a pilot study. Int Psychogeriatr. 2010;22:812-818.
48. Lonergan E, Luxenberg J, Areosa Sastre A. Benzodiazepines for delirium. Cochrane Database Syst Rev. 2009;(4):CD006379.-
49. Leung JM, Sands LP, Rico M, et al. Pilot clinical trial of gabapentin to decrease postoperative delirium in older patients. Neurology. 2006;67:1251-1253.
1. Casselman WG. Dictionary of Medical Derivations. The Real Meaning of Medical Terms. New York, NY: Informa Healthcare; 1998.
2. Kiely DK, Bergmann MA, Murphy KM, et al. Delirium among newly admitted postacute facility patients, prevalence, symptoms, and severity. J Gerontol Biol Sci Med Sci. 2003;58:M441-M445.
3. Inouye SK, Charpentier PA. Precipitating factors for delirium in hospitalized elderly persons. Predictive model and interrelationship with baseline vulnerability. JAMA. 1996;275:852-857.
4. Inouye SK. The dilemma of delirium: clinical and research controversies regarding diagnosis and evaluation of delirium in hospitalized elderly medical patients. Am J Med. 1994;97:278-288.
5. Pompei P, Foreman M, Rudberg M, et al. Delirium in hospitalized older persons: outcomes and predictors. J Am Geriatr Soc. 1994;42:809-815.
6. Kolbeinsson H, Jonsson A. Delirium and dementia in acute medical admissions of elderly patients in Iceland. Acta Psychiatr Scand. 1993;87:123-127.
7. Cole MG, Primeau FJ. Prognosis of delirium in elderly hospital patients. CMAJ. 1993;149:41-46.
8. Rahkonen T, Eloniemi-Sulkava U, Halonen P, et al. Delirium in the non-demented oldest old in the general population: risk factors and prognosis. Int J Geriatr Psychiatry. 2001;16:415-421.
9. Edlund A, Lundstrom M, Brannstrom B, et al. Delirium before and after operation for femoral neck fracture. J Am Geriatr Soc. 2001;49:1335-1340.
10. Andersson EM, Gustafson L, Hallberg IR. Acute confusional state in elderly orthopaedic patients: factors of importance for detection in nursing care. Int J Geriatr Psychiatry. 2001;16:7-17.
11. Inouye SK, Viscoli CM, Horwitz RI, et al. A predictive model for delirium in hospitalized elderly medical patients based on admission characteristics. Ann Intern Med. 1993;119:474-481.
12. Marcantonio ER, Juarez G, Goldman L, et al. The relationship of postoperative delirium with psychoactive medications. JAMA. 1994;272:1518-1522.
13. Marcantonio ER, Goldman L, Orav EJ, et al. The association of intraoperative factors with the development of postoperative delirium. Am J Med. 1998;105:380-384.
14. Tune L, Carr S, Hoag E, et al. Anticholinergic effects of drugs commonly prescribed for the elderly: potential means for assessing risk of delirium. Am J Psychiatry. 1992;149:1393-1394.
15. Flaherty JH, Shay K, Weir C, et al. The development of a mental status vital sign for use across the spectrum of care . J Am Med Dir Assoc. 2009;10:379-380.
16. Inouye SK, Van Dyck CH, Alessi CA, et al. Clarifying confusion: the Confusion Assessment Method. A new method for detection of delirium. Ann Intern Med. 1990;113:941-948.
17. Inouye SK. Confusion Assessment Method (CAM): Training Manual and Coding Guide. New Haven, Conn: Yale University School of Medicine; 2003.
18. Halter J, Ouslander J, Tinetti M, et al. Hazzard’s Geriatric Medicine and Gerontology. 6th ed. New York, NY: McGraw-Hill; 2009;648-658.
19. Eriksson S. Social and environmental contributants to delirium in the elderly. Dement Geriatr Cogn Disord. 1999;10:350-352.
20. Francis J, Martin D, Kapoor WN. A prospective study of delirium in hospitalized elderly. JAMA. 1990;263:1097-1101.
21. Francis J, Hilko EM, Kapoor WN. Acute mental change: when are head scans needed? Clin Res. 1991;39:103.-
22. Rudberg MA, Pompei P, Foreman MD, et al. The natural history of delirium in older hospitalized patients: a syndrome of heterogeneity. Age Ageing. 1997;26:169-174.
23. Soiza RL, Sharma V, Ferguson K, et al. Neuroimaging studies of delirium: a systematic review. J Psychosom Res. 2008;65:239-248.
24. Fong TG, Bogardus ST Jr, Daftary A, et al. Cerebral perfusion changes in older delirious patients using 99mTc HMPAO SPECT. J Gerontol A Biol Sci Med Sci. 2006;61:1294-1299.
25. Jacobson SA, Leuchter AF, Walter DO. Conventional and quantitative EEG in the diagnosis of delirium among the elderly. J Neurol Neurosurg Psychiatry. 1993;56:153-158.
26. Marcantonio ER, Flacker JM, Wright RJ, et al. Reducing delirium after hip fracture: a randomized trial. J Am Geriatr Soc. 2001;49:516-522.
27. Inouye SK, Bogardus ST Jr, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340:669-676.
28. Weber JB, Coverdale JH, Kunik ME. Delirium: current trends in prevention and treatment. Intern Med J. 2004;34:115-121.
29. Bo M, Martini B, Ruatta C, et al. Geriatric ward hospitalization reduced incidence delirium among older medical inpatients. Am J Geriatr Psychiatry. 2009;17:760-768.
30. Taguchi T, Yano M, Kido Y. Influence of bright light therapy on postoperative patients: a pilot study. Intensive Crit Care Nurs. 2007;23:289-297.
31. McCaffrey R, Locsin R. The effect of music listening on acute confusion and delirium in elders undergoing elective hip and knee surgery. J Clin Nurs. 2004;13:91-96.
32. Remington R. Calming music and hand massage with agitated elderly. Nurs Res. 2004;51:317-323.
33. Seitz DP, Gill SS, van Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:11-21.
34. Schwartz T, Masand PS. The role of atypical antipsychotics in the treatment of delirium. Psychosomatics. 2002;43:171-174.
35. Lonergan E, Britton AM, Luxenberg J, et al. Antipsychotics for delirium. Cochrane Database Syst Rev. 2007;(2):CD005594.-
36. Hu H, Deng W, Yang H. A prospective random control study comparison of olanzapine and haloperidol in senile delirium. Chongqing Med J. 2004;8:1234-1237.
37. Han CS, Kim YK. A double-blind trial of risperidone and haloperidol for the treatment of delirium. Psychosomatics. 2004;45:297-301.
38. Kim SW, Yoo JA, Lee SY, et al. Risperidone versus olanzapine for the treatment of delirium. Hum Psychopharmacol. 2010;25:298-302.
39. Prakanrattana U, Prapaitrakool S. Efficacy of risperidone for prevention of postoperative delirium in cardiac surgery. Anaesth Intensive Care. 2007;35:714-719.
40. Maneeton B, Maneeton N, Srisurapanont M. An open-label study of quetiapine for delirium. J Med Assoc Thai. 2007;90:2158-2163.
41. Devlin JW, Roberts RJ, Fong JJ, et al. Efficacy and safety of quetiapine in critically ill patients with delirium: a prospective, multicenter, randomized, double-blind, placebo-controlled pilot study. Crit Care Med. 2010;38:419-427.
42. Gill SS, Bronskill SE, Normand SL, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775-786.
43. Wang PS, Schneeweiss S, Avorn J, et al. Death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:2335-2341.
44. Liptzin B, Laki A, Garb JL, et al. Donepezil in the prevention and treatment of post-surgical delirium. Am J Geriatr Psychiatry. 2005;13:1100-1106.
45. Sampson EL, Raven PR, Ndhlovu PN, et al. A randomized, double-blind, placebo-controlled trial of donepezil hydrochloride (Aricept) for reducing the incidence of postoperative delirium after elective total hip replacement. Int J Geriatr Psychiatry. 2007;22:343-349.
46. Gamberini M, Bolliger D, Lurati Buse GA, et al. Rivastigmine for the prevention of postoperative delirium in elderly patients undergoing elective cardiac surgery—a randomized controlled trial. Crit Care Med. 2009;37:1762-1768.
47. Overshott R, Vernon M, Morris J, et al. Rivastigmine in the treatment of delirium in older people: a pilot study. Int Psychogeriatr. 2010;22:812-818.
48. Lonergan E, Luxenberg J, Areosa Sastre A. Benzodiazepines for delirium. Cochrane Database Syst Rev. 2009;(4):CD006379.-
49. Leung JM, Sands LP, Rico M, et al. Pilot clinical trial of gabapentin to decrease postoperative delirium in older patients. Neurology. 2006;67:1251-1253.
Clinical Findings Associated with Radiographic Pneumonia in Nursing Home Residents
STUDY DESIGN: This was a prospective cohort study.
POPULATION: The residents of 36 nursing homes in central Missouri and the St. Louis area with signs or symptoms suggesting a lower respiratory infection were included.
OUTCOME MEASURED: We compared evaluation findings by project nurses with findings reported from chest radiographs.
RESULTS: Among 2334 episodes of illness in 1474 nursing home residents, 45% of the radiograph reports suggested pneumonia (possible=12%; probable or definite = 33%). In 80% of pneumonia episodes, subjects had 3 or fewer respiratory or general symptoms. Eight variables were significant independent predictors of pneumonia (increased pulse, respiratory rate Ž30, temperature Ž38°C, somnolence or decreased alertness, presence of acute confusion, lung crackles on auscultation, absence of wheezes, and increased white blood count). A simple score (range = -1 to 8) on the basis of these variables identified 33% of subjects (score Ž3) with more than 50% probability of pneumonia and an additional 24% (score of 2) with 44% probability of pneumonia.
CONCLUSIONS: Pneumonia in nursing home residents is usually associated with few symptoms. Nonetheless, a simple clinical prediction rule can identify residents at very high risk for pneumonia. If validated in other studies, physicians could consider treating such residents without obtaining a chest radiograph.
Pneumonia is a leading cause of morbidity, mortality, and hospitalization of nursing home residents.1-8 Atypical presentations and fewer presenting signs and symptoms in older patients complicate diagnosis.9,10 Also, clinician (physician, nurse practitioner, and physician assistant) visits to nursing homes are often sporadic, and radiology facilities are rarely on the premises. As a consequence, residents are commonly sent to emergency departments for evaluation,4,11,12 which undoubtedly contributes to a high hospitalization rate.
Clinicians who periodically see nursing home residents could benefit from a simple clinical tool to identify pneumonia. No large studies of community nursing home residents have systematically studied findings associated with pneumonia. As part of the Missouri LRI Project, we examined how well clinical findings predict radiographic pneumonia.
Methods
The Missouri LRI Project was a prospective observational study in 36 nursing homes in Central Missouri and St. Louis designed to investigate predictors of 2 outcomes of lower respiratory infections (LRIs): mortality and functional decline. Potential cases were identified from August 15, 1995, through September 29, 1998; however, all facilities were not involved until fall 1997. Study facilities were similar in size, ownership, and occupancy to national estimates from the 1995 National Nursing Home Survey (data available on request).13
We trained nursing home staff to report ill residents with any of 6 respiratory symptoms (eg, cough, dyspnea, sputum production) or 6 general symptoms (eg, fever, decline in mobility, mental status changes). Project nurses called and visited facilities frequently to reinforce reporting. Under a physician-authorized protocol, ill residents with a possible LRI received a standardized evaluation by a trained project nurse and usually a chest radiograph, complete blood count, and a chemistry panel. Complete criteria for triggering an evaluation are listed in Table 1. For this paper, we were concerned with the 90% of evaluated residents who received a chest radiograph. Criteria for excluding residents from evaluation are summarized in the Figure 1.
The nurse evaluation included an inventory of current symptoms, a review of important chronic conditions (eg, congestive heart failure), and a targeted physical examination. The examination included vital signs and the following body areas or systems: ears, nose, and throat; cardiac; abdominal; neurologic; extremities; skin; and a detailed lung examination. Most project nurses had advanced practice training; the remainder had extensive clinical experience and training in physical assessment. All received an individualized training session with a project geriatrician. Project nurses had substantially more experience than the nursing home staff, who usually report clinical findings to physicians.
Results of the evaluation were reported to the attending physician, who made all treatment decisions. Since the evaluations were clinically appropriate care authorized by individual attending physicians, the institutional review boards that reviewed the project allowed us to substantially simplify the consent process to a simple acceptance or refusal of the evaluation. In 9.2% of evaluations the resident was transferred to the hospital before project nurses could complete a physical assessment. In these instances, we obtained vital sign and clinical examination data from hospital records.
Radiographic Classification
Since all subjects had at least one illness symptom, for this analysis we classified the presence or absence of pneumonia on the basis of reported radiographic findings. Using defined criteria, 2 clinicians independently separated radiology reports into 3 categories: (a) negative, (b) possible, or (c) probable or definite for pneumonia (hereafter, probable pneumonia). For example, a report describing “new left lower lobe infiltrate suggestive of pneumonia” would have been rated as probable, while a report indicating “possible infiltrate” or “infiltrate suggestive of pneumonia or congestive heart failure” would have been rated as possible. As radiologists rarely provide completely unequivocal readings, we did not separate probable and definite pneumonia. In St. Louis 2 clinicians evaluated the reports, and in central Missouri 2 of 4 clinicians considered each report. Where there was disagreement, all 6 raters from the 2 sites independently reviewed the reports and then attempted to reach consensus. For 13% of radiographs, the project radiologist independently interpreted the actual films. This occurred when: (1) consensus could not be achieved; or (2) consensus was possible pneumonia, but probable pneumonia was needed to quality the episode as an LRI under the project definition.
Statistical Analyses
As residents could be included more than once, the unit of analysis throughout is episode of illness. In our major analysis, we developed a multivariable logistic model to estimate the probability of radiographic pneumonia (possible or probable). Before beginning modeling, we imputed mean values for missing continuous data and the largest category for missing dichotomous variables (the number of missing values is noted in Table 2). Data imputation is less biased than dropping cases in developing multivariable models.14
Illness episodes were then randomly assigned to a two thirds model-development and a one third model-validation sample. On the basis of the literature and clinical experience, we defined categories of variables that might relate to the presence or absence of pneumonia, such as lung findings (eg, crackles, wheezes), respiratory symptoms (eg, cough, sputum production), vital signs, findings of delirium (eg, acute confusion, decreased alertness), and laboratory findings. Restricting our focus to the development sample, we selected the best representatives of these groups on clinical and statistical grounds. For continuous variables, we considered the shape of the relationship to presence of pneumonia. For example, both very high and very low pulse rates predicted increased risk of pneumonia. In such cases, we considered several different ways to represent the variable in the model. We also limited the range of some variables to avoid undue influence of outliers (approximately the 1% most extreme values). For example, pulse rate above 140 was set equal to 140.
We then employed forward and backward stepwise logistic regression with possible or probable pneumonia (also referred to as positive x-ray results) as the dependent variable. For final model inclusion, we required variables to bear a plausible relationship to the diagnosis of pneumonia and meet a statistical significance criterion (a=.05).
To obtain final estimates of the relationship of each model variable to pneumonia probability, we considered adjustments for 2 kinds of correlation within our data: (1) individuals are nested within facilities, and (2) subjects could be represented by more than one episode.15 Using generalized estimating equations (GEE) in Proc Genmod in SAS software (SAS Institute, Cary, NC),16 we noted that the effect of facilities was minor, but the effect of repeat episodes by the same subject was more marked. Consequently, we used GEE to account for repeat episodes on subjects. To avoid unstable GEE estimates, we dropped 5 episodes in the development sample and 8 in the overall sample (episodes beyond the 5th and 6th per individual, respectively).
Using parameter estimates from the development sample, we tested the model’s discrimination and calibration in the validation sample.17 To assess discrimination, we used the c-statistic, which evaluates among all possible pairs of individuals whether those with higher predicted risk are more likely to die. The c-statistic is also equal to the area under the receiver operating characteristic curve. To assess calibration—agreement between observed and predicted mortality over the range of predicted risk—we used the Hosmer-Lemeshow goodness-of-fit statistic.18 We then used estimates fitted to the overall sample to develop a simple additive score to provide a clinically usable prediction rule. Statistical analyses were performed with SAS statistical software.16
Results
Project nurses performed 2592 evaluations. In 90% (2337), residents received chest x-rays either in the nursing home or on hospital transfer. In 3 additional cases crucial information was missing from nursing home records. This left for final analysis 2334 episodes in 1474 individuals Figure 1.
Fifty-five percent of radiographs were interpreted as negative, 12% showed possible pneumonia, and 33% showed probable pneumonia. Most nursing home residents with pneumonia had few presenting symptoms; 80% had 3 or fewer respiratory or general symptoms. However, only 7.5% of subjects evaluated had no respiratory symptoms. Table 2 shows the relationship of selected variables to radiographic findings of absent, possible, or probable pneumonia. Though a few signs and symptoms are more common in those with positive (possible or probable pneumonia) than negative chest x-ray results, most did not discriminate at all. Fever (temperature Ž38°C) was present in 44.4% of positives but only 28.5% of negatives (P=.001).
Multivariable Analysis and Prediction Score
Our GEE model to predict radiographic pneumonia includes 3 vital sign abnormalities (fever, rapid pulse, and rapid respiratory rate), 2 lung findings (presence of crackles and absence of wheezes), 2 potential indicators of delirium (somnolence or decreased alertness and acute confusion), and elevated white blood count. Table 3 reports GEE estimates for the entire sample. Though only exhibiting fair overall performance, the model did well at distinguishing subjects with a high probability of pneumonia. In the 20% of subjects with the highest predicted risks, more than two thirds had pneumonia.
For the full range of values, the model derived on the development sample showed a c-statistic of 0.672, which reduced to 0.632 in the validation sample. A value of 1.0 would indicate perfect discrimination between those who did and did not have radiographic pneumonia, while a value of 0.5 would indicate no better than chance discrimination. Model calibration was not acceptable in the validation sample (Hosmer-Lemeshow goodness-of-fit statistic, P=.008). Inspection suggested the disagreement between predicted and observed probability of pneumonia was primarily with lower-risk estimates.
Because the model performed relatively well at distinguishing subjects very likely to have pneumonia, we created a simple point system aimed at identifying such high-risk individuals. Table 4 shows the scoring system. For 33% of subjects (score Ž3), there was a 56% or higher probability of radiographic pneumonia. An additional 24% of subjects (score of 2) had 44% probability of radiographic pneumonia. However, even those with the lowest scores (-1 to 0, 15% of subjects) still had a 24% probability of pneumonia. The relationship between the score and the probability of radiographic evidence of pneumonia is shown in Figure W1.*
Discussion
In a large community-based sample, we considered presenting symptoms, signs, and laboratory findings associated with radiographic pneumonia. Individual findings discriminated poorly, and we could not separate out a very-low-risk group. However, our simple scoring system identified approximately one third to slightly more than one half with high probability of pneumonia—individuals who might be treated without a confirmatory chest x-ray. If our data are confirmed, they suggest a simple clinical strategy in patients with respiratory or general symptoms Table 1 that might suggest pneumonia: (1) if there are no respiratory symptoms, consider other conditions, such as a urinary tract infection, that might fully explain the symptoms; (2) obtain information to apply our symptom score Table 4; (3) for those with scores of 2 or higher (some might choose 3 instead), treat for pneumonia; (4) for those with scores of -1, 0, or 1, obtain a chest radiograph as a guide to treatment.
Considering individual findings, fever was significantly more common in pneumonia, but only 43% of those with possible or probable pneumonia had a temperature of at least 38°C. This reaffirms common wisdom and previous findings that fever is frequently absent in elderly people with pneumonia.9,19 We also confirmed that few signs or symptoms are the norm for nursing home-acquired pneumonia.
Chest examination findings also do not adequately distinguish patients with and without pneumonia Table 2. Also, even expert physicians frequently differ on lung examination findings.20 Nonetheless, presence of crackles and absence of wheezing contribute to our scoring system. Both findings are seen with multiple conditions, but in our data crackles are slightly more associated with pneumonia, while wheezing is more strongly associated with other diseases.
The other components of our scoring system are clinical factors commonly associated with pneumonia. Though none individually discriminates well between those with and without pneumonia Table 2, several combined serve to identify a high-risk group.
Four previous studies from emergency department or outpatient settings developed clinical prediction rules to identify pneumonia.21-24 Criteria for identifying subjects varied substantially, and each rule has limited accuracy in predicting radiographic pneumonia.20 We had adequate data to evaluate 3 of the rules.21-23 As is usually the case when transporting a prediction rule to a new sample, none performed any better than our rule (data not shown). Our sample created the very difficult challenge for any prediction rule of a very high overall prevalence of pneumonia (45%). That made it unlikely that we could identify a low-risk group in whom x-ray studies could be readily forgone, but we were able to identify a highrisk group.
Limitations
Our findings are subject to several limitations. All facilities in our study were located in central or eastern Missouri, and not all physicians or eligible residents in those facilities participated. Compared with national data, we studied an unusually representative sample of nursing home residents from 36 facilities, including rural and urban locations. Also, in episodes excluded because of physician nonparticipation, residents were very similar to included residents in age, vital signs, and presenting symptoms (data available on request). More important, we lack an independent validation sample from a different cohort. Clinical prediction rules usually do not perform as well in independent samples. This is exemplified by the poor performance of the 3 rules we considered from other settings. Overall, our logistic model was only modest in discriminating and was not well calibrated for low-risk episodes in our reserved validation sample. Although we have developed a promising scoring system to identify residents with high probability of radiographic pneumonia, it needs to be validated in other samples of nursing home residents to determine its ultimate usefulness. For all these reasons, our results may not generalize.
Also, although we identified residents prospectively, project nurses were unable to evaluate 9.2% of residents before transfer to a hospital. Clinical findings abstracted from medical records, such as lung findings, may not have been complete. It is also possible that project nurses could have missed some important findings. However, our staff provided a higher level of expertise than is typically available in nursing homes. In fact, this may limit application of our findings. Nursing home staff vary widely in their ability to accurately examine residents or even identify illness. In many instances, facility staff had not obtained vital signs at the point when we identified a resident as ill enough to qualify for an evaluation.25 Therefore, in many nursing homes, physicians may lack confidence to apply our rule without an evaluation by a physician, advanced practice nurse, or physician assistant.
Finally, determining whether subjects had pneumonia primarily depended on our classification of radiographic reports. Though radiographs generally included 2 views, many were portable films of variable quality, and frequently there was no previous radiograph for comparison. In some subjects with pneumonia, radiographic infiltrates might not yet have developed. Also, even under ideal conditions, radiologists commonly disagree on the presence of pneumonia.26 Some subjects may have been misclassified. However, unless radiographic technique or interpretation was specifically related to clinical predictors, misclassification would simply diminish the relationship of predictors to pneumonia rather than creating a bias. We reviewed reports rather than radiographs, because that is the information usually available to clinicians faced with diagnosis and treatment decisions. We also paid special attention to avoiding any bias in the interpretations. All data were recorded before interpreting radiology reports and the interpretations were performed independent of clinical data. We also made special efforts to assure consistency in labeling radiology reports as possible, probable, or negative for pneumonia. When lack of agreement persisted, the study radiologist reinterpreted the actual films.
Conclusions
Most nursing home residents with pneumonia have few symptoms. We created a simple scoring to identify nursing home residents who have a high probability of radiographic pneumonia. If our results are confirmed, physicians might consider initiating treatment without an x-ray in such residents. Low scores do not rule out pneumonia, and most physicians would want to press for further diagnosis or treatment in this group.
Acknowledgments
This study was supported by the Agency for Healthcare Research and Quality (grant HS08551) and Dr Mehr’s Robert Wood Johnson Foundation Generalist Physician Faculty Scholars award. Dr Kruse was partially supported by an Institutional National Research Service Award (PE10038) from the Health Resources and Services Administration. Our project would not have been possible without the support of the many attending physicians, administrators, and staff of the involved nursing homes. Dr Clive Levine re-read more than 200 radiographs; Karen Davenport provided crucial administrative support; and Karen Madrone, MPA, assisted with manuscript preparation. Many other unnamed project staff also contributed.
1. Irvine PW, Van Buren N, Crossley K. Causes for hospitalization of nursing home residents: the role of infection. J Am Geriatr Soc 1984;32:103-07.
2. Murtaugh CM, Freiman MP. Nursing home residents at risk of hospitalization and the characteristics of their hospital stays. Gerontologist 1995;35:35-43.
3. Jackson MM, Fierer J, Barrett-Connor E, et al. Intensive surveillance for infections in a three-year study of nursing home patients. Am J Epidemiol 1992;135:685-96.
4. Brooks S, Warshaw G, Hasse L, Kues JR. The physician decision-making process in transferring nursing home patients to the hospital. Arch Intern Med 1994;154:902-08.
5. Fried TR, Gillick MR, Lipsitz LA. Whether to transfer? Factors associated with hospitalization and outcome of elderly long-term care patients with pneumonia. J Gen Intern Med 1995;10:246-50.
6. Degelau J, Guay D, Straub K, Luxenberg MG. Effectiveness of oral antibiotic treatment in nursing home-acquired pneumonia. J Am Geriatr Soc 1995;43:245-51.
7. Muder RR, Brennen C, Swenson DL, Wagener M. Pneumonia in a long-term care facility: a prospective study of outcome. Arch Intern Med 1996;156:2365-70.
8. Medina-Walpole AM, Katz PR. Nursing home-acquired pneumonia. J Am Geriatr Soc 1999;47:1005-15.
9. Harper C, Newton P. Clinical aspects of pneumonia in the elderly veteran. J Am Geriatr Soc 1989;37:867-72.
10. Metlay JP, Schulz R, Li YH, Singer DE, Marrie TJ, Coley CM, et al. Influence of age on symptoms at presentation in patients with community-acquired pneumonia. Arch Intern Med 1997;157:1453-59.
11. Kayser-Jones JS, Wiener CL, Barbaccia JC. Factors contributing to the hospitalization of nursing home residents. Gerontologist 1989;29:502-10.
12. Scott HD, Logan M, Waters WJ, Jr, et al. Medical practice variation in the management of acute medical events in nursing homes: a pilot study. R I Med J 1988;71:69-74.
13. Gabrel CS, Jones A. The National Nursing Home Survey: 1997 summary. Vital Health Stat-series 13: data from the National Health Survey 2000;147:1-121.
14. Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-87.
15. Preisser JS, Koch GG. Categorical data analysis in public health. nn Rev Public Health 1997;18:51-82.
16. SAS Institute Inc The SAS System for Windows. Version 6.1. Cary, NC: SAS Institute, Inc; 1996.
17. D’Agostino RB, Sr, Griffith JL, Schmid CH, Terrin N. Measures for evaluating model performance. In: Proceedings of the biometrics section, 1997. Alexandria, Va: American Statistical Association. Biometrics section; 1998;253-58.
18. Hosmer DW Jr, Lemeshow S. Applied logistic regression. New York, NY: Wiley; 1989.
19. Marrie TJ, Haldane EV, Faulkner RS, Durant H, Kwan C. Community-acquired pneumonia requiring hospitalization: is it different in the elderly? J Am Geriatr Soc 1985;33:671-80.
20. Metlay JP, Kapoor WN, Fine MJ. Does this patient have community-acquired pneumonia? Diagnosing pneumonia by history and physical examination. JAMA 1997;278:1440-45.
21. Heckerling PS, Tape TG, Wigton RS, et al. Clinical prediction rule for pulmonary infiltrates. Ann Intern Med 1990;113:664-70.
22. Singal BM, Hedges JR, Radack KL. Decision rules and clinical prediction of pneumonia: evaluation of low-yield criteria. Ann Emerg Med 1989;18:13-20.
23. Gennis P, Gallagher J, Falvo C, Baker S, Than W. Clinical criteria for the detection of pneumonia in adults: guidelines for ordering chest roentgenograms in the emergency department. J Emerg Med 1989;7:263-68.
24. Diehr P, Wood RW, Bushyhead J, Krueger L, Wolcott B, Tompkins RK. Prediction of pneumonia in outpatients with acute cough—a statistical approach. J Chronic Dis 1984;37:215.-
25. Barry CR, Brown K, Esker D, Denning MD, Kruse RL, Binder EF. Nursing assessment of ill nursing home residents. In press.
26. Albaum MN, Hill LC, Murphy M, et al. Interobserver reliability of the chest radiograph in community-acquired pneumonia: PORT Investigators. Chest 1996;110:343-50.
STUDY DESIGN: This was a prospective cohort study.
POPULATION: The residents of 36 nursing homes in central Missouri and the St. Louis area with signs or symptoms suggesting a lower respiratory infection were included.
OUTCOME MEASURED: We compared evaluation findings by project nurses with findings reported from chest radiographs.
RESULTS: Among 2334 episodes of illness in 1474 nursing home residents, 45% of the radiograph reports suggested pneumonia (possible=12%; probable or definite = 33%). In 80% of pneumonia episodes, subjects had 3 or fewer respiratory or general symptoms. Eight variables were significant independent predictors of pneumonia (increased pulse, respiratory rate Ž30, temperature Ž38°C, somnolence or decreased alertness, presence of acute confusion, lung crackles on auscultation, absence of wheezes, and increased white blood count). A simple score (range = -1 to 8) on the basis of these variables identified 33% of subjects (score Ž3) with more than 50% probability of pneumonia and an additional 24% (score of 2) with 44% probability of pneumonia.
CONCLUSIONS: Pneumonia in nursing home residents is usually associated with few symptoms. Nonetheless, a simple clinical prediction rule can identify residents at very high risk for pneumonia. If validated in other studies, physicians could consider treating such residents without obtaining a chest radiograph.
Pneumonia is a leading cause of morbidity, mortality, and hospitalization of nursing home residents.1-8 Atypical presentations and fewer presenting signs and symptoms in older patients complicate diagnosis.9,10 Also, clinician (physician, nurse practitioner, and physician assistant) visits to nursing homes are often sporadic, and radiology facilities are rarely on the premises. As a consequence, residents are commonly sent to emergency departments for evaluation,4,11,12 which undoubtedly contributes to a high hospitalization rate.
Clinicians who periodically see nursing home residents could benefit from a simple clinical tool to identify pneumonia. No large studies of community nursing home residents have systematically studied findings associated with pneumonia. As part of the Missouri LRI Project, we examined how well clinical findings predict radiographic pneumonia.
Methods
The Missouri LRI Project was a prospective observational study in 36 nursing homes in Central Missouri and St. Louis designed to investigate predictors of 2 outcomes of lower respiratory infections (LRIs): mortality and functional decline. Potential cases were identified from August 15, 1995, through September 29, 1998; however, all facilities were not involved until fall 1997. Study facilities were similar in size, ownership, and occupancy to national estimates from the 1995 National Nursing Home Survey (data available on request).13
We trained nursing home staff to report ill residents with any of 6 respiratory symptoms (eg, cough, dyspnea, sputum production) or 6 general symptoms (eg, fever, decline in mobility, mental status changes). Project nurses called and visited facilities frequently to reinforce reporting. Under a physician-authorized protocol, ill residents with a possible LRI received a standardized evaluation by a trained project nurse and usually a chest radiograph, complete blood count, and a chemistry panel. Complete criteria for triggering an evaluation are listed in Table 1. For this paper, we were concerned with the 90% of evaluated residents who received a chest radiograph. Criteria for excluding residents from evaluation are summarized in the Figure 1.
The nurse evaluation included an inventory of current symptoms, a review of important chronic conditions (eg, congestive heart failure), and a targeted physical examination. The examination included vital signs and the following body areas or systems: ears, nose, and throat; cardiac; abdominal; neurologic; extremities; skin; and a detailed lung examination. Most project nurses had advanced practice training; the remainder had extensive clinical experience and training in physical assessment. All received an individualized training session with a project geriatrician. Project nurses had substantially more experience than the nursing home staff, who usually report clinical findings to physicians.
Results of the evaluation were reported to the attending physician, who made all treatment decisions. Since the evaluations were clinically appropriate care authorized by individual attending physicians, the institutional review boards that reviewed the project allowed us to substantially simplify the consent process to a simple acceptance or refusal of the evaluation. In 9.2% of evaluations the resident was transferred to the hospital before project nurses could complete a physical assessment. In these instances, we obtained vital sign and clinical examination data from hospital records.
Radiographic Classification
Since all subjects had at least one illness symptom, for this analysis we classified the presence or absence of pneumonia on the basis of reported radiographic findings. Using defined criteria, 2 clinicians independently separated radiology reports into 3 categories: (a) negative, (b) possible, or (c) probable or definite for pneumonia (hereafter, probable pneumonia). For example, a report describing “new left lower lobe infiltrate suggestive of pneumonia” would have been rated as probable, while a report indicating “possible infiltrate” or “infiltrate suggestive of pneumonia or congestive heart failure” would have been rated as possible. As radiologists rarely provide completely unequivocal readings, we did not separate probable and definite pneumonia. In St. Louis 2 clinicians evaluated the reports, and in central Missouri 2 of 4 clinicians considered each report. Where there was disagreement, all 6 raters from the 2 sites independently reviewed the reports and then attempted to reach consensus. For 13% of radiographs, the project radiologist independently interpreted the actual films. This occurred when: (1) consensus could not be achieved; or (2) consensus was possible pneumonia, but probable pneumonia was needed to quality the episode as an LRI under the project definition.
Statistical Analyses
As residents could be included more than once, the unit of analysis throughout is episode of illness. In our major analysis, we developed a multivariable logistic model to estimate the probability of radiographic pneumonia (possible or probable). Before beginning modeling, we imputed mean values for missing continuous data and the largest category for missing dichotomous variables (the number of missing values is noted in Table 2). Data imputation is less biased than dropping cases in developing multivariable models.14
Illness episodes were then randomly assigned to a two thirds model-development and a one third model-validation sample. On the basis of the literature and clinical experience, we defined categories of variables that might relate to the presence or absence of pneumonia, such as lung findings (eg, crackles, wheezes), respiratory symptoms (eg, cough, sputum production), vital signs, findings of delirium (eg, acute confusion, decreased alertness), and laboratory findings. Restricting our focus to the development sample, we selected the best representatives of these groups on clinical and statistical grounds. For continuous variables, we considered the shape of the relationship to presence of pneumonia. For example, both very high and very low pulse rates predicted increased risk of pneumonia. In such cases, we considered several different ways to represent the variable in the model. We also limited the range of some variables to avoid undue influence of outliers (approximately the 1% most extreme values). For example, pulse rate above 140 was set equal to 140.
We then employed forward and backward stepwise logistic regression with possible or probable pneumonia (also referred to as positive x-ray results) as the dependent variable. For final model inclusion, we required variables to bear a plausible relationship to the diagnosis of pneumonia and meet a statistical significance criterion (a=.05).
To obtain final estimates of the relationship of each model variable to pneumonia probability, we considered adjustments for 2 kinds of correlation within our data: (1) individuals are nested within facilities, and (2) subjects could be represented by more than one episode.15 Using generalized estimating equations (GEE) in Proc Genmod in SAS software (SAS Institute, Cary, NC),16 we noted that the effect of facilities was minor, but the effect of repeat episodes by the same subject was more marked. Consequently, we used GEE to account for repeat episodes on subjects. To avoid unstable GEE estimates, we dropped 5 episodes in the development sample and 8 in the overall sample (episodes beyond the 5th and 6th per individual, respectively).
Using parameter estimates from the development sample, we tested the model’s discrimination and calibration in the validation sample.17 To assess discrimination, we used the c-statistic, which evaluates among all possible pairs of individuals whether those with higher predicted risk are more likely to die. The c-statistic is also equal to the area under the receiver operating characteristic curve. To assess calibration—agreement between observed and predicted mortality over the range of predicted risk—we used the Hosmer-Lemeshow goodness-of-fit statistic.18 We then used estimates fitted to the overall sample to develop a simple additive score to provide a clinically usable prediction rule. Statistical analyses were performed with SAS statistical software.16
Results
Project nurses performed 2592 evaluations. In 90% (2337), residents received chest x-rays either in the nursing home or on hospital transfer. In 3 additional cases crucial information was missing from nursing home records. This left for final analysis 2334 episodes in 1474 individuals Figure 1.
Fifty-five percent of radiographs were interpreted as negative, 12% showed possible pneumonia, and 33% showed probable pneumonia. Most nursing home residents with pneumonia had few presenting symptoms; 80% had 3 or fewer respiratory or general symptoms. However, only 7.5% of subjects evaluated had no respiratory symptoms. Table 2 shows the relationship of selected variables to radiographic findings of absent, possible, or probable pneumonia. Though a few signs and symptoms are more common in those with positive (possible or probable pneumonia) than negative chest x-ray results, most did not discriminate at all. Fever (temperature Ž38°C) was present in 44.4% of positives but only 28.5% of negatives (P=.001).
Multivariable Analysis and Prediction Score
Our GEE model to predict radiographic pneumonia includes 3 vital sign abnormalities (fever, rapid pulse, and rapid respiratory rate), 2 lung findings (presence of crackles and absence of wheezes), 2 potential indicators of delirium (somnolence or decreased alertness and acute confusion), and elevated white blood count. Table 3 reports GEE estimates for the entire sample. Though only exhibiting fair overall performance, the model did well at distinguishing subjects with a high probability of pneumonia. In the 20% of subjects with the highest predicted risks, more than two thirds had pneumonia.
For the full range of values, the model derived on the development sample showed a c-statistic of 0.672, which reduced to 0.632 in the validation sample. A value of 1.0 would indicate perfect discrimination between those who did and did not have radiographic pneumonia, while a value of 0.5 would indicate no better than chance discrimination. Model calibration was not acceptable in the validation sample (Hosmer-Lemeshow goodness-of-fit statistic, P=.008). Inspection suggested the disagreement between predicted and observed probability of pneumonia was primarily with lower-risk estimates.
Because the model performed relatively well at distinguishing subjects very likely to have pneumonia, we created a simple point system aimed at identifying such high-risk individuals. Table 4 shows the scoring system. For 33% of subjects (score Ž3), there was a 56% or higher probability of radiographic pneumonia. An additional 24% of subjects (score of 2) had 44% probability of radiographic pneumonia. However, even those with the lowest scores (-1 to 0, 15% of subjects) still had a 24% probability of pneumonia. The relationship between the score and the probability of radiographic evidence of pneumonia is shown in Figure W1.*
Discussion
In a large community-based sample, we considered presenting symptoms, signs, and laboratory findings associated with radiographic pneumonia. Individual findings discriminated poorly, and we could not separate out a very-low-risk group. However, our simple scoring system identified approximately one third to slightly more than one half with high probability of pneumonia—individuals who might be treated without a confirmatory chest x-ray. If our data are confirmed, they suggest a simple clinical strategy in patients with respiratory or general symptoms Table 1 that might suggest pneumonia: (1) if there are no respiratory symptoms, consider other conditions, such as a urinary tract infection, that might fully explain the symptoms; (2) obtain information to apply our symptom score Table 4; (3) for those with scores of 2 or higher (some might choose 3 instead), treat for pneumonia; (4) for those with scores of -1, 0, or 1, obtain a chest radiograph as a guide to treatment.
Considering individual findings, fever was significantly more common in pneumonia, but only 43% of those with possible or probable pneumonia had a temperature of at least 38°C. This reaffirms common wisdom and previous findings that fever is frequently absent in elderly people with pneumonia.9,19 We also confirmed that few signs or symptoms are the norm for nursing home-acquired pneumonia.
Chest examination findings also do not adequately distinguish patients with and without pneumonia Table 2. Also, even expert physicians frequently differ on lung examination findings.20 Nonetheless, presence of crackles and absence of wheezing contribute to our scoring system. Both findings are seen with multiple conditions, but in our data crackles are slightly more associated with pneumonia, while wheezing is more strongly associated with other diseases.
The other components of our scoring system are clinical factors commonly associated with pneumonia. Though none individually discriminates well between those with and without pneumonia Table 2, several combined serve to identify a high-risk group.
Four previous studies from emergency department or outpatient settings developed clinical prediction rules to identify pneumonia.21-24 Criteria for identifying subjects varied substantially, and each rule has limited accuracy in predicting radiographic pneumonia.20 We had adequate data to evaluate 3 of the rules.21-23 As is usually the case when transporting a prediction rule to a new sample, none performed any better than our rule (data not shown). Our sample created the very difficult challenge for any prediction rule of a very high overall prevalence of pneumonia (45%). That made it unlikely that we could identify a low-risk group in whom x-ray studies could be readily forgone, but we were able to identify a highrisk group.
Limitations
Our findings are subject to several limitations. All facilities in our study were located in central or eastern Missouri, and not all physicians or eligible residents in those facilities participated. Compared with national data, we studied an unusually representative sample of nursing home residents from 36 facilities, including rural and urban locations. Also, in episodes excluded because of physician nonparticipation, residents were very similar to included residents in age, vital signs, and presenting symptoms (data available on request). More important, we lack an independent validation sample from a different cohort. Clinical prediction rules usually do not perform as well in independent samples. This is exemplified by the poor performance of the 3 rules we considered from other settings. Overall, our logistic model was only modest in discriminating and was not well calibrated for low-risk episodes in our reserved validation sample. Although we have developed a promising scoring system to identify residents with high probability of radiographic pneumonia, it needs to be validated in other samples of nursing home residents to determine its ultimate usefulness. For all these reasons, our results may not generalize.
Also, although we identified residents prospectively, project nurses were unable to evaluate 9.2% of residents before transfer to a hospital. Clinical findings abstracted from medical records, such as lung findings, may not have been complete. It is also possible that project nurses could have missed some important findings. However, our staff provided a higher level of expertise than is typically available in nursing homes. In fact, this may limit application of our findings. Nursing home staff vary widely in their ability to accurately examine residents or even identify illness. In many instances, facility staff had not obtained vital signs at the point when we identified a resident as ill enough to qualify for an evaluation.25 Therefore, in many nursing homes, physicians may lack confidence to apply our rule without an evaluation by a physician, advanced practice nurse, or physician assistant.
Finally, determining whether subjects had pneumonia primarily depended on our classification of radiographic reports. Though radiographs generally included 2 views, many were portable films of variable quality, and frequently there was no previous radiograph for comparison. In some subjects with pneumonia, radiographic infiltrates might not yet have developed. Also, even under ideal conditions, radiologists commonly disagree on the presence of pneumonia.26 Some subjects may have been misclassified. However, unless radiographic technique or interpretation was specifically related to clinical predictors, misclassification would simply diminish the relationship of predictors to pneumonia rather than creating a bias. We reviewed reports rather than radiographs, because that is the information usually available to clinicians faced with diagnosis and treatment decisions. We also paid special attention to avoiding any bias in the interpretations. All data were recorded before interpreting radiology reports and the interpretations were performed independent of clinical data. We also made special efforts to assure consistency in labeling radiology reports as possible, probable, or negative for pneumonia. When lack of agreement persisted, the study radiologist reinterpreted the actual films.
Conclusions
Most nursing home residents with pneumonia have few symptoms. We created a simple scoring to identify nursing home residents who have a high probability of radiographic pneumonia. If our results are confirmed, physicians might consider initiating treatment without an x-ray in such residents. Low scores do not rule out pneumonia, and most physicians would want to press for further diagnosis or treatment in this group.
Acknowledgments
This study was supported by the Agency for Healthcare Research and Quality (grant HS08551) and Dr Mehr’s Robert Wood Johnson Foundation Generalist Physician Faculty Scholars award. Dr Kruse was partially supported by an Institutional National Research Service Award (PE10038) from the Health Resources and Services Administration. Our project would not have been possible without the support of the many attending physicians, administrators, and staff of the involved nursing homes. Dr Clive Levine re-read more than 200 radiographs; Karen Davenport provided crucial administrative support; and Karen Madrone, MPA, assisted with manuscript preparation. Many other unnamed project staff also contributed.
STUDY DESIGN: This was a prospective cohort study.
POPULATION: The residents of 36 nursing homes in central Missouri and the St. Louis area with signs or symptoms suggesting a lower respiratory infection were included.
OUTCOME MEASURED: We compared evaluation findings by project nurses with findings reported from chest radiographs.
RESULTS: Among 2334 episodes of illness in 1474 nursing home residents, 45% of the radiograph reports suggested pneumonia (possible=12%; probable or definite = 33%). In 80% of pneumonia episodes, subjects had 3 or fewer respiratory or general symptoms. Eight variables were significant independent predictors of pneumonia (increased pulse, respiratory rate Ž30, temperature Ž38°C, somnolence or decreased alertness, presence of acute confusion, lung crackles on auscultation, absence of wheezes, and increased white blood count). A simple score (range = -1 to 8) on the basis of these variables identified 33% of subjects (score Ž3) with more than 50% probability of pneumonia and an additional 24% (score of 2) with 44% probability of pneumonia.
CONCLUSIONS: Pneumonia in nursing home residents is usually associated with few symptoms. Nonetheless, a simple clinical prediction rule can identify residents at very high risk for pneumonia. If validated in other studies, physicians could consider treating such residents without obtaining a chest radiograph.
Pneumonia is a leading cause of morbidity, mortality, and hospitalization of nursing home residents.1-8 Atypical presentations and fewer presenting signs and symptoms in older patients complicate diagnosis.9,10 Also, clinician (physician, nurse practitioner, and physician assistant) visits to nursing homes are often sporadic, and radiology facilities are rarely on the premises. As a consequence, residents are commonly sent to emergency departments for evaluation,4,11,12 which undoubtedly contributes to a high hospitalization rate.
Clinicians who periodically see nursing home residents could benefit from a simple clinical tool to identify pneumonia. No large studies of community nursing home residents have systematically studied findings associated with pneumonia. As part of the Missouri LRI Project, we examined how well clinical findings predict radiographic pneumonia.
Methods
The Missouri LRI Project was a prospective observational study in 36 nursing homes in Central Missouri and St. Louis designed to investigate predictors of 2 outcomes of lower respiratory infections (LRIs): mortality and functional decline. Potential cases were identified from August 15, 1995, through September 29, 1998; however, all facilities were not involved until fall 1997. Study facilities were similar in size, ownership, and occupancy to national estimates from the 1995 National Nursing Home Survey (data available on request).13
We trained nursing home staff to report ill residents with any of 6 respiratory symptoms (eg, cough, dyspnea, sputum production) or 6 general symptoms (eg, fever, decline in mobility, mental status changes). Project nurses called and visited facilities frequently to reinforce reporting. Under a physician-authorized protocol, ill residents with a possible LRI received a standardized evaluation by a trained project nurse and usually a chest radiograph, complete blood count, and a chemistry panel. Complete criteria for triggering an evaluation are listed in Table 1. For this paper, we were concerned with the 90% of evaluated residents who received a chest radiograph. Criteria for excluding residents from evaluation are summarized in the Figure 1.
The nurse evaluation included an inventory of current symptoms, a review of important chronic conditions (eg, congestive heart failure), and a targeted physical examination. The examination included vital signs and the following body areas or systems: ears, nose, and throat; cardiac; abdominal; neurologic; extremities; skin; and a detailed lung examination. Most project nurses had advanced practice training; the remainder had extensive clinical experience and training in physical assessment. All received an individualized training session with a project geriatrician. Project nurses had substantially more experience than the nursing home staff, who usually report clinical findings to physicians.
Results of the evaluation were reported to the attending physician, who made all treatment decisions. Since the evaluations were clinically appropriate care authorized by individual attending physicians, the institutional review boards that reviewed the project allowed us to substantially simplify the consent process to a simple acceptance or refusal of the evaluation. In 9.2% of evaluations the resident was transferred to the hospital before project nurses could complete a physical assessment. In these instances, we obtained vital sign and clinical examination data from hospital records.
Radiographic Classification
Since all subjects had at least one illness symptom, for this analysis we classified the presence or absence of pneumonia on the basis of reported radiographic findings. Using defined criteria, 2 clinicians independently separated radiology reports into 3 categories: (a) negative, (b) possible, or (c) probable or definite for pneumonia (hereafter, probable pneumonia). For example, a report describing “new left lower lobe infiltrate suggestive of pneumonia” would have been rated as probable, while a report indicating “possible infiltrate” or “infiltrate suggestive of pneumonia or congestive heart failure” would have been rated as possible. As radiologists rarely provide completely unequivocal readings, we did not separate probable and definite pneumonia. In St. Louis 2 clinicians evaluated the reports, and in central Missouri 2 of 4 clinicians considered each report. Where there was disagreement, all 6 raters from the 2 sites independently reviewed the reports and then attempted to reach consensus. For 13% of radiographs, the project radiologist independently interpreted the actual films. This occurred when: (1) consensus could not be achieved; or (2) consensus was possible pneumonia, but probable pneumonia was needed to quality the episode as an LRI under the project definition.
Statistical Analyses
As residents could be included more than once, the unit of analysis throughout is episode of illness. In our major analysis, we developed a multivariable logistic model to estimate the probability of radiographic pneumonia (possible or probable). Before beginning modeling, we imputed mean values for missing continuous data and the largest category for missing dichotomous variables (the number of missing values is noted in Table 2). Data imputation is less biased than dropping cases in developing multivariable models.14
Illness episodes were then randomly assigned to a two thirds model-development and a one third model-validation sample. On the basis of the literature and clinical experience, we defined categories of variables that might relate to the presence or absence of pneumonia, such as lung findings (eg, crackles, wheezes), respiratory symptoms (eg, cough, sputum production), vital signs, findings of delirium (eg, acute confusion, decreased alertness), and laboratory findings. Restricting our focus to the development sample, we selected the best representatives of these groups on clinical and statistical grounds. For continuous variables, we considered the shape of the relationship to presence of pneumonia. For example, both very high and very low pulse rates predicted increased risk of pneumonia. In such cases, we considered several different ways to represent the variable in the model. We also limited the range of some variables to avoid undue influence of outliers (approximately the 1% most extreme values). For example, pulse rate above 140 was set equal to 140.
We then employed forward and backward stepwise logistic regression with possible or probable pneumonia (also referred to as positive x-ray results) as the dependent variable. For final model inclusion, we required variables to bear a plausible relationship to the diagnosis of pneumonia and meet a statistical significance criterion (a=.05).
To obtain final estimates of the relationship of each model variable to pneumonia probability, we considered adjustments for 2 kinds of correlation within our data: (1) individuals are nested within facilities, and (2) subjects could be represented by more than one episode.15 Using generalized estimating equations (GEE) in Proc Genmod in SAS software (SAS Institute, Cary, NC),16 we noted that the effect of facilities was minor, but the effect of repeat episodes by the same subject was more marked. Consequently, we used GEE to account for repeat episodes on subjects. To avoid unstable GEE estimates, we dropped 5 episodes in the development sample and 8 in the overall sample (episodes beyond the 5th and 6th per individual, respectively).
Using parameter estimates from the development sample, we tested the model’s discrimination and calibration in the validation sample.17 To assess discrimination, we used the c-statistic, which evaluates among all possible pairs of individuals whether those with higher predicted risk are more likely to die. The c-statistic is also equal to the area under the receiver operating characteristic curve. To assess calibration—agreement between observed and predicted mortality over the range of predicted risk—we used the Hosmer-Lemeshow goodness-of-fit statistic.18 We then used estimates fitted to the overall sample to develop a simple additive score to provide a clinically usable prediction rule. Statistical analyses were performed with SAS statistical software.16
Results
Project nurses performed 2592 evaluations. In 90% (2337), residents received chest x-rays either in the nursing home or on hospital transfer. In 3 additional cases crucial information was missing from nursing home records. This left for final analysis 2334 episodes in 1474 individuals Figure 1.
Fifty-five percent of radiographs were interpreted as negative, 12% showed possible pneumonia, and 33% showed probable pneumonia. Most nursing home residents with pneumonia had few presenting symptoms; 80% had 3 or fewer respiratory or general symptoms. However, only 7.5% of subjects evaluated had no respiratory symptoms. Table 2 shows the relationship of selected variables to radiographic findings of absent, possible, or probable pneumonia. Though a few signs and symptoms are more common in those with positive (possible or probable pneumonia) than negative chest x-ray results, most did not discriminate at all. Fever (temperature Ž38°C) was present in 44.4% of positives but only 28.5% of negatives (P=.001).
Multivariable Analysis and Prediction Score
Our GEE model to predict radiographic pneumonia includes 3 vital sign abnormalities (fever, rapid pulse, and rapid respiratory rate), 2 lung findings (presence of crackles and absence of wheezes), 2 potential indicators of delirium (somnolence or decreased alertness and acute confusion), and elevated white blood count. Table 3 reports GEE estimates for the entire sample. Though only exhibiting fair overall performance, the model did well at distinguishing subjects with a high probability of pneumonia. In the 20% of subjects with the highest predicted risks, more than two thirds had pneumonia.
For the full range of values, the model derived on the development sample showed a c-statistic of 0.672, which reduced to 0.632 in the validation sample. A value of 1.0 would indicate perfect discrimination between those who did and did not have radiographic pneumonia, while a value of 0.5 would indicate no better than chance discrimination. Model calibration was not acceptable in the validation sample (Hosmer-Lemeshow goodness-of-fit statistic, P=.008). Inspection suggested the disagreement between predicted and observed probability of pneumonia was primarily with lower-risk estimates.
Because the model performed relatively well at distinguishing subjects very likely to have pneumonia, we created a simple point system aimed at identifying such high-risk individuals. Table 4 shows the scoring system. For 33% of subjects (score Ž3), there was a 56% or higher probability of radiographic pneumonia. An additional 24% of subjects (score of 2) had 44% probability of radiographic pneumonia. However, even those with the lowest scores (-1 to 0, 15% of subjects) still had a 24% probability of pneumonia. The relationship between the score and the probability of radiographic evidence of pneumonia is shown in Figure W1.*
Discussion
In a large community-based sample, we considered presenting symptoms, signs, and laboratory findings associated with radiographic pneumonia. Individual findings discriminated poorly, and we could not separate out a very-low-risk group. However, our simple scoring system identified approximately one third to slightly more than one half with high probability of pneumonia—individuals who might be treated without a confirmatory chest x-ray. If our data are confirmed, they suggest a simple clinical strategy in patients with respiratory or general symptoms Table 1 that might suggest pneumonia: (1) if there are no respiratory symptoms, consider other conditions, such as a urinary tract infection, that might fully explain the symptoms; (2) obtain information to apply our symptom score Table 4; (3) for those with scores of 2 or higher (some might choose 3 instead), treat for pneumonia; (4) for those with scores of -1, 0, or 1, obtain a chest radiograph as a guide to treatment.
Considering individual findings, fever was significantly more common in pneumonia, but only 43% of those with possible or probable pneumonia had a temperature of at least 38°C. This reaffirms common wisdom and previous findings that fever is frequently absent in elderly people with pneumonia.9,19 We also confirmed that few signs or symptoms are the norm for nursing home-acquired pneumonia.
Chest examination findings also do not adequately distinguish patients with and without pneumonia Table 2. Also, even expert physicians frequently differ on lung examination findings.20 Nonetheless, presence of crackles and absence of wheezing contribute to our scoring system. Both findings are seen with multiple conditions, but in our data crackles are slightly more associated with pneumonia, while wheezing is more strongly associated with other diseases.
The other components of our scoring system are clinical factors commonly associated with pneumonia. Though none individually discriminates well between those with and without pneumonia Table 2, several combined serve to identify a high-risk group.
Four previous studies from emergency department or outpatient settings developed clinical prediction rules to identify pneumonia.21-24 Criteria for identifying subjects varied substantially, and each rule has limited accuracy in predicting radiographic pneumonia.20 We had adequate data to evaluate 3 of the rules.21-23 As is usually the case when transporting a prediction rule to a new sample, none performed any better than our rule (data not shown). Our sample created the very difficult challenge for any prediction rule of a very high overall prevalence of pneumonia (45%). That made it unlikely that we could identify a low-risk group in whom x-ray studies could be readily forgone, but we were able to identify a highrisk group.
Limitations
Our findings are subject to several limitations. All facilities in our study were located in central or eastern Missouri, and not all physicians or eligible residents in those facilities participated. Compared with national data, we studied an unusually representative sample of nursing home residents from 36 facilities, including rural and urban locations. Also, in episodes excluded because of physician nonparticipation, residents were very similar to included residents in age, vital signs, and presenting symptoms (data available on request). More important, we lack an independent validation sample from a different cohort. Clinical prediction rules usually do not perform as well in independent samples. This is exemplified by the poor performance of the 3 rules we considered from other settings. Overall, our logistic model was only modest in discriminating and was not well calibrated for low-risk episodes in our reserved validation sample. Although we have developed a promising scoring system to identify residents with high probability of radiographic pneumonia, it needs to be validated in other samples of nursing home residents to determine its ultimate usefulness. For all these reasons, our results may not generalize.
Also, although we identified residents prospectively, project nurses were unable to evaluate 9.2% of residents before transfer to a hospital. Clinical findings abstracted from medical records, such as lung findings, may not have been complete. It is also possible that project nurses could have missed some important findings. However, our staff provided a higher level of expertise than is typically available in nursing homes. In fact, this may limit application of our findings. Nursing home staff vary widely in their ability to accurately examine residents or even identify illness. In many instances, facility staff had not obtained vital signs at the point when we identified a resident as ill enough to qualify for an evaluation.25 Therefore, in many nursing homes, physicians may lack confidence to apply our rule without an evaluation by a physician, advanced practice nurse, or physician assistant.
Finally, determining whether subjects had pneumonia primarily depended on our classification of radiographic reports. Though radiographs generally included 2 views, many were portable films of variable quality, and frequently there was no previous radiograph for comparison. In some subjects with pneumonia, radiographic infiltrates might not yet have developed. Also, even under ideal conditions, radiologists commonly disagree on the presence of pneumonia.26 Some subjects may have been misclassified. However, unless radiographic technique or interpretation was specifically related to clinical predictors, misclassification would simply diminish the relationship of predictors to pneumonia rather than creating a bias. We reviewed reports rather than radiographs, because that is the information usually available to clinicians faced with diagnosis and treatment decisions. We also paid special attention to avoiding any bias in the interpretations. All data were recorded before interpreting radiology reports and the interpretations were performed independent of clinical data. We also made special efforts to assure consistency in labeling radiology reports as possible, probable, or negative for pneumonia. When lack of agreement persisted, the study radiologist reinterpreted the actual films.
Conclusions
Most nursing home residents with pneumonia have few symptoms. We created a simple scoring to identify nursing home residents who have a high probability of radiographic pneumonia. If our results are confirmed, physicians might consider initiating treatment without an x-ray in such residents. Low scores do not rule out pneumonia, and most physicians would want to press for further diagnosis or treatment in this group.
Acknowledgments
This study was supported by the Agency for Healthcare Research and Quality (grant HS08551) and Dr Mehr’s Robert Wood Johnson Foundation Generalist Physician Faculty Scholars award. Dr Kruse was partially supported by an Institutional National Research Service Award (PE10038) from the Health Resources and Services Administration. Our project would not have been possible without the support of the many attending physicians, administrators, and staff of the involved nursing homes. Dr Clive Levine re-read more than 200 radiographs; Karen Davenport provided crucial administrative support; and Karen Madrone, MPA, assisted with manuscript preparation. Many other unnamed project staff also contributed.
1. Irvine PW, Van Buren N, Crossley K. Causes for hospitalization of nursing home residents: the role of infection. J Am Geriatr Soc 1984;32:103-07.
2. Murtaugh CM, Freiman MP. Nursing home residents at risk of hospitalization and the characteristics of their hospital stays. Gerontologist 1995;35:35-43.
3. Jackson MM, Fierer J, Barrett-Connor E, et al. Intensive surveillance for infections in a three-year study of nursing home patients. Am J Epidemiol 1992;135:685-96.
4. Brooks S, Warshaw G, Hasse L, Kues JR. The physician decision-making process in transferring nursing home patients to the hospital. Arch Intern Med 1994;154:902-08.
5. Fried TR, Gillick MR, Lipsitz LA. Whether to transfer? Factors associated with hospitalization and outcome of elderly long-term care patients with pneumonia. J Gen Intern Med 1995;10:246-50.
6. Degelau J, Guay D, Straub K, Luxenberg MG. Effectiveness of oral antibiotic treatment in nursing home-acquired pneumonia. J Am Geriatr Soc 1995;43:245-51.
7. Muder RR, Brennen C, Swenson DL, Wagener M. Pneumonia in a long-term care facility: a prospective study of outcome. Arch Intern Med 1996;156:2365-70.
8. Medina-Walpole AM, Katz PR. Nursing home-acquired pneumonia. J Am Geriatr Soc 1999;47:1005-15.
9. Harper C, Newton P. Clinical aspects of pneumonia in the elderly veteran. J Am Geriatr Soc 1989;37:867-72.
10. Metlay JP, Schulz R, Li YH, Singer DE, Marrie TJ, Coley CM, et al. Influence of age on symptoms at presentation in patients with community-acquired pneumonia. Arch Intern Med 1997;157:1453-59.
11. Kayser-Jones JS, Wiener CL, Barbaccia JC. Factors contributing to the hospitalization of nursing home residents. Gerontologist 1989;29:502-10.
12. Scott HD, Logan M, Waters WJ, Jr, et al. Medical practice variation in the management of acute medical events in nursing homes: a pilot study. R I Med J 1988;71:69-74.
13. Gabrel CS, Jones A. The National Nursing Home Survey: 1997 summary. Vital Health Stat-series 13: data from the National Health Survey 2000;147:1-121.
14. Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-87.
15. Preisser JS, Koch GG. Categorical data analysis in public health. nn Rev Public Health 1997;18:51-82.
16. SAS Institute Inc The SAS System for Windows. Version 6.1. Cary, NC: SAS Institute, Inc; 1996.
17. D’Agostino RB, Sr, Griffith JL, Schmid CH, Terrin N. Measures for evaluating model performance. In: Proceedings of the biometrics section, 1997. Alexandria, Va: American Statistical Association. Biometrics section; 1998;253-58.
18. Hosmer DW Jr, Lemeshow S. Applied logistic regression. New York, NY: Wiley; 1989.
19. Marrie TJ, Haldane EV, Faulkner RS, Durant H, Kwan C. Community-acquired pneumonia requiring hospitalization: is it different in the elderly? J Am Geriatr Soc 1985;33:671-80.
20. Metlay JP, Kapoor WN, Fine MJ. Does this patient have community-acquired pneumonia? Diagnosing pneumonia by history and physical examination. JAMA 1997;278:1440-45.
21. Heckerling PS, Tape TG, Wigton RS, et al. Clinical prediction rule for pulmonary infiltrates. Ann Intern Med 1990;113:664-70.
22. Singal BM, Hedges JR, Radack KL. Decision rules and clinical prediction of pneumonia: evaluation of low-yield criteria. Ann Emerg Med 1989;18:13-20.
23. Gennis P, Gallagher J, Falvo C, Baker S, Than W. Clinical criteria for the detection of pneumonia in adults: guidelines for ordering chest roentgenograms in the emergency department. J Emerg Med 1989;7:263-68.
24. Diehr P, Wood RW, Bushyhead J, Krueger L, Wolcott B, Tompkins RK. Prediction of pneumonia in outpatients with acute cough—a statistical approach. J Chronic Dis 1984;37:215.-
25. Barry CR, Brown K, Esker D, Denning MD, Kruse RL, Binder EF. Nursing assessment of ill nursing home residents. In press.
26. Albaum MN, Hill LC, Murphy M, et al. Interobserver reliability of the chest radiograph in community-acquired pneumonia: PORT Investigators. Chest 1996;110:343-50.
1. Irvine PW, Van Buren N, Crossley K. Causes for hospitalization of nursing home residents: the role of infection. J Am Geriatr Soc 1984;32:103-07.
2. Murtaugh CM, Freiman MP. Nursing home residents at risk of hospitalization and the characteristics of their hospital stays. Gerontologist 1995;35:35-43.
3. Jackson MM, Fierer J, Barrett-Connor E, et al. Intensive surveillance for infections in a three-year study of nursing home patients. Am J Epidemiol 1992;135:685-96.
4. Brooks S, Warshaw G, Hasse L, Kues JR. The physician decision-making process in transferring nursing home patients to the hospital. Arch Intern Med 1994;154:902-08.
5. Fried TR, Gillick MR, Lipsitz LA. Whether to transfer? Factors associated with hospitalization and outcome of elderly long-term care patients with pneumonia. J Gen Intern Med 1995;10:246-50.
6. Degelau J, Guay D, Straub K, Luxenberg MG. Effectiveness of oral antibiotic treatment in nursing home-acquired pneumonia. J Am Geriatr Soc 1995;43:245-51.
7. Muder RR, Brennen C, Swenson DL, Wagener M. Pneumonia in a long-term care facility: a prospective study of outcome. Arch Intern Med 1996;156:2365-70.
8. Medina-Walpole AM, Katz PR. Nursing home-acquired pneumonia. J Am Geriatr Soc 1999;47:1005-15.
9. Harper C, Newton P. Clinical aspects of pneumonia in the elderly veteran. J Am Geriatr Soc 1989;37:867-72.
10. Metlay JP, Schulz R, Li YH, Singer DE, Marrie TJ, Coley CM, et al. Influence of age on symptoms at presentation in patients with community-acquired pneumonia. Arch Intern Med 1997;157:1453-59.
11. Kayser-Jones JS, Wiener CL, Barbaccia JC. Factors contributing to the hospitalization of nursing home residents. Gerontologist 1989;29:502-10.
12. Scott HD, Logan M, Waters WJ, Jr, et al. Medical practice variation in the management of acute medical events in nursing homes: a pilot study. R I Med J 1988;71:69-74.
13. Gabrel CS, Jones A. The National Nursing Home Survey: 1997 summary. Vital Health Stat-series 13: data from the National Health Survey 2000;147:1-121.
14. Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-87.
15. Preisser JS, Koch GG. Categorical data analysis in public health. nn Rev Public Health 1997;18:51-82.
16. SAS Institute Inc The SAS System for Windows. Version 6.1. Cary, NC: SAS Institute, Inc; 1996.
17. D’Agostino RB, Sr, Griffith JL, Schmid CH, Terrin N. Measures for evaluating model performance. In: Proceedings of the biometrics section, 1997. Alexandria, Va: American Statistical Association. Biometrics section; 1998;253-58.
18. Hosmer DW Jr, Lemeshow S. Applied logistic regression. New York, NY: Wiley; 1989.
19. Marrie TJ, Haldane EV, Faulkner RS, Durant H, Kwan C. Community-acquired pneumonia requiring hospitalization: is it different in the elderly? J Am Geriatr Soc 1985;33:671-80.
20. Metlay JP, Kapoor WN, Fine MJ. Does this patient have community-acquired pneumonia? Diagnosing pneumonia by history and physical examination. JAMA 1997;278:1440-45.
21. Heckerling PS, Tape TG, Wigton RS, et al. Clinical prediction rule for pulmonary infiltrates. Ann Intern Med 1990;113:664-70.
22. Singal BM, Hedges JR, Radack KL. Decision rules and clinical prediction of pneumonia: evaluation of low-yield criteria. Ann Emerg Med 1989;18:13-20.
23. Gennis P, Gallagher J, Falvo C, Baker S, Than W. Clinical criteria for the detection of pneumonia in adults: guidelines for ordering chest roentgenograms in the emergency department. J Emerg Med 1989;7:263-68.
24. Diehr P, Wood RW, Bushyhead J, Krueger L, Wolcott B, Tompkins RK. Prediction of pneumonia in outpatients with acute cough—a statistical approach. J Chronic Dis 1984;37:215.-
25. Barry CR, Brown K, Esker D, Denning MD, Kruse RL, Binder EF. Nursing assessment of ill nursing home residents. In press.
26. Albaum MN, Hill LC, Murphy M, et al. Interobserver reliability of the chest radiograph in community-acquired pneumonia: PORT Investigators. Chest 1996;110:343-50.