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Use of Microalbuminuria Testing in Persons with Type 2 Diabetes: Are the Right Patients Being Tested?
STUDY DESIGN: This was a retrospective cross-sectional study.
POPULATION: We included a total of 278 adult patients with type 2 diabetes seen during 1998 and 1999 at the family medicine practices of the Medical University of South Carolina.
OUTCOMES MEASURED: The outcomes were microalbuminuria testing during either 1998 or 1999 and the initiation of medication if the screening test result was positive.
RESULTS: We found that patients who could derive the greatest benefit from testing (ie, those without preexisting proteinuria or who were not receiving an angiotensin-blocking drug) were no more likely to be screened for microalbuminuria than those with existing proteinuria (16% vs 18%, P=.84) or those who were already being treated with an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (16% vs 16%, P=.83). Also, when the microalbuminuria test result was positive, only 40% of the patients were placed on angiotensin-blocking drugs.
CONCLUSIONS: Physician use of microalbuminuria screening does not follow established guidelines. The test appears to be used for many patients who might not need to be screened, and it is not always used for patients who should be screened. Consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes.
Nephropathy is one of the most common long-term side effects of diabetes mellitus and accounts for the largest percentage of patients requiring chronic renal dialysis in the United States and Europe.1,2 The high prevalence of type 2 diabetes among adults in the United States and the high rate of nephropathy in these individuals pose a great economic burden to the health care system.
Several studies have noted that angiotensin-converting enzyme inhibitors (ACEIs) can delay the progression of renal impairment in patients with type 2 diabetes.3-7 Patients with diabetic nephropathy generally progress from a stage of normal renal function to microalbuminuria, gross proteinuria, and then renal dysfunction.1 ACEIs appear to delay or prevent the progression from microalbuminuria to proteinuria. Although there are no controlled trials that show microalbuminuria screening as effective at reducing proteinuria, expert panels of the American Diabetes Association8 and National Kidney Foundation9 have recommended that patients with type 2 diabetes receive annual screening for microalbuminuria, and if it is detected on 2 of 3 occasions, these patients should be placed on an ACEI or an angiotensin receptor blocker (ARB) for renal protection.
Initial evaluation of data from primary care practices, however, reveals that screening for microalbuminuria is not optimal.10,11 One reason microalbuminuria screening may happen less often than expected could be that many patients with diabetes mellitus are already being treated with an ACEI or ARB for hypertension, congestive heart failure, or other reasons. Some physicians also might employ an ACEI or ARB prophylactically, starting treatment before recognizing microalbuminuria. Given that these patients are already being treated with an ACEI or ARB, clinicians may not recognize any usefulness in performing a microalbuminuria test.
The purpose of our study was to examine what patient factors are associated with screening for microalbuminuria in patients with type 2 diabetes mellitus. Specifically, we examined how often patients who were not screened were already being treated with an ACEI or ARB. Also, we hoped to characterize the populations being screened more fully to determine if certain patient and disease characteristics were associated with the likelihood of a screening test being performed. A better awareness of these characteristics will help in targeting specific patient groups and changing physician behavior.
Methods
Sample
Our sample was drawn from the primary care practice in the department of family medicine at the Medical University of South Carolina (MUSC) in 1998 and 1999. The department provided care for approximately 18,000 patients who made 42,000 and 48,000 patient visits in 1998 and 1999, respectively, at 2 clinical sites. These 2 sites serve a diverse population of patients in downtown Charleston and a nearby suburban area, which in 1998 had a payer mix distribution that was 26% Medicaid, 27% Medicare, 37% commercially insured, and 10% self-pay.
We identified patients with diabetes at the 2 clinical sites from a search of the problem list in an electronic medical record database that has been used in the department of family medicine since 1992 Table 1. All patients aged between 18 years and 65 years in 2000 and who had an appointment scheduled in 1998 or 1999 were included. The charts that were initially selected for review had diabetes mellitus listed as a problem; after the chart review, we excluded 18 patients from the study because they were using insulin or had not been seen in the practices since 1995, even though they had scheduled an appointment in 1998 or 1999. This left a final sample size of 278.
Data Collection and Variables
Two medical students performed the chart reviews and recorded the following variables when available: age, weight, sex, serum creatinine level, hemoglobin A1C (Hb A1C) level, proteinuria on urinalysis testing, blood pressure, total serum cholesterol, and whether a microalbuminuria test was recommended and, if performed, the results. Race was not included because the patient charts do not consistently note the patient’s race. Also, because care is often shared between attending and resident physicians, we did not include the physician training level as a variable in our analysis.
To determine whether patients were on ACEI or ARB therapy, we searched the electronic medical record database for all medications in the previous 5 years. The medical record used during this period required all prescriptions to be entered before a printed version could be generated, so we could determine if a drug had been used in the past. Although this system overlooked prescriptions that might be called in to a pharmacy and not documented in the record, it captured every prescription written by a physician in the practice. When an ACEI or ARB was used, we examined whether the medication had been started before screening was indicated or after a microalbuminuria test was performed.
We searched the laboratory section of the electronic medical record and also the hospital patient database to determine if the hospital laboratory had performed the test. Searching the hospital database would indicate if the test was performed by any other clinician (eg, an endocrinologist) or in another setting (eg, inpatient) in the university medical center. Whether a microalbuminuria test was recommended was recorded, with the returned value (if available) and the date the test was recommended. We considered values greater than 20 mg per L positive for microalbuminuria. Protein-uria tests were considered positive if they returned a 1+ protein or greater result. We also recorded whether the subject was on an ACEI or ARB therapy, and if so at what date it had been prescribed.
To minimize inter-rater variability, the 2 medical students each reviewed a pilot sample of the same 20 charts. Data were compared and differences between the auditors were reviewed to standardize definitions of data elements. After standardization, sets of 10 different charts were selected, and the process was repeated until the data from 40 consecutive charts were recorded identically by both students.
Analysis
When comparing mean values, we performed a Student t test to determine statistical significance. A chi-square test was done to determine statistical significance when comparing proportions. A P value of <.05 was determined to be statistically significant.
Results
Of the 278 eligible patients, 44 (16%) had a urinalysis with 1+ or greater protein result at baseline; 18 (41%) of these were already taking an ACEI or ARB drug. In patients without previous evidence of proteinuria, 51 (18%) patients were using ACEI or ARB therapy. This left 183 patients (66%) who had no evidence of renal disease and who were not using ACEI or ARB therapy and therefore were the prime candidates for microalbuminuria screening Figure 1.
When we examined the demographics and clinical variables of these 3 groups, we found that patients with proteinuria or who were already using drug treatment were older and had higher systolic and diastolic blood pressures than those who were not. Unexpectedly, we also found that patients with existing proteinuria had lower Hb A1C levels than patients in the other 2 categories.
Of these prime candidates for screening, only 31 (17%) received at least 1 microalbuminuria test between 1995 and 1999. The rate of screening in this group was no different from those who were taking an ACEI or ARB drug (16%, P=.83) or already had gross proteinuria (18%, P=.84).
When we examined the patients who were most likely to benefit from screening and looked at demographic or clinical factors that might influence whether a screening test was performed, we found that patients who received microalbuminuria testing were very similar to those who did not. The only difference we found was that patients who received screening had lower systolic blood pressures than those who were not screened. Weight, age, Hb A1C levels, and cholesterol levels were not predictors of being screened for microalbuminuria Table 2.
Because of the low rates of microalbuminuria screening for patients who were eligible and the relatively frequent use of screening in patients who already had evidence of gross proteinuria, we were interested in what clinicians did when a microalbuminuria test result was positive. In the group without evidence of proteinuria and not using ACEI or ARB therapy, 10 of the 31 patients who received screening for microalbuminuria tested positive. However, only 4 (40%) were placed on ACE inhibitor or ARB therapy.
Discussion
Our data suggest that several problems exist in the use and interpretation of microalbuminuria testing in the primary care setting. First, microalbuminuria testing is being performed on only 1 of 5 adult patients with type 2 diabetes. Second, in this practice, testing is not targeted to the patients who are most likely to benefit from the results. Rather, the tests seemed to be used indiscriminately. Finally, even when patients are screened and found to have microalbuminuria, only a small percentage were started on appropriate therapy. At least in this patient population, it appears that ACEI or ARB therapy is reserved for patients with higher blood pressures rather than used for renal protection.
The observation that patients with existing proteinuria or who were on ACEI or ARB therapy were screened just as often as those who were prime candidates for screening contradicts our initial hypothesis. We had assumed that clinicians would not screen patients who were on ACEI or ARB therapy, reducing the overall screening rate. Apparently, this is not the case. At least in this practice, a low screening rate is not due to selective screening.
The lack of optimal use of microalbuminuria testing and the failure to respond appropriately to positive test results suggests that current recommendations have not been embraced by physicians. Also, the complexity of carrying out these recommendations may make it difficult to integrate this screening into routine practice. If the current evidence on ACEI and ARB therapy for the prevention of renal dysfunction is to be translated into practice, either greater emphasis needs to be placed on microalbuminuria screening or more efficient ways to provide renal protection for patients with diabetes should be considered. Other studies have found that between 17% and 30% of patients with type 2 diabetes have microalbuminuria.1,12,13 Although primary care physicians report that they provide microalbuminuria screening to a large percentage of their patients with diabetes, in fact only a small percentage of those who should be screened actually are screened.10 Suboptimal screening rates for important conditions seen in primary care are not unique for microalbuminuria. Other studies have documented comparable low screening rates for a wide variety of cancers.14 Since physicians do not screen reliably for potentially fatal diseases with screening modalities that have been available for decades, it is unlikely that their behavior is likely to improve when asked to screen for microalbuminuria.
Also, recent evidence that ACEI therapy may improve endothelial function in patients with type 2 diabetes suggests that even patients without microalbuminuria may benefit from routine ACEI therapy.15 Other studies suggest that routine use of ACEIs in middle-aged patients with type 2 diabetes may provide substantial benefits at only modest costs compared with a screening strategy.16 These data suggest that a more effective strategy would be to advise that all patients with type 2 diabetes start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while assuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. However, using this strategy, patients who may not have proteinuria will have to take the medication for a prolonged period, pay for it, and run the risks for any complications associated with using the drug.
Limitations
Our study has several limitations. Only 1 practice was examined, and it was part of a residency training practice. This means that less-experienced clinicians were providing care that could reduce the overall rate of screening. However, the rate of screening observed in this study was very similar to rates found in the practices of clinicians with more experience,11,12 suggesting that the lack of experience of resident physicians may be balanced by the oversight provided by faculty preceptors.
Another limitation is that it was not possible to account for microalbuminuria screening completed outside the MUSC medical center. Patients who split their care among several providers could have had testing performed in other health care facilities. However, since more than 95% of the referrals from the MUSC Family Medicine Center stay within the university health care system, it is doubtful that many patients would have received testing outside the search capabilities of the hospital laboratory database.
Finally, the study was limited in its power to detect small differences between the groups. We originally conceived our project as an exploratory study to determine how many patients were already taking ACEIs and the potential effect of this on overall screening rates for microalbuminuria. Without any reference for the percentage of patients who were taking ACEIs, we could not perform an ad hoc power analysis. However, a post hoc analysis shows that for a sample in which the groups are matched in a 1-to-3 ratio (approximating the proportion of the 51 patients in our sample taking ACEIs and the 183 not taking these drugs) and given the study sample size, our study had a power of 80% to detect a difference in screening rates between 20% in the baseline group and 5% in the ACE or ARB groups. The actual difference seen in our study was much smaller, which increases the possibility of a type II error.
Conclusions
Because physician use of microalbuminuria screening does not follow established guidelines, consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes. One proposed strategy would advise all patients with type 2 diabetes to start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while ensuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. It is unknown, however, whether patients would accept universal treatment rather than periodic screening. This is an important question that should be addressed before any population-based strategies are adopted.
1. McKenna K, Thompson C. Microalbuminuria: a marker to increased renal and cardiovascular risk in diabetes mellitus. Scottish Med J 1997;42:99-104.
2. American Diabetes Association. Standards of medical care for patients with diabetes mellitus (position statement). Diabetes Care 2000;23(suppl):S32—42.
3. Vibreti G, Mogensen CE, Groop LC, Pauls JF. Effect of captopril on progression to clinical proteinuria in patients with insulin-dependent diabetes mellitus and microalbuminuria. JAMA 1994;271:275-79.
4. Ravid M, Brosh D, Levi Z, et al. Use of enalapril to attenuate decline in renal function in normotensive, normoalbuminuric patients with type II diabetes mellitus: a randomized, controlled trial. Ann Intern Med 1998;128:982-88.
5. Ahmad J, Siddiqui MA, Ahmad H. Effective postponement of diabetic nephropathy with enalapril in type II diabetes patients with microalbuminuria. Diabetes Care 1997;20:1576-81.
6. Mogensen CE. Renoprotective role of ACE inhibitors in diabetes nephropathy. Br Heart J 1994;72:S38-45.
7. Lewis EJ, Hunsicker LG, Bain KP, Rohde RD. The Collaborative Study Group. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N Engl J Med 1993;329:1456-62.
8. American Diabetes Association. Treatment of hypertension in diabetes (consensus statement). Diabetes Care 1993;16:1394-401.
9. Barkis GL, Williams M, Dworkin L, et al. Preserving renal function in adults with hypertension and diabetes: a consensus approach. Am J Kidney Dis 2000;36:646-61.
10. Mainous AG, III, Gill J. Testing for diabetic nephropathy: evidence from a privately insured population. Fam Med. In press.
11. Kraft SK, Lazaridis EN, Qiu C, Clark CM, Marrero DG. Screening and treatment of diabetic nephropathy by primary care physicians. J Gen Intern Med 1999;14:88-97.
12. Gall MA, Borch-Johnson K, Hougaard P, Nielsen FS, Parving HH. Albuminuria and poor glycaemic control predict mortality in NIDDM. Diabetes 1995;44:1303-09.
13. Piehlmeier W, Renner R, Schramm W, et al. Screening of diabetic patients for microalbuminuria in primary care: the PROSIT-project. Exp Clin Endocrinol Diabetes 1999;107:244-51.
14. Ruffin MT, Gorenflo DW, Woodman B. Predictors of screening for breast, cervical, colorectal, and prostatic cancer among community-based primary care practices. J Am Board Fam Pract 2000;13:1-10.
15. O’Driscoll G, Green D, Maiorana A, Stanton K, Colreavy F, Taylor R. Improvement in endothelial function by angiotensin-converting enzyme inhibition in non-insulin-dependent diabetes mellitus. J Am Coll Cardiol 1999;33:506-11.
16. Golan L, Birkmeyer JD, Welch G. The cost-effectiveness of treating all patients with type 2 diabetes with angiotensin-converting enzyme inhibitors. Ann Intern Med 1999;131:660-67.
STUDY DESIGN: This was a retrospective cross-sectional study.
POPULATION: We included a total of 278 adult patients with type 2 diabetes seen during 1998 and 1999 at the family medicine practices of the Medical University of South Carolina.
OUTCOMES MEASURED: The outcomes were microalbuminuria testing during either 1998 or 1999 and the initiation of medication if the screening test result was positive.
RESULTS: We found that patients who could derive the greatest benefit from testing (ie, those without preexisting proteinuria or who were not receiving an angiotensin-blocking drug) were no more likely to be screened for microalbuminuria than those with existing proteinuria (16% vs 18%, P=.84) or those who were already being treated with an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (16% vs 16%, P=.83). Also, when the microalbuminuria test result was positive, only 40% of the patients were placed on angiotensin-blocking drugs.
CONCLUSIONS: Physician use of microalbuminuria screening does not follow established guidelines. The test appears to be used for many patients who might not need to be screened, and it is not always used for patients who should be screened. Consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes.
Nephropathy is one of the most common long-term side effects of diabetes mellitus and accounts for the largest percentage of patients requiring chronic renal dialysis in the United States and Europe.1,2 The high prevalence of type 2 diabetes among adults in the United States and the high rate of nephropathy in these individuals pose a great economic burden to the health care system.
Several studies have noted that angiotensin-converting enzyme inhibitors (ACEIs) can delay the progression of renal impairment in patients with type 2 diabetes.3-7 Patients with diabetic nephropathy generally progress from a stage of normal renal function to microalbuminuria, gross proteinuria, and then renal dysfunction.1 ACEIs appear to delay or prevent the progression from microalbuminuria to proteinuria. Although there are no controlled trials that show microalbuminuria screening as effective at reducing proteinuria, expert panels of the American Diabetes Association8 and National Kidney Foundation9 have recommended that patients with type 2 diabetes receive annual screening for microalbuminuria, and if it is detected on 2 of 3 occasions, these patients should be placed on an ACEI or an angiotensin receptor blocker (ARB) for renal protection.
Initial evaluation of data from primary care practices, however, reveals that screening for microalbuminuria is not optimal.10,11 One reason microalbuminuria screening may happen less often than expected could be that many patients with diabetes mellitus are already being treated with an ACEI or ARB for hypertension, congestive heart failure, or other reasons. Some physicians also might employ an ACEI or ARB prophylactically, starting treatment before recognizing microalbuminuria. Given that these patients are already being treated with an ACEI or ARB, clinicians may not recognize any usefulness in performing a microalbuminuria test.
The purpose of our study was to examine what patient factors are associated with screening for microalbuminuria in patients with type 2 diabetes mellitus. Specifically, we examined how often patients who were not screened were already being treated with an ACEI or ARB. Also, we hoped to characterize the populations being screened more fully to determine if certain patient and disease characteristics were associated with the likelihood of a screening test being performed. A better awareness of these characteristics will help in targeting specific patient groups and changing physician behavior.
Methods
Sample
Our sample was drawn from the primary care practice in the department of family medicine at the Medical University of South Carolina (MUSC) in 1998 and 1999. The department provided care for approximately 18,000 patients who made 42,000 and 48,000 patient visits in 1998 and 1999, respectively, at 2 clinical sites. These 2 sites serve a diverse population of patients in downtown Charleston and a nearby suburban area, which in 1998 had a payer mix distribution that was 26% Medicaid, 27% Medicare, 37% commercially insured, and 10% self-pay.
We identified patients with diabetes at the 2 clinical sites from a search of the problem list in an electronic medical record database that has been used in the department of family medicine since 1992 Table 1. All patients aged between 18 years and 65 years in 2000 and who had an appointment scheduled in 1998 or 1999 were included. The charts that were initially selected for review had diabetes mellitus listed as a problem; after the chart review, we excluded 18 patients from the study because they were using insulin or had not been seen in the practices since 1995, even though they had scheduled an appointment in 1998 or 1999. This left a final sample size of 278.
Data Collection and Variables
Two medical students performed the chart reviews and recorded the following variables when available: age, weight, sex, serum creatinine level, hemoglobin A1C (Hb A1C) level, proteinuria on urinalysis testing, blood pressure, total serum cholesterol, and whether a microalbuminuria test was recommended and, if performed, the results. Race was not included because the patient charts do not consistently note the patient’s race. Also, because care is often shared between attending and resident physicians, we did not include the physician training level as a variable in our analysis.
To determine whether patients were on ACEI or ARB therapy, we searched the electronic medical record database for all medications in the previous 5 years. The medical record used during this period required all prescriptions to be entered before a printed version could be generated, so we could determine if a drug had been used in the past. Although this system overlooked prescriptions that might be called in to a pharmacy and not documented in the record, it captured every prescription written by a physician in the practice. When an ACEI or ARB was used, we examined whether the medication had been started before screening was indicated or after a microalbuminuria test was performed.
We searched the laboratory section of the electronic medical record and also the hospital patient database to determine if the hospital laboratory had performed the test. Searching the hospital database would indicate if the test was performed by any other clinician (eg, an endocrinologist) or in another setting (eg, inpatient) in the university medical center. Whether a microalbuminuria test was recommended was recorded, with the returned value (if available) and the date the test was recommended. We considered values greater than 20 mg per L positive for microalbuminuria. Protein-uria tests were considered positive if they returned a 1+ protein or greater result. We also recorded whether the subject was on an ACEI or ARB therapy, and if so at what date it had been prescribed.
To minimize inter-rater variability, the 2 medical students each reviewed a pilot sample of the same 20 charts. Data were compared and differences between the auditors were reviewed to standardize definitions of data elements. After standardization, sets of 10 different charts were selected, and the process was repeated until the data from 40 consecutive charts were recorded identically by both students.
Analysis
When comparing mean values, we performed a Student t test to determine statistical significance. A chi-square test was done to determine statistical significance when comparing proportions. A P value of <.05 was determined to be statistically significant.
Results
Of the 278 eligible patients, 44 (16%) had a urinalysis with 1+ or greater protein result at baseline; 18 (41%) of these were already taking an ACEI or ARB drug. In patients without previous evidence of proteinuria, 51 (18%) patients were using ACEI or ARB therapy. This left 183 patients (66%) who had no evidence of renal disease and who were not using ACEI or ARB therapy and therefore were the prime candidates for microalbuminuria screening Figure 1.
When we examined the demographics and clinical variables of these 3 groups, we found that patients with proteinuria or who were already using drug treatment were older and had higher systolic and diastolic blood pressures than those who were not. Unexpectedly, we also found that patients with existing proteinuria had lower Hb A1C levels than patients in the other 2 categories.
Of these prime candidates for screening, only 31 (17%) received at least 1 microalbuminuria test between 1995 and 1999. The rate of screening in this group was no different from those who were taking an ACEI or ARB drug (16%, P=.83) or already had gross proteinuria (18%, P=.84).
When we examined the patients who were most likely to benefit from screening and looked at demographic or clinical factors that might influence whether a screening test was performed, we found that patients who received microalbuminuria testing were very similar to those who did not. The only difference we found was that patients who received screening had lower systolic blood pressures than those who were not screened. Weight, age, Hb A1C levels, and cholesterol levels were not predictors of being screened for microalbuminuria Table 2.
Because of the low rates of microalbuminuria screening for patients who were eligible and the relatively frequent use of screening in patients who already had evidence of gross proteinuria, we were interested in what clinicians did when a microalbuminuria test result was positive. In the group without evidence of proteinuria and not using ACEI or ARB therapy, 10 of the 31 patients who received screening for microalbuminuria tested positive. However, only 4 (40%) were placed on ACE inhibitor or ARB therapy.
Discussion
Our data suggest that several problems exist in the use and interpretation of microalbuminuria testing in the primary care setting. First, microalbuminuria testing is being performed on only 1 of 5 adult patients with type 2 diabetes. Second, in this practice, testing is not targeted to the patients who are most likely to benefit from the results. Rather, the tests seemed to be used indiscriminately. Finally, even when patients are screened and found to have microalbuminuria, only a small percentage were started on appropriate therapy. At least in this patient population, it appears that ACEI or ARB therapy is reserved for patients with higher blood pressures rather than used for renal protection.
The observation that patients with existing proteinuria or who were on ACEI or ARB therapy were screened just as often as those who were prime candidates for screening contradicts our initial hypothesis. We had assumed that clinicians would not screen patients who were on ACEI or ARB therapy, reducing the overall screening rate. Apparently, this is not the case. At least in this practice, a low screening rate is not due to selective screening.
The lack of optimal use of microalbuminuria testing and the failure to respond appropriately to positive test results suggests that current recommendations have not been embraced by physicians. Also, the complexity of carrying out these recommendations may make it difficult to integrate this screening into routine practice. If the current evidence on ACEI and ARB therapy for the prevention of renal dysfunction is to be translated into practice, either greater emphasis needs to be placed on microalbuminuria screening or more efficient ways to provide renal protection for patients with diabetes should be considered. Other studies have found that between 17% and 30% of patients with type 2 diabetes have microalbuminuria.1,12,13 Although primary care physicians report that they provide microalbuminuria screening to a large percentage of their patients with diabetes, in fact only a small percentage of those who should be screened actually are screened.10 Suboptimal screening rates for important conditions seen in primary care are not unique for microalbuminuria. Other studies have documented comparable low screening rates for a wide variety of cancers.14 Since physicians do not screen reliably for potentially fatal diseases with screening modalities that have been available for decades, it is unlikely that their behavior is likely to improve when asked to screen for microalbuminuria.
Also, recent evidence that ACEI therapy may improve endothelial function in patients with type 2 diabetes suggests that even patients without microalbuminuria may benefit from routine ACEI therapy.15 Other studies suggest that routine use of ACEIs in middle-aged patients with type 2 diabetes may provide substantial benefits at only modest costs compared with a screening strategy.16 These data suggest that a more effective strategy would be to advise that all patients with type 2 diabetes start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while assuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. However, using this strategy, patients who may not have proteinuria will have to take the medication for a prolonged period, pay for it, and run the risks for any complications associated with using the drug.
Limitations
Our study has several limitations. Only 1 practice was examined, and it was part of a residency training practice. This means that less-experienced clinicians were providing care that could reduce the overall rate of screening. However, the rate of screening observed in this study was very similar to rates found in the practices of clinicians with more experience,11,12 suggesting that the lack of experience of resident physicians may be balanced by the oversight provided by faculty preceptors.
Another limitation is that it was not possible to account for microalbuminuria screening completed outside the MUSC medical center. Patients who split their care among several providers could have had testing performed in other health care facilities. However, since more than 95% of the referrals from the MUSC Family Medicine Center stay within the university health care system, it is doubtful that many patients would have received testing outside the search capabilities of the hospital laboratory database.
Finally, the study was limited in its power to detect small differences between the groups. We originally conceived our project as an exploratory study to determine how many patients were already taking ACEIs and the potential effect of this on overall screening rates for microalbuminuria. Without any reference for the percentage of patients who were taking ACEIs, we could not perform an ad hoc power analysis. However, a post hoc analysis shows that for a sample in which the groups are matched in a 1-to-3 ratio (approximating the proportion of the 51 patients in our sample taking ACEIs and the 183 not taking these drugs) and given the study sample size, our study had a power of 80% to detect a difference in screening rates between 20% in the baseline group and 5% in the ACE or ARB groups. The actual difference seen in our study was much smaller, which increases the possibility of a type II error.
Conclusions
Because physician use of microalbuminuria screening does not follow established guidelines, consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes. One proposed strategy would advise all patients with type 2 diabetes to start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while ensuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. It is unknown, however, whether patients would accept universal treatment rather than periodic screening. This is an important question that should be addressed before any population-based strategies are adopted.
STUDY DESIGN: This was a retrospective cross-sectional study.
POPULATION: We included a total of 278 adult patients with type 2 diabetes seen during 1998 and 1999 at the family medicine practices of the Medical University of South Carolina.
OUTCOMES MEASURED: The outcomes were microalbuminuria testing during either 1998 or 1999 and the initiation of medication if the screening test result was positive.
RESULTS: We found that patients who could derive the greatest benefit from testing (ie, those without preexisting proteinuria or who were not receiving an angiotensin-blocking drug) were no more likely to be screened for microalbuminuria than those with existing proteinuria (16% vs 18%, P=.84) or those who were already being treated with an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker (16% vs 16%, P=.83). Also, when the microalbuminuria test result was positive, only 40% of the patients were placed on angiotensin-blocking drugs.
CONCLUSIONS: Physician use of microalbuminuria screening does not follow established guidelines. The test appears to be used for many patients who might not need to be screened, and it is not always used for patients who should be screened. Consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes.
Nephropathy is one of the most common long-term side effects of diabetes mellitus and accounts for the largest percentage of patients requiring chronic renal dialysis in the United States and Europe.1,2 The high prevalence of type 2 diabetes among adults in the United States and the high rate of nephropathy in these individuals pose a great economic burden to the health care system.
Several studies have noted that angiotensin-converting enzyme inhibitors (ACEIs) can delay the progression of renal impairment in patients with type 2 diabetes.3-7 Patients with diabetic nephropathy generally progress from a stage of normal renal function to microalbuminuria, gross proteinuria, and then renal dysfunction.1 ACEIs appear to delay or prevent the progression from microalbuminuria to proteinuria. Although there are no controlled trials that show microalbuminuria screening as effective at reducing proteinuria, expert panels of the American Diabetes Association8 and National Kidney Foundation9 have recommended that patients with type 2 diabetes receive annual screening for microalbuminuria, and if it is detected on 2 of 3 occasions, these patients should be placed on an ACEI or an angiotensin receptor blocker (ARB) for renal protection.
Initial evaluation of data from primary care practices, however, reveals that screening for microalbuminuria is not optimal.10,11 One reason microalbuminuria screening may happen less often than expected could be that many patients with diabetes mellitus are already being treated with an ACEI or ARB for hypertension, congestive heart failure, or other reasons. Some physicians also might employ an ACEI or ARB prophylactically, starting treatment before recognizing microalbuminuria. Given that these patients are already being treated with an ACEI or ARB, clinicians may not recognize any usefulness in performing a microalbuminuria test.
The purpose of our study was to examine what patient factors are associated with screening for microalbuminuria in patients with type 2 diabetes mellitus. Specifically, we examined how often patients who were not screened were already being treated with an ACEI or ARB. Also, we hoped to characterize the populations being screened more fully to determine if certain patient and disease characteristics were associated with the likelihood of a screening test being performed. A better awareness of these characteristics will help in targeting specific patient groups and changing physician behavior.
Methods
Sample
Our sample was drawn from the primary care practice in the department of family medicine at the Medical University of South Carolina (MUSC) in 1998 and 1999. The department provided care for approximately 18,000 patients who made 42,000 and 48,000 patient visits in 1998 and 1999, respectively, at 2 clinical sites. These 2 sites serve a diverse population of patients in downtown Charleston and a nearby suburban area, which in 1998 had a payer mix distribution that was 26% Medicaid, 27% Medicare, 37% commercially insured, and 10% self-pay.
We identified patients with diabetes at the 2 clinical sites from a search of the problem list in an electronic medical record database that has been used in the department of family medicine since 1992 Table 1. All patients aged between 18 years and 65 years in 2000 and who had an appointment scheduled in 1998 or 1999 were included. The charts that were initially selected for review had diabetes mellitus listed as a problem; after the chart review, we excluded 18 patients from the study because they were using insulin or had not been seen in the practices since 1995, even though they had scheduled an appointment in 1998 or 1999. This left a final sample size of 278.
Data Collection and Variables
Two medical students performed the chart reviews and recorded the following variables when available: age, weight, sex, serum creatinine level, hemoglobin A1C (Hb A1C) level, proteinuria on urinalysis testing, blood pressure, total serum cholesterol, and whether a microalbuminuria test was recommended and, if performed, the results. Race was not included because the patient charts do not consistently note the patient’s race. Also, because care is often shared between attending and resident physicians, we did not include the physician training level as a variable in our analysis.
To determine whether patients were on ACEI or ARB therapy, we searched the electronic medical record database for all medications in the previous 5 years. The medical record used during this period required all prescriptions to be entered before a printed version could be generated, so we could determine if a drug had been used in the past. Although this system overlooked prescriptions that might be called in to a pharmacy and not documented in the record, it captured every prescription written by a physician in the practice. When an ACEI or ARB was used, we examined whether the medication had been started before screening was indicated or after a microalbuminuria test was performed.
We searched the laboratory section of the electronic medical record and also the hospital patient database to determine if the hospital laboratory had performed the test. Searching the hospital database would indicate if the test was performed by any other clinician (eg, an endocrinologist) or in another setting (eg, inpatient) in the university medical center. Whether a microalbuminuria test was recommended was recorded, with the returned value (if available) and the date the test was recommended. We considered values greater than 20 mg per L positive for microalbuminuria. Protein-uria tests were considered positive if they returned a 1+ protein or greater result. We also recorded whether the subject was on an ACEI or ARB therapy, and if so at what date it had been prescribed.
To minimize inter-rater variability, the 2 medical students each reviewed a pilot sample of the same 20 charts. Data were compared and differences between the auditors were reviewed to standardize definitions of data elements. After standardization, sets of 10 different charts were selected, and the process was repeated until the data from 40 consecutive charts were recorded identically by both students.
Analysis
When comparing mean values, we performed a Student t test to determine statistical significance. A chi-square test was done to determine statistical significance when comparing proportions. A P value of <.05 was determined to be statistically significant.
Results
Of the 278 eligible patients, 44 (16%) had a urinalysis with 1+ or greater protein result at baseline; 18 (41%) of these were already taking an ACEI or ARB drug. In patients without previous evidence of proteinuria, 51 (18%) patients were using ACEI or ARB therapy. This left 183 patients (66%) who had no evidence of renal disease and who were not using ACEI or ARB therapy and therefore were the prime candidates for microalbuminuria screening Figure 1.
When we examined the demographics and clinical variables of these 3 groups, we found that patients with proteinuria or who were already using drug treatment were older and had higher systolic and diastolic blood pressures than those who were not. Unexpectedly, we also found that patients with existing proteinuria had lower Hb A1C levels than patients in the other 2 categories.
Of these prime candidates for screening, only 31 (17%) received at least 1 microalbuminuria test between 1995 and 1999. The rate of screening in this group was no different from those who were taking an ACEI or ARB drug (16%, P=.83) or already had gross proteinuria (18%, P=.84).
When we examined the patients who were most likely to benefit from screening and looked at demographic or clinical factors that might influence whether a screening test was performed, we found that patients who received microalbuminuria testing were very similar to those who did not. The only difference we found was that patients who received screening had lower systolic blood pressures than those who were not screened. Weight, age, Hb A1C levels, and cholesterol levels were not predictors of being screened for microalbuminuria Table 2.
Because of the low rates of microalbuminuria screening for patients who were eligible and the relatively frequent use of screening in patients who already had evidence of gross proteinuria, we were interested in what clinicians did when a microalbuminuria test result was positive. In the group without evidence of proteinuria and not using ACEI or ARB therapy, 10 of the 31 patients who received screening for microalbuminuria tested positive. However, only 4 (40%) were placed on ACE inhibitor or ARB therapy.
Discussion
Our data suggest that several problems exist in the use and interpretation of microalbuminuria testing in the primary care setting. First, microalbuminuria testing is being performed on only 1 of 5 adult patients with type 2 diabetes. Second, in this practice, testing is not targeted to the patients who are most likely to benefit from the results. Rather, the tests seemed to be used indiscriminately. Finally, even when patients are screened and found to have microalbuminuria, only a small percentage were started on appropriate therapy. At least in this patient population, it appears that ACEI or ARB therapy is reserved for patients with higher blood pressures rather than used for renal protection.
The observation that patients with existing proteinuria or who were on ACEI or ARB therapy were screened just as often as those who were prime candidates for screening contradicts our initial hypothesis. We had assumed that clinicians would not screen patients who were on ACEI or ARB therapy, reducing the overall screening rate. Apparently, this is not the case. At least in this practice, a low screening rate is not due to selective screening.
The lack of optimal use of microalbuminuria testing and the failure to respond appropriately to positive test results suggests that current recommendations have not been embraced by physicians. Also, the complexity of carrying out these recommendations may make it difficult to integrate this screening into routine practice. If the current evidence on ACEI and ARB therapy for the prevention of renal dysfunction is to be translated into practice, either greater emphasis needs to be placed on microalbuminuria screening or more efficient ways to provide renal protection for patients with diabetes should be considered. Other studies have found that between 17% and 30% of patients with type 2 diabetes have microalbuminuria.1,12,13 Although primary care physicians report that they provide microalbuminuria screening to a large percentage of their patients with diabetes, in fact only a small percentage of those who should be screened actually are screened.10 Suboptimal screening rates for important conditions seen in primary care are not unique for microalbuminuria. Other studies have documented comparable low screening rates for a wide variety of cancers.14 Since physicians do not screen reliably for potentially fatal diseases with screening modalities that have been available for decades, it is unlikely that their behavior is likely to improve when asked to screen for microalbuminuria.
Also, recent evidence that ACEI therapy may improve endothelial function in patients with type 2 diabetes suggests that even patients without microalbuminuria may benefit from routine ACEI therapy.15 Other studies suggest that routine use of ACEIs in middle-aged patients with type 2 diabetes may provide substantial benefits at only modest costs compared with a screening strategy.16 These data suggest that a more effective strategy would be to advise that all patients with type 2 diabetes start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while assuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. However, using this strategy, patients who may not have proteinuria will have to take the medication for a prolonged period, pay for it, and run the risks for any complications associated with using the drug.
Limitations
Our study has several limitations. Only 1 practice was examined, and it was part of a residency training practice. This means that less-experienced clinicians were providing care that could reduce the overall rate of screening. However, the rate of screening observed in this study was very similar to rates found in the practices of clinicians with more experience,11,12 suggesting that the lack of experience of resident physicians may be balanced by the oversight provided by faculty preceptors.
Another limitation is that it was not possible to account for microalbuminuria screening completed outside the MUSC medical center. Patients who split their care among several providers could have had testing performed in other health care facilities. However, since more than 95% of the referrals from the MUSC Family Medicine Center stay within the university health care system, it is doubtful that many patients would have received testing outside the search capabilities of the hospital laboratory database.
Finally, the study was limited in its power to detect small differences between the groups. We originally conceived our project as an exploratory study to determine how many patients were already taking ACEIs and the potential effect of this on overall screening rates for microalbuminuria. Without any reference for the percentage of patients who were taking ACEIs, we could not perform an ad hoc power analysis. However, a post hoc analysis shows that for a sample in which the groups are matched in a 1-to-3 ratio (approximating the proportion of the 51 patients in our sample taking ACEIs and the 183 not taking these drugs) and given the study sample size, our study had a power of 80% to detect a difference in screening rates between 20% in the baseline group and 5% in the ACE or ARB groups. The actual difference seen in our study was much smaller, which increases the possibility of a type II error.
Conclusions
Because physician use of microalbuminuria screening does not follow established guidelines, consideration should be given to other strategies to prevent nephropathy in persons with type 2 diabetes. One proposed strategy would advise all patients with type 2 diabetes to start ACEI or ARB therapy along with their medications for diabetes. This strategy would obviate the need for microalbuminuria screening, while ensuring that patients receive any additional benefits of ACEI or ARB therapy unrelated to renal protection. It is unknown, however, whether patients would accept universal treatment rather than periodic screening. This is an important question that should be addressed before any population-based strategies are adopted.
1. McKenna K, Thompson C. Microalbuminuria: a marker to increased renal and cardiovascular risk in diabetes mellitus. Scottish Med J 1997;42:99-104.
2. American Diabetes Association. Standards of medical care for patients with diabetes mellitus (position statement). Diabetes Care 2000;23(suppl):S32—42.
3. Vibreti G, Mogensen CE, Groop LC, Pauls JF. Effect of captopril on progression to clinical proteinuria in patients with insulin-dependent diabetes mellitus and microalbuminuria. JAMA 1994;271:275-79.
4. Ravid M, Brosh D, Levi Z, et al. Use of enalapril to attenuate decline in renal function in normotensive, normoalbuminuric patients with type II diabetes mellitus: a randomized, controlled trial. Ann Intern Med 1998;128:982-88.
5. Ahmad J, Siddiqui MA, Ahmad H. Effective postponement of diabetic nephropathy with enalapril in type II diabetes patients with microalbuminuria. Diabetes Care 1997;20:1576-81.
6. Mogensen CE. Renoprotective role of ACE inhibitors in diabetes nephropathy. Br Heart J 1994;72:S38-45.
7. Lewis EJ, Hunsicker LG, Bain KP, Rohde RD. The Collaborative Study Group. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N Engl J Med 1993;329:1456-62.
8. American Diabetes Association. Treatment of hypertension in diabetes (consensus statement). Diabetes Care 1993;16:1394-401.
9. Barkis GL, Williams M, Dworkin L, et al. Preserving renal function in adults with hypertension and diabetes: a consensus approach. Am J Kidney Dis 2000;36:646-61.
10. Mainous AG, III, Gill J. Testing for diabetic nephropathy: evidence from a privately insured population. Fam Med. In press.
11. Kraft SK, Lazaridis EN, Qiu C, Clark CM, Marrero DG. Screening and treatment of diabetic nephropathy by primary care physicians. J Gen Intern Med 1999;14:88-97.
12. Gall MA, Borch-Johnson K, Hougaard P, Nielsen FS, Parving HH. Albuminuria and poor glycaemic control predict mortality in NIDDM. Diabetes 1995;44:1303-09.
13. Piehlmeier W, Renner R, Schramm W, et al. Screening of diabetic patients for microalbuminuria in primary care: the PROSIT-project. Exp Clin Endocrinol Diabetes 1999;107:244-51.
14. Ruffin MT, Gorenflo DW, Woodman B. Predictors of screening for breast, cervical, colorectal, and prostatic cancer among community-based primary care practices. J Am Board Fam Pract 2000;13:1-10.
15. O’Driscoll G, Green D, Maiorana A, Stanton K, Colreavy F, Taylor R. Improvement in endothelial function by angiotensin-converting enzyme inhibition in non-insulin-dependent diabetes mellitus. J Am Coll Cardiol 1999;33:506-11.
16. Golan L, Birkmeyer JD, Welch G. The cost-effectiveness of treating all patients with type 2 diabetes with angiotensin-converting enzyme inhibitors. Ann Intern Med 1999;131:660-67.
1. McKenna K, Thompson C. Microalbuminuria: a marker to increased renal and cardiovascular risk in diabetes mellitus. Scottish Med J 1997;42:99-104.
2. American Diabetes Association. Standards of medical care for patients with diabetes mellitus (position statement). Diabetes Care 2000;23(suppl):S32—42.
3. Vibreti G, Mogensen CE, Groop LC, Pauls JF. Effect of captopril on progression to clinical proteinuria in patients with insulin-dependent diabetes mellitus and microalbuminuria. JAMA 1994;271:275-79.
4. Ravid M, Brosh D, Levi Z, et al. Use of enalapril to attenuate decline in renal function in normotensive, normoalbuminuric patients with type II diabetes mellitus: a randomized, controlled trial. Ann Intern Med 1998;128:982-88.
5. Ahmad J, Siddiqui MA, Ahmad H. Effective postponement of diabetic nephropathy with enalapril in type II diabetes patients with microalbuminuria. Diabetes Care 1997;20:1576-81.
6. Mogensen CE. Renoprotective role of ACE inhibitors in diabetes nephropathy. Br Heart J 1994;72:S38-45.
7. Lewis EJ, Hunsicker LG, Bain KP, Rohde RD. The Collaborative Study Group. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N Engl J Med 1993;329:1456-62.
8. American Diabetes Association. Treatment of hypertension in diabetes (consensus statement). Diabetes Care 1993;16:1394-401.
9. Barkis GL, Williams M, Dworkin L, et al. Preserving renal function in adults with hypertension and diabetes: a consensus approach. Am J Kidney Dis 2000;36:646-61.
10. Mainous AG, III, Gill J. Testing for diabetic nephropathy: evidence from a privately insured population. Fam Med. In press.
11. Kraft SK, Lazaridis EN, Qiu C, Clark CM, Marrero DG. Screening and treatment of diabetic nephropathy by primary care physicians. J Gen Intern Med 1999;14:88-97.
12. Gall MA, Borch-Johnson K, Hougaard P, Nielsen FS, Parving HH. Albuminuria and poor glycaemic control predict mortality in NIDDM. Diabetes 1995;44:1303-09.
13. Piehlmeier W, Renner R, Schramm W, et al. Screening of diabetic patients for microalbuminuria in primary care: the PROSIT-project. Exp Clin Endocrinol Diabetes 1999;107:244-51.
14. Ruffin MT, Gorenflo DW, Woodman B. Predictors of screening for breast, cervical, colorectal, and prostatic cancer among community-based primary care practices. J Am Board Fam Pract 2000;13:1-10.
15. O’Driscoll G, Green D, Maiorana A, Stanton K, Colreavy F, Taylor R. Improvement in endothelial function by angiotensin-converting enzyme inhibition in non-insulin-dependent diabetes mellitus. J Am Coll Cardiol 1999;33:506-11.
16. Golan L, Birkmeyer JD, Welch G. The cost-effectiveness of treating all patients with type 2 diabetes with angiotensin-converting enzyme inhibitors. Ann Intern Med 1999;131:660-67.
Does acute bronchitis really exist?
METHODS: We performed a retrospective chart audit on 135 patients who had been given a diagnosis of acute bronchitis and a random sample of 409 patients with URIs over a 2.5-year period. Patient and provider characteristics, patient symptoms, and physical findings were compared with bivariate analyses and then entered into a logistic regression model.
RESULTS: In bivariate analyses, a number of demographic variables, symptoms, and signs were associated with acute bronchitis. Multivariate analysis showed that the strongest independent predictors of acute bronchitis were cough (adjusted odds ratio [AOR]=21.12; 95% confidence interval [CI], 6.01-74.26), and wheezing on examination (AOR=12.16; 95% CI, 5.39-27.42). Nausea was the strongest independent predictor that the diagnosis would not be acute bronchitis (AOR=0.01; 95% CI, 0.01-0.85). However, there was considerable overlap between the 2 conditions, and the logistic model explained only 37% of the variation between the diagnoses.
CONCLUSIONS: We hypothesize that sinusitis, URI, and acute bronchitis are all variations of the same clinical condition (acute respiratory infection) and should be conceptualized as a single clinical entity, with primary symptoms related to different anatomic areas rather than as different conditions.
Acute bronchitis and upper respiratory infections (URIs) represent 2 of the most common diagnoses made by primary care physicians.1,2 It is frequently difficult for clinicians to differentiate between these conditions, since a considerable amount of overlap exists. This confusion is similar to our previous findings that URIs and sinusitis are very similar in their clinical presentation.3
As we observed with sinusitis, it is possible that the diagnosis of acute bronchitis is made primarily to justify treatment decisions. A study of children suggests that the diagnosis of bronchitis may depend on a physician’s desire to justify antibiotic treatment.4 Although antibiotics are of only minimal benefit at best to bronchitis patients,5-7 data from a variety of studies demonstrate that physicians prescribe antibiotics for bronchitis at much higher rates than for URIs.8,9
Four previous studies have provided some indication of clinicians’ opinions about what signs and symptoms constitute acute bronchitis. However, those studies reveal divergent opinions among primary care physicians. For example, in a survey of family physicians concerning what criteria was used to make the diagnosis of acute bronchitis, Oeffinger and colleagues10 found that 58% of physicians made the diagnosis of acute bronchitis only if the patient had a productive cough; 39%, however, stated that whether the cough was productive did not influence their diagnosis. A similar survey of physicians in the Netherlands suggested that clinicians did not rely on any specific sign or symptom but instead relied on the total number of symptoms to define acute bronchitis.11 This implies that the conditions may not be different and that acute bronchitis may really be a “bad cold.” Our work suggests that most physicians diagnosed simulated cases as acute bronchitis and treated them with antibiotics if the sputum had some discoloration,12 a finding that represents inflammation and is not predictive of antibiotic response.13 A previous study that examined clinicians’ reports from patient encounters suggested that patients thought to have acute bronchitis were more likely to have a productive cough, purulent sputum, and abnormal findings on lung examination.14 However, the study had fewer than 50 patients (29 with bronchitis and 19 with upper respiratory tract infection) and was completed before much of the information on the lack of benefit from antibiotics was reported.
The purpose of our study was to explore whether any clinical signs and symptoms predict the diagnosis of acute bronchitis rather than URI. We hypothesized that these 2 conditions would have few clinical differences and that the patients with acute bronchitis would differ from those with URI primarily on the basis of treatments rendered rather than clinical presentation. This would support the hypothesis that acute bronchitis and the common cold constitute the same clinical condition, differing only in the severity of cough and the desire for a condition amenable to treatment.
Methods
We selected subjects for this study from the population of patients who presented for care at the Department of Family Medicine at the Medical University of South Carolina. Two clinical sites for the department serve approximately 48,000 ambulatory patient encounters each year. The Department of Family Medicine uses family practice residents, physician faculty, nurse practitioners, and physician assistants to provide patient care. The design of the study was a case-control model with acute bronchitis patients representing cases and URI patients serving as control subjects.
To identify patients with URIs and acute bronchitis, we used International Classification of Diseases-ninth revision-Clinical Modification (ICD-9-CM) codes for acute bronchitis and URI that were entered with the diagnosis recorded in the electronic medical record. A search for all patients with a problem code 466.0 for acute bronchitis between June 1, 1996, and December 31, 1998, found 165 patient records. Using a similar approach and the ICD-9 code 465.9 for upper respiratory infection, we identified 526 patients with a diagnosis of URI in the same time period. We selected a random sample of 495 records to create a 3:1 match with the bronchitis cases. Patient visits that contained a secondary diagnosis of otitis media, sinusitis, asthma, chronic obstructive pulmonary disease, congestive heart failure, or pneumonia were excluded. The remaining number of total records was 544 (409 URI and 135 acute bronchitis).
Two medical students conducted a detailed chart review of these records. Information on demographics, symptoms, and physical findings were recorded, as well as the provider’s level of training for each record reviewed. To assess the interrater reliability of the reviewers, a random sample of 54 charts (10% of the total sample) was abstracted by both reviewers. Interrater agreement was excellent, ranging from 93% to 100%, depending on the variable.
Information was entered into Epi Info, version 6 (Centers for Disease Control, Atlanta, Ga), a standard epidemiologic database. Data were analyzed with bivariate comparisons of possible predictors. From the initial analysis, the statistically significant variables most often associated with either URI or acute bronchitis were used to perform a multiple regression to determine the independent predictive value of these variables. Regression analysis was performed using True Epistat software (Epistat Services, Richardson, Tex). Because of the large number of variables used in the study (4 demographic, 11 symptom, and 9 physical finding variables), we used the Bonferroni adjustment for multiple comparisons and adjusted the P value for 24 multiple comparisons to set a value of 0.002 (0.05 of 24 possible comparisons) as the indicator of statistical significance.14
Results
Table 1 shows the demographics and recent visit history for patients based on their diagnosis. Acute bronchitis patients were slightly older than those with URIs and were more likely to be smokers. Acute bronchitis patients also were more likely to have made previous visits for either URI or acute bronchitis within 3 months of the diagnosis. When we examined the training level of physicians who saw these patients, we found that attending physicians were more likely to diagnose acute bronchitis than were residents. Of the patients seen by attending physicians, 33% were were considered to have acute bronchitis, compared with 19% of the patients seen by residents (P <.001). Examining the reported symptoms of those patients with acute bronchitis and URI revealed that several symptoms were more often associated with each diagnosis Table 2. Cough was present in the majority of both conditions but occurred more often when acute bronchitis was the diagnosis. Chest pain, shortness of breath, and a history of wheezing also were associated with acute bronchitis, although each was present in only 8% to 12% of the cases. In contrast, symptoms associated with URI included runny nose and sore throat, but neither alone was seen in a majority of patients considered to have URIs.
Comparisons of physical findings provided similar results Table 3. A red throat, red nose, or cervical lymph nodes were all associated with the diagnosis of URI. The only physical finding associated with bronchitis was the presence of wheezing, found in approximately 30% of patients with acute bronchitis.
Since very few of the symptoms and physical findings associated with the diagnosis of acute bronchitis or URI appeared in the majority of patients, it was thought that clinicians probably rely on a constellation of these signs and symptoms to make the diagnosis of bronchitis. Also, because each of the symptoms and signs might not be independent predictors of URI as opposed to bronchitis, we entered symptoms and physical findings associated with each diagnosis into a multivariate logistic regression model along with possible demographic predictors. Table 4 shows the results of this logistic regression. All other things being equal, cough and wheezing were the strongest independent predictors of acute bronchitis, while nausea or a red or runny nose were the strongest predictors of URI. Also of note is that adults and patients who had been seen in the previous 3 months with bronchitis were more likely to receive a diagnosis of acute bronchitis independent of their other signs and symptoms.
The logistic regression model had an R2 of 0.37, indicating that all the signs and symptom variables that we considered explained only approximately a third of the differences between these 2 conditions. To illustrate the limitations of using just these 2 variables to differentiate acute bronchitis and URI, if the presence of a cough was used as the sole determinant of the diagnosis, the sensitivity for acute bronchitis would be 98% (132 out of 135), but specificity would be only 29% (120 out of 409). Wheezing on its own had a sensitivity of 30% with a specificity of 97%. Adding in the absence of runny nose or nausea to these other predictors was no better than wheezing alone. Leaving out wheezing and including cough, absence of runny nose, and nausea gave a sensitivity of 82% with a specificity of 65%. Thus, clinical factors alone are insufficient to explain why some patients were given the diagnosis they received.
We also explored whether clinicians used a diagnosis to influence treatment decisions. It appeared that treatments did differ according to the diagnosis. Patients with acute bronchitis were 8 times more likely to receive a bronchodilator than those with a URI (70% vs 8%, P <.001) and were more likely to receive antibiotics (26% vs 4%, P <.001). Decongestants ere prescribed more often when the diagnosis was a URI (33% vs 16%, P <.001).
Discussion
Our study demonstrates that physicians rely on only a few clinical factors to differentiate acute bronchitis from URI. These factors are poor predictors of which patients will receive a diagnosis of acute bronchitis and which URI, since significant overlap exists between these 2 disorders. Despite a lack of clear differences between these conditions, physicians use these diagnoses to make treatment decisions. The low degree of specificity for clinical signs and symptoms in differentiating the 2 conditions implies that physicians use other cues such as the patient’s desire for treatment, personal thresholds for prescribing, and a general gestalt when labeling the condition.
These findings mirror our previous study that shows significant clinical overlap between sinusitis and URI.3 As in our current findings with acute bronchitis, “sinusitis” may simply be an acute respiratory infection predominantly in the head with moderate to high severity. Computed tomography scans of patients with clinical syndromes diagnosed as “colds” show sinus inflammation in 47% of patients.16 The condition frequently labeled as “sinusitis” may constitute a desire of physicians to use antibiotics to treat symptoms caused by this virally induced inflammation, even though scant evidence exists that antibiotics improve outcomes even when sinusitis is demonstrated by radiologic techniques. In most clinical situations, a majority of patients, clinicians, and clinical pharmacists appear to assign the diagnosis of sinusitis or believe antibiotics are helpful for any respiratory infection with discolored nasal discharge.12,17,18 However, discolored discharge on its own is a poor predictor of antibiotic response and is a common manifestation of a URI.
The same situation seems to exist with acute bronchitis. Acute bronchitis may be nothing more than an acute respiratory infection that is predominantly in the chest. This would explain why there is such a high degree of overlap between the signs and symptoms of this condition and illnesses labeled as URIs. Except for the observation that albuterol is effective at hastening symptom resolution in patients with productive coughs and wheezes19,20 but ineffective for patients with nonspecific coughs associated with upper airway symptoms,21 there is little value in labeling patients as having acute bronchitis rather than a cold, since these 2 entities are probably manifestations of the same clinical condition.
Taken together, it may be more useful to reconceptualize respiratory infections as a single clinical entity rather than by anatomically specific diagnoses. Patients may differ in the degree of severity of the illness and the anatomic area that produces most of their complaints. Clinically and in our research, we may be better served by representing all viral respiratory conditions as a single clinical diagnosis with severity and anatomic indications. All patients with acute symptoms involving the sinuses, nose, pharynx, and bronchial tree could be labeled as having an “acute respiratory infection,” either “sinus predominant,” “bronchial predominant,” or even “generalized” when involving all areas of the respiratory tract.
Most evidence suggests that the vast majority of all these infections are caused by viruses. This does not imply that patients do not have infections and are not ill. Not only do viruses vary in the degree in which they infect individuals, but patients also differ in their abilities to cope with their cold symptoms. Rather than attempting to eradicate the cause of the cold, the goals of treatment for these disorders should be improving outcomes that matter to patients, such as reducing their symptoms, improving their ability to function at work and at home, and relieving their anxiety. Instead of trying to eradicate bacteria that do not exist in the majority of patients, physicians should focus on treatments proven to alleviate the predominant symptoms and the underlying reasons patients seek care for this self-limited problem. These latter reasons could include loneliness, anxiety about the severity of the condition (for example, in a young child), or frustration that the symptoms are interrupting other important activities. Attention to these important psychosocial events may be even more important than the type of drug written on the prescription pad. Dismissing a patient with these problems as being uninformed, bothersome, or inappropriately seeking care may not be productive in addressing the underlying reasons the patient has consulted a physician. Failing to address these underlying psychosocial issues or prescribing an antibiotic to meet physicians’ desire to help and meet an unstated patient expectation may result in repetitive care-seeking for the same self-limited symptoms,22,23 ultimately benefiting no one and consuming health care resources that could be better invested elsewhere.
This new model of conceptualizing respiratory infections implies that assigning an anatomic-specific diagnosis to the condition is irrelevant beyond being a determinant for which symptomatic care should be the highest priority (eg, decongestants for predominantly sinus symptoms or bronchodilators for wheezing or rhonchi). Also, although acute respiratory infection is a very common reason for visits to primary care physicians, only some patients consult a clinician for their problem. Care-seeking for acute respiratory infection is likely determined by a combination of forces that include severity of the infection (patients with more severe viral illnesses would be more likely to seek care), anatomic localization (infections with more specifically localized symptoms would be more likely to seek care), and personal psychosocial issues (including the perceived importance of existing or possible disruption of daily activities, personal coping skills and resources, and the need for empathy and affirmation from someone else that they are sick). Rather than focusing research in this area on identifying how certain subsets of patients with what is predominantly a viral illness respond to antibiotics that do not treat what they have, future research in acute respiratory infections might be better directed at why some patients seek care or self-medicate inappropriately24 and how clinicians can best serve this population.
Conclusions
Our study shows a great deal of overlap in the signs and symptoms of acute bronchitis and URIs. Very little of the variation in the diagnoses could be explained by clinical factors. These results are similar to earlier work suggesting considerable clinical overlap in sinusitis. This leads us to hypothesize that these conditions all represent the same clinical entity and that a reconceptualization of acute viral respiratory infections as a single problem, rather than anatomically distinct disorders, is warranted.
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19. Hueston WJ. Albuterol delivered by metered-dose inhaler to treat acute bronchitis. J Fam Pract 1994;39:437-40.
20. Hueston WJ. A comparison of albuterol and erythromycin for the treatment of acute bronchitis. J Fam Pract 1991;33:476-80.
21. Littenberg B, Wheeler M, Smith DS. A randomized controlled trial of oral albuterol in acute cough. J Fam Pract 1996;42:49-53.
22. Holmes WF, Macfarlane JT, Macfarlane RM, Lewis S. The influence of antibiotics and other factors on reconsultation for acute lower respiratory tract illness in primary care. Br J Gen Pract 1997;47:815-8.
23. Macfarlane J, Prewett J, Rose D, et al. Prospective case-control study of role of infection in patients who reconsult after initial antibiotic treatment for lower respiratory infection in primary care. BMJ 1997;315:1206-10.
24. McKee MD, Mills L, Mainous AG, III. Antibiotic use for the treatment of upper respiratory infections in a diverse community. J Fam Pract 1999;48:993-6.
METHODS: We performed a retrospective chart audit on 135 patients who had been given a diagnosis of acute bronchitis and a random sample of 409 patients with URIs over a 2.5-year period. Patient and provider characteristics, patient symptoms, and physical findings were compared with bivariate analyses and then entered into a logistic regression model.
RESULTS: In bivariate analyses, a number of demographic variables, symptoms, and signs were associated with acute bronchitis. Multivariate analysis showed that the strongest independent predictors of acute bronchitis were cough (adjusted odds ratio [AOR]=21.12; 95% confidence interval [CI], 6.01-74.26), and wheezing on examination (AOR=12.16; 95% CI, 5.39-27.42). Nausea was the strongest independent predictor that the diagnosis would not be acute bronchitis (AOR=0.01; 95% CI, 0.01-0.85). However, there was considerable overlap between the 2 conditions, and the logistic model explained only 37% of the variation between the diagnoses.
CONCLUSIONS: We hypothesize that sinusitis, URI, and acute bronchitis are all variations of the same clinical condition (acute respiratory infection) and should be conceptualized as a single clinical entity, with primary symptoms related to different anatomic areas rather than as different conditions.
Acute bronchitis and upper respiratory infections (URIs) represent 2 of the most common diagnoses made by primary care physicians.1,2 It is frequently difficult for clinicians to differentiate between these conditions, since a considerable amount of overlap exists. This confusion is similar to our previous findings that URIs and sinusitis are very similar in their clinical presentation.3
As we observed with sinusitis, it is possible that the diagnosis of acute bronchitis is made primarily to justify treatment decisions. A study of children suggests that the diagnosis of bronchitis may depend on a physician’s desire to justify antibiotic treatment.4 Although antibiotics are of only minimal benefit at best to bronchitis patients,5-7 data from a variety of studies demonstrate that physicians prescribe antibiotics for bronchitis at much higher rates than for URIs.8,9
Four previous studies have provided some indication of clinicians’ opinions about what signs and symptoms constitute acute bronchitis. However, those studies reveal divergent opinions among primary care physicians. For example, in a survey of family physicians concerning what criteria was used to make the diagnosis of acute bronchitis, Oeffinger and colleagues10 found that 58% of physicians made the diagnosis of acute bronchitis only if the patient had a productive cough; 39%, however, stated that whether the cough was productive did not influence their diagnosis. A similar survey of physicians in the Netherlands suggested that clinicians did not rely on any specific sign or symptom but instead relied on the total number of symptoms to define acute bronchitis.11 This implies that the conditions may not be different and that acute bronchitis may really be a “bad cold.” Our work suggests that most physicians diagnosed simulated cases as acute bronchitis and treated them with antibiotics if the sputum had some discoloration,12 a finding that represents inflammation and is not predictive of antibiotic response.13 A previous study that examined clinicians’ reports from patient encounters suggested that patients thought to have acute bronchitis were more likely to have a productive cough, purulent sputum, and abnormal findings on lung examination.14 However, the study had fewer than 50 patients (29 with bronchitis and 19 with upper respiratory tract infection) and was completed before much of the information on the lack of benefit from antibiotics was reported.
The purpose of our study was to explore whether any clinical signs and symptoms predict the diagnosis of acute bronchitis rather than URI. We hypothesized that these 2 conditions would have few clinical differences and that the patients with acute bronchitis would differ from those with URI primarily on the basis of treatments rendered rather than clinical presentation. This would support the hypothesis that acute bronchitis and the common cold constitute the same clinical condition, differing only in the severity of cough and the desire for a condition amenable to treatment.
Methods
We selected subjects for this study from the population of patients who presented for care at the Department of Family Medicine at the Medical University of South Carolina. Two clinical sites for the department serve approximately 48,000 ambulatory patient encounters each year. The Department of Family Medicine uses family practice residents, physician faculty, nurse practitioners, and physician assistants to provide patient care. The design of the study was a case-control model with acute bronchitis patients representing cases and URI patients serving as control subjects.
To identify patients with URIs and acute bronchitis, we used International Classification of Diseases-ninth revision-Clinical Modification (ICD-9-CM) codes for acute bronchitis and URI that were entered with the diagnosis recorded in the electronic medical record. A search for all patients with a problem code 466.0 for acute bronchitis between June 1, 1996, and December 31, 1998, found 165 patient records. Using a similar approach and the ICD-9 code 465.9 for upper respiratory infection, we identified 526 patients with a diagnosis of URI in the same time period. We selected a random sample of 495 records to create a 3:1 match with the bronchitis cases. Patient visits that contained a secondary diagnosis of otitis media, sinusitis, asthma, chronic obstructive pulmonary disease, congestive heart failure, or pneumonia were excluded. The remaining number of total records was 544 (409 URI and 135 acute bronchitis).
Two medical students conducted a detailed chart review of these records. Information on demographics, symptoms, and physical findings were recorded, as well as the provider’s level of training for each record reviewed. To assess the interrater reliability of the reviewers, a random sample of 54 charts (10% of the total sample) was abstracted by both reviewers. Interrater agreement was excellent, ranging from 93% to 100%, depending on the variable.
Information was entered into Epi Info, version 6 (Centers for Disease Control, Atlanta, Ga), a standard epidemiologic database. Data were analyzed with bivariate comparisons of possible predictors. From the initial analysis, the statistically significant variables most often associated with either URI or acute bronchitis were used to perform a multiple regression to determine the independent predictive value of these variables. Regression analysis was performed using True Epistat software (Epistat Services, Richardson, Tex). Because of the large number of variables used in the study (4 demographic, 11 symptom, and 9 physical finding variables), we used the Bonferroni adjustment for multiple comparisons and adjusted the P value for 24 multiple comparisons to set a value of 0.002 (0.05 of 24 possible comparisons) as the indicator of statistical significance.14
Results
Table 1 shows the demographics and recent visit history for patients based on their diagnosis. Acute bronchitis patients were slightly older than those with URIs and were more likely to be smokers. Acute bronchitis patients also were more likely to have made previous visits for either URI or acute bronchitis within 3 months of the diagnosis. When we examined the training level of physicians who saw these patients, we found that attending physicians were more likely to diagnose acute bronchitis than were residents. Of the patients seen by attending physicians, 33% were were considered to have acute bronchitis, compared with 19% of the patients seen by residents (P <.001). Examining the reported symptoms of those patients with acute bronchitis and URI revealed that several symptoms were more often associated with each diagnosis Table 2. Cough was present in the majority of both conditions but occurred more often when acute bronchitis was the diagnosis. Chest pain, shortness of breath, and a history of wheezing also were associated with acute bronchitis, although each was present in only 8% to 12% of the cases. In contrast, symptoms associated with URI included runny nose and sore throat, but neither alone was seen in a majority of patients considered to have URIs.
Comparisons of physical findings provided similar results Table 3. A red throat, red nose, or cervical lymph nodes were all associated with the diagnosis of URI. The only physical finding associated with bronchitis was the presence of wheezing, found in approximately 30% of patients with acute bronchitis.
Since very few of the symptoms and physical findings associated with the diagnosis of acute bronchitis or URI appeared in the majority of patients, it was thought that clinicians probably rely on a constellation of these signs and symptoms to make the diagnosis of bronchitis. Also, because each of the symptoms and signs might not be independent predictors of URI as opposed to bronchitis, we entered symptoms and physical findings associated with each diagnosis into a multivariate logistic regression model along with possible demographic predictors. Table 4 shows the results of this logistic regression. All other things being equal, cough and wheezing were the strongest independent predictors of acute bronchitis, while nausea or a red or runny nose were the strongest predictors of URI. Also of note is that adults and patients who had been seen in the previous 3 months with bronchitis were more likely to receive a diagnosis of acute bronchitis independent of their other signs and symptoms.
The logistic regression model had an R2 of 0.37, indicating that all the signs and symptom variables that we considered explained only approximately a third of the differences between these 2 conditions. To illustrate the limitations of using just these 2 variables to differentiate acute bronchitis and URI, if the presence of a cough was used as the sole determinant of the diagnosis, the sensitivity for acute bronchitis would be 98% (132 out of 135), but specificity would be only 29% (120 out of 409). Wheezing on its own had a sensitivity of 30% with a specificity of 97%. Adding in the absence of runny nose or nausea to these other predictors was no better than wheezing alone. Leaving out wheezing and including cough, absence of runny nose, and nausea gave a sensitivity of 82% with a specificity of 65%. Thus, clinical factors alone are insufficient to explain why some patients were given the diagnosis they received.
We also explored whether clinicians used a diagnosis to influence treatment decisions. It appeared that treatments did differ according to the diagnosis. Patients with acute bronchitis were 8 times more likely to receive a bronchodilator than those with a URI (70% vs 8%, P <.001) and were more likely to receive antibiotics (26% vs 4%, P <.001). Decongestants ere prescribed more often when the diagnosis was a URI (33% vs 16%, P <.001).
Discussion
Our study demonstrates that physicians rely on only a few clinical factors to differentiate acute bronchitis from URI. These factors are poor predictors of which patients will receive a diagnosis of acute bronchitis and which URI, since significant overlap exists between these 2 disorders. Despite a lack of clear differences between these conditions, physicians use these diagnoses to make treatment decisions. The low degree of specificity for clinical signs and symptoms in differentiating the 2 conditions implies that physicians use other cues such as the patient’s desire for treatment, personal thresholds for prescribing, and a general gestalt when labeling the condition.
These findings mirror our previous study that shows significant clinical overlap between sinusitis and URI.3 As in our current findings with acute bronchitis, “sinusitis” may simply be an acute respiratory infection predominantly in the head with moderate to high severity. Computed tomography scans of patients with clinical syndromes diagnosed as “colds” show sinus inflammation in 47% of patients.16 The condition frequently labeled as “sinusitis” may constitute a desire of physicians to use antibiotics to treat symptoms caused by this virally induced inflammation, even though scant evidence exists that antibiotics improve outcomes even when sinusitis is demonstrated by radiologic techniques. In most clinical situations, a majority of patients, clinicians, and clinical pharmacists appear to assign the diagnosis of sinusitis or believe antibiotics are helpful for any respiratory infection with discolored nasal discharge.12,17,18 However, discolored discharge on its own is a poor predictor of antibiotic response and is a common manifestation of a URI.
The same situation seems to exist with acute bronchitis. Acute bronchitis may be nothing more than an acute respiratory infection that is predominantly in the chest. This would explain why there is such a high degree of overlap between the signs and symptoms of this condition and illnesses labeled as URIs. Except for the observation that albuterol is effective at hastening symptom resolution in patients with productive coughs and wheezes19,20 but ineffective for patients with nonspecific coughs associated with upper airway symptoms,21 there is little value in labeling patients as having acute bronchitis rather than a cold, since these 2 entities are probably manifestations of the same clinical condition.
Taken together, it may be more useful to reconceptualize respiratory infections as a single clinical entity rather than by anatomically specific diagnoses. Patients may differ in the degree of severity of the illness and the anatomic area that produces most of their complaints. Clinically and in our research, we may be better served by representing all viral respiratory conditions as a single clinical diagnosis with severity and anatomic indications. All patients with acute symptoms involving the sinuses, nose, pharynx, and bronchial tree could be labeled as having an “acute respiratory infection,” either “sinus predominant,” “bronchial predominant,” or even “generalized” when involving all areas of the respiratory tract.
Most evidence suggests that the vast majority of all these infections are caused by viruses. This does not imply that patients do not have infections and are not ill. Not only do viruses vary in the degree in which they infect individuals, but patients also differ in their abilities to cope with their cold symptoms. Rather than attempting to eradicate the cause of the cold, the goals of treatment for these disorders should be improving outcomes that matter to patients, such as reducing their symptoms, improving their ability to function at work and at home, and relieving their anxiety. Instead of trying to eradicate bacteria that do not exist in the majority of patients, physicians should focus on treatments proven to alleviate the predominant symptoms and the underlying reasons patients seek care for this self-limited problem. These latter reasons could include loneliness, anxiety about the severity of the condition (for example, in a young child), or frustration that the symptoms are interrupting other important activities. Attention to these important psychosocial events may be even more important than the type of drug written on the prescription pad. Dismissing a patient with these problems as being uninformed, bothersome, or inappropriately seeking care may not be productive in addressing the underlying reasons the patient has consulted a physician. Failing to address these underlying psychosocial issues or prescribing an antibiotic to meet physicians’ desire to help and meet an unstated patient expectation may result in repetitive care-seeking for the same self-limited symptoms,22,23 ultimately benefiting no one and consuming health care resources that could be better invested elsewhere.
This new model of conceptualizing respiratory infections implies that assigning an anatomic-specific diagnosis to the condition is irrelevant beyond being a determinant for which symptomatic care should be the highest priority (eg, decongestants for predominantly sinus symptoms or bronchodilators for wheezing or rhonchi). Also, although acute respiratory infection is a very common reason for visits to primary care physicians, only some patients consult a clinician for their problem. Care-seeking for acute respiratory infection is likely determined by a combination of forces that include severity of the infection (patients with more severe viral illnesses would be more likely to seek care), anatomic localization (infections with more specifically localized symptoms would be more likely to seek care), and personal psychosocial issues (including the perceived importance of existing or possible disruption of daily activities, personal coping skills and resources, and the need for empathy and affirmation from someone else that they are sick). Rather than focusing research in this area on identifying how certain subsets of patients with what is predominantly a viral illness respond to antibiotics that do not treat what they have, future research in acute respiratory infections might be better directed at why some patients seek care or self-medicate inappropriately24 and how clinicians can best serve this population.
Conclusions
Our study shows a great deal of overlap in the signs and symptoms of acute bronchitis and URIs. Very little of the variation in the diagnoses could be explained by clinical factors. These results are similar to earlier work suggesting considerable clinical overlap in sinusitis. This leads us to hypothesize that these conditions all represent the same clinical entity and that a reconceptualization of acute viral respiratory infections as a single problem, rather than anatomically distinct disorders, is warranted.
METHODS: We performed a retrospective chart audit on 135 patients who had been given a diagnosis of acute bronchitis and a random sample of 409 patients with URIs over a 2.5-year period. Patient and provider characteristics, patient symptoms, and physical findings were compared with bivariate analyses and then entered into a logistic regression model.
RESULTS: In bivariate analyses, a number of demographic variables, symptoms, and signs were associated with acute bronchitis. Multivariate analysis showed that the strongest independent predictors of acute bronchitis were cough (adjusted odds ratio [AOR]=21.12; 95% confidence interval [CI], 6.01-74.26), and wheezing on examination (AOR=12.16; 95% CI, 5.39-27.42). Nausea was the strongest independent predictor that the diagnosis would not be acute bronchitis (AOR=0.01; 95% CI, 0.01-0.85). However, there was considerable overlap between the 2 conditions, and the logistic model explained only 37% of the variation between the diagnoses.
CONCLUSIONS: We hypothesize that sinusitis, URI, and acute bronchitis are all variations of the same clinical condition (acute respiratory infection) and should be conceptualized as a single clinical entity, with primary symptoms related to different anatomic areas rather than as different conditions.
Acute bronchitis and upper respiratory infections (URIs) represent 2 of the most common diagnoses made by primary care physicians.1,2 It is frequently difficult for clinicians to differentiate between these conditions, since a considerable amount of overlap exists. This confusion is similar to our previous findings that URIs and sinusitis are very similar in their clinical presentation.3
As we observed with sinusitis, it is possible that the diagnosis of acute bronchitis is made primarily to justify treatment decisions. A study of children suggests that the diagnosis of bronchitis may depend on a physician’s desire to justify antibiotic treatment.4 Although antibiotics are of only minimal benefit at best to bronchitis patients,5-7 data from a variety of studies demonstrate that physicians prescribe antibiotics for bronchitis at much higher rates than for URIs.8,9
Four previous studies have provided some indication of clinicians’ opinions about what signs and symptoms constitute acute bronchitis. However, those studies reveal divergent opinions among primary care physicians. For example, in a survey of family physicians concerning what criteria was used to make the diagnosis of acute bronchitis, Oeffinger and colleagues10 found that 58% of physicians made the diagnosis of acute bronchitis only if the patient had a productive cough; 39%, however, stated that whether the cough was productive did not influence their diagnosis. A similar survey of physicians in the Netherlands suggested that clinicians did not rely on any specific sign or symptom but instead relied on the total number of symptoms to define acute bronchitis.11 This implies that the conditions may not be different and that acute bronchitis may really be a “bad cold.” Our work suggests that most physicians diagnosed simulated cases as acute bronchitis and treated them with antibiotics if the sputum had some discoloration,12 a finding that represents inflammation and is not predictive of antibiotic response.13 A previous study that examined clinicians’ reports from patient encounters suggested that patients thought to have acute bronchitis were more likely to have a productive cough, purulent sputum, and abnormal findings on lung examination.14 However, the study had fewer than 50 patients (29 with bronchitis and 19 with upper respiratory tract infection) and was completed before much of the information on the lack of benefit from antibiotics was reported.
The purpose of our study was to explore whether any clinical signs and symptoms predict the diagnosis of acute bronchitis rather than URI. We hypothesized that these 2 conditions would have few clinical differences and that the patients with acute bronchitis would differ from those with URI primarily on the basis of treatments rendered rather than clinical presentation. This would support the hypothesis that acute bronchitis and the common cold constitute the same clinical condition, differing only in the severity of cough and the desire for a condition amenable to treatment.
Methods
We selected subjects for this study from the population of patients who presented for care at the Department of Family Medicine at the Medical University of South Carolina. Two clinical sites for the department serve approximately 48,000 ambulatory patient encounters each year. The Department of Family Medicine uses family practice residents, physician faculty, nurse practitioners, and physician assistants to provide patient care. The design of the study was a case-control model with acute bronchitis patients representing cases and URI patients serving as control subjects.
To identify patients with URIs and acute bronchitis, we used International Classification of Diseases-ninth revision-Clinical Modification (ICD-9-CM) codes for acute bronchitis and URI that were entered with the diagnosis recorded in the electronic medical record. A search for all patients with a problem code 466.0 for acute bronchitis between June 1, 1996, and December 31, 1998, found 165 patient records. Using a similar approach and the ICD-9 code 465.9 for upper respiratory infection, we identified 526 patients with a diagnosis of URI in the same time period. We selected a random sample of 495 records to create a 3:1 match with the bronchitis cases. Patient visits that contained a secondary diagnosis of otitis media, sinusitis, asthma, chronic obstructive pulmonary disease, congestive heart failure, or pneumonia were excluded. The remaining number of total records was 544 (409 URI and 135 acute bronchitis).
Two medical students conducted a detailed chart review of these records. Information on demographics, symptoms, and physical findings were recorded, as well as the provider’s level of training for each record reviewed. To assess the interrater reliability of the reviewers, a random sample of 54 charts (10% of the total sample) was abstracted by both reviewers. Interrater agreement was excellent, ranging from 93% to 100%, depending on the variable.
Information was entered into Epi Info, version 6 (Centers for Disease Control, Atlanta, Ga), a standard epidemiologic database. Data were analyzed with bivariate comparisons of possible predictors. From the initial analysis, the statistically significant variables most often associated with either URI or acute bronchitis were used to perform a multiple regression to determine the independent predictive value of these variables. Regression analysis was performed using True Epistat software (Epistat Services, Richardson, Tex). Because of the large number of variables used in the study (4 demographic, 11 symptom, and 9 physical finding variables), we used the Bonferroni adjustment for multiple comparisons and adjusted the P value for 24 multiple comparisons to set a value of 0.002 (0.05 of 24 possible comparisons) as the indicator of statistical significance.14
Results
Table 1 shows the demographics and recent visit history for patients based on their diagnosis. Acute bronchitis patients were slightly older than those with URIs and were more likely to be smokers. Acute bronchitis patients also were more likely to have made previous visits for either URI or acute bronchitis within 3 months of the diagnosis. When we examined the training level of physicians who saw these patients, we found that attending physicians were more likely to diagnose acute bronchitis than were residents. Of the patients seen by attending physicians, 33% were were considered to have acute bronchitis, compared with 19% of the patients seen by residents (P <.001). Examining the reported symptoms of those patients with acute bronchitis and URI revealed that several symptoms were more often associated with each diagnosis Table 2. Cough was present in the majority of both conditions but occurred more often when acute bronchitis was the diagnosis. Chest pain, shortness of breath, and a history of wheezing also were associated with acute bronchitis, although each was present in only 8% to 12% of the cases. In contrast, symptoms associated with URI included runny nose and sore throat, but neither alone was seen in a majority of patients considered to have URIs.
Comparisons of physical findings provided similar results Table 3. A red throat, red nose, or cervical lymph nodes were all associated with the diagnosis of URI. The only physical finding associated with bronchitis was the presence of wheezing, found in approximately 30% of patients with acute bronchitis.
Since very few of the symptoms and physical findings associated with the diagnosis of acute bronchitis or URI appeared in the majority of patients, it was thought that clinicians probably rely on a constellation of these signs and symptoms to make the diagnosis of bronchitis. Also, because each of the symptoms and signs might not be independent predictors of URI as opposed to bronchitis, we entered symptoms and physical findings associated with each diagnosis into a multivariate logistic regression model along with possible demographic predictors. Table 4 shows the results of this logistic regression. All other things being equal, cough and wheezing were the strongest independent predictors of acute bronchitis, while nausea or a red or runny nose were the strongest predictors of URI. Also of note is that adults and patients who had been seen in the previous 3 months with bronchitis were more likely to receive a diagnosis of acute bronchitis independent of their other signs and symptoms.
The logistic regression model had an R2 of 0.37, indicating that all the signs and symptom variables that we considered explained only approximately a third of the differences between these 2 conditions. To illustrate the limitations of using just these 2 variables to differentiate acute bronchitis and URI, if the presence of a cough was used as the sole determinant of the diagnosis, the sensitivity for acute bronchitis would be 98% (132 out of 135), but specificity would be only 29% (120 out of 409). Wheezing on its own had a sensitivity of 30% with a specificity of 97%. Adding in the absence of runny nose or nausea to these other predictors was no better than wheezing alone. Leaving out wheezing and including cough, absence of runny nose, and nausea gave a sensitivity of 82% with a specificity of 65%. Thus, clinical factors alone are insufficient to explain why some patients were given the diagnosis they received.
We also explored whether clinicians used a diagnosis to influence treatment decisions. It appeared that treatments did differ according to the diagnosis. Patients with acute bronchitis were 8 times more likely to receive a bronchodilator than those with a URI (70% vs 8%, P <.001) and were more likely to receive antibiotics (26% vs 4%, P <.001). Decongestants ere prescribed more often when the diagnosis was a URI (33% vs 16%, P <.001).
Discussion
Our study demonstrates that physicians rely on only a few clinical factors to differentiate acute bronchitis from URI. These factors are poor predictors of which patients will receive a diagnosis of acute bronchitis and which URI, since significant overlap exists between these 2 disorders. Despite a lack of clear differences between these conditions, physicians use these diagnoses to make treatment decisions. The low degree of specificity for clinical signs and symptoms in differentiating the 2 conditions implies that physicians use other cues such as the patient’s desire for treatment, personal thresholds for prescribing, and a general gestalt when labeling the condition.
These findings mirror our previous study that shows significant clinical overlap between sinusitis and URI.3 As in our current findings with acute bronchitis, “sinusitis” may simply be an acute respiratory infection predominantly in the head with moderate to high severity. Computed tomography scans of patients with clinical syndromes diagnosed as “colds” show sinus inflammation in 47% of patients.16 The condition frequently labeled as “sinusitis” may constitute a desire of physicians to use antibiotics to treat symptoms caused by this virally induced inflammation, even though scant evidence exists that antibiotics improve outcomes even when sinusitis is demonstrated by radiologic techniques. In most clinical situations, a majority of patients, clinicians, and clinical pharmacists appear to assign the diagnosis of sinusitis or believe antibiotics are helpful for any respiratory infection with discolored nasal discharge.12,17,18 However, discolored discharge on its own is a poor predictor of antibiotic response and is a common manifestation of a URI.
The same situation seems to exist with acute bronchitis. Acute bronchitis may be nothing more than an acute respiratory infection that is predominantly in the chest. This would explain why there is such a high degree of overlap between the signs and symptoms of this condition and illnesses labeled as URIs. Except for the observation that albuterol is effective at hastening symptom resolution in patients with productive coughs and wheezes19,20 but ineffective for patients with nonspecific coughs associated with upper airway symptoms,21 there is little value in labeling patients as having acute bronchitis rather than a cold, since these 2 entities are probably manifestations of the same clinical condition.
Taken together, it may be more useful to reconceptualize respiratory infections as a single clinical entity rather than by anatomically specific diagnoses. Patients may differ in the degree of severity of the illness and the anatomic area that produces most of their complaints. Clinically and in our research, we may be better served by representing all viral respiratory conditions as a single clinical diagnosis with severity and anatomic indications. All patients with acute symptoms involving the sinuses, nose, pharynx, and bronchial tree could be labeled as having an “acute respiratory infection,” either “sinus predominant,” “bronchial predominant,” or even “generalized” when involving all areas of the respiratory tract.
Most evidence suggests that the vast majority of all these infections are caused by viruses. This does not imply that patients do not have infections and are not ill. Not only do viruses vary in the degree in which they infect individuals, but patients also differ in their abilities to cope with their cold symptoms. Rather than attempting to eradicate the cause of the cold, the goals of treatment for these disorders should be improving outcomes that matter to patients, such as reducing their symptoms, improving their ability to function at work and at home, and relieving their anxiety. Instead of trying to eradicate bacteria that do not exist in the majority of patients, physicians should focus on treatments proven to alleviate the predominant symptoms and the underlying reasons patients seek care for this self-limited problem. These latter reasons could include loneliness, anxiety about the severity of the condition (for example, in a young child), or frustration that the symptoms are interrupting other important activities. Attention to these important psychosocial events may be even more important than the type of drug written on the prescription pad. Dismissing a patient with these problems as being uninformed, bothersome, or inappropriately seeking care may not be productive in addressing the underlying reasons the patient has consulted a physician. Failing to address these underlying psychosocial issues or prescribing an antibiotic to meet physicians’ desire to help and meet an unstated patient expectation may result in repetitive care-seeking for the same self-limited symptoms,22,23 ultimately benefiting no one and consuming health care resources that could be better invested elsewhere.
This new model of conceptualizing respiratory infections implies that assigning an anatomic-specific diagnosis to the condition is irrelevant beyond being a determinant for which symptomatic care should be the highest priority (eg, decongestants for predominantly sinus symptoms or bronchodilators for wheezing or rhonchi). Also, although acute respiratory infection is a very common reason for visits to primary care physicians, only some patients consult a clinician for their problem. Care-seeking for acute respiratory infection is likely determined by a combination of forces that include severity of the infection (patients with more severe viral illnesses would be more likely to seek care), anatomic localization (infections with more specifically localized symptoms would be more likely to seek care), and personal psychosocial issues (including the perceived importance of existing or possible disruption of daily activities, personal coping skills and resources, and the need for empathy and affirmation from someone else that they are sick). Rather than focusing research in this area on identifying how certain subsets of patients with what is predominantly a viral illness respond to antibiotics that do not treat what they have, future research in acute respiratory infections might be better directed at why some patients seek care or self-medicate inappropriately24 and how clinicians can best serve this population.
Conclusions
Our study shows a great deal of overlap in the signs and symptoms of acute bronchitis and URIs. Very little of the variation in the diagnoses could be explained by clinical factors. These results are similar to earlier work suggesting considerable clinical overlap in sinusitis. This leads us to hypothesize that these conditions all represent the same clinical entity and that a reconceptualization of acute viral respiratory infections as a single problem, rather than anatomically distinct disorders, is warranted.
1. Kirkwood CR, Clure HR, Brodsky R, et al. The diagnostic content of family practice: 50 most common diagnoses recorded in the WAMI community practices. J Fam Pract 1982;15:485-92.
2. Marsland DW, Wood M, Mayo F. Content of family practice. Part 1: rank order of diagnoses by frequency. J Fam Pract 1976;3:37-68.
3. Hueston WJ, Eberlein C, Johnson D, Mainous AG, III. Criteria used by clinicians to differentiate sinusitis from viral upper respiratory tract infection. J Fam Pract 1998;46:487-92.
4. Vinson DC, Lutz LJ. The effect of parental expectations on the treatment of children with a cough: a report from ASPN. J Fam Pract 1993;37:23-7.
5. Orr PH, Scherer K, Macdonald A, Moffatt MEK. Randomized placebo-controlled trials of antibiotics for acute bronchitis: a critical review of the literature. J Fam Pract 1993;36:507-12.
6. Smucny JJ, Becker LA, Glazier RH, McIsaac W. Are antibiotics effective treatment for acute bronchitis? A meta-analysis. J Fam Pract 1998;47:453-60.
7. Fahey T, Stocks N, Thomas T. Quantitative systematic review of randomised controlled trials comparing antibiotic with placebo for acute cough in adults. BMJ 1998;316:906-10.
8. Mainous AG, Hueston WJ, Clark JR. Do some folks think there is a cure for the common cold? Evidence of widespread use of antibiotics in ambulatory care. J Fam Pract 1996;42:357-61.
9. Hueston WJ, Mainous AG, Brauer N, Mercuri J. Evaluation and treatment of respiratory infections: does managed care make a difference? J Fam Pract 1997;44:572-7.
10. Oeffinger KC, Snell LM, Foster BM, Panico KG, Archer RK. Diagnosis of acute bronchitis in adults: a national survey of family physicians. J Fam Pract 1997;45:402-9.
11. Verheij JM, Hermans J, Kaptein AA, et al. Acute bronchitis: general practitioners’ views regarding diagnosis and treatments. Fam Pract 1990;7:175-80.
12. Mainous AG, Hueston WJ, Eberlein C. Colour of respiratory discharges and antibiotic use. Letter. Lancet 1997;350:1077.-
13. Stott NC, West RR. Randomised controlled trial of antibiotics in patients with cough and purulent sputum. BMJ 1976;2:556-9.
14. Dunlay J, Reinhardt R. Clinical features and treatment of acute bronchitis. J Fam Pract 1984;18:719-22.
15. Mathews DE, Farewell VT. Using and understanding medical statistics. Basel, Switzerland: S. Karger AG; 1988.
16. Manning SC, Biavati MJ, Phillips Dl. Correlation of clinical sinusitis signs and symptoms to imaging findings in pediatric patients. Int J Pediatr Otorhinolaryngol 1996;37:65-74.
17. Mainous AG, III, Zoorob RJ, Oler MJ, Haynes DM. Patient knowledge of colds: implications for antibiotic expectations and unnecessary utilization. J Fam Pract 1997;45:75-83.
18. Mainous AG, MacFarlane LL, Connor MK, Green LA, Fowler K, Hueston WJ. Survey of clinical pharmacists’ knowledge of appropriateness of antimicrobial therapy for upper respiratory infections and bronchitis. Pharmacotherapy 1999;19:388-92.
19. Hueston WJ. Albuterol delivered by metered-dose inhaler to treat acute bronchitis. J Fam Pract 1994;39:437-40.
20. Hueston WJ. A comparison of albuterol and erythromycin for the treatment of acute bronchitis. J Fam Pract 1991;33:476-80.
21. Littenberg B, Wheeler M, Smith DS. A randomized controlled trial of oral albuterol in acute cough. J Fam Pract 1996;42:49-53.
22. Holmes WF, Macfarlane JT, Macfarlane RM, Lewis S. The influence of antibiotics and other factors on reconsultation for acute lower respiratory tract illness in primary care. Br J Gen Pract 1997;47:815-8.
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24. McKee MD, Mills L, Mainous AG, III. Antibiotic use for the treatment of upper respiratory infections in a diverse community. J Fam Pract 1999;48:993-6.
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