Jeanne M. Huddleston, MD

Perioperative myocardial infarction (PMI) often remains unrecognized with higher mortality in the aged.13 Perioperative ischemic symptoms are often masked by analgesia, sedation, and transient and subtle electrocardiographic (ECG) changes. Postoperative troponin measurement is not routinely done for PMI diagnosis. Hip fracture surgery is the most common non‐cardiac surgical procedure in the elderly, with limited data on clinical presentation of PMI.46 Moreover, the elderly are significantly underrepresented in clinical studies.7 We therefore examined the clinical presentation of PMI and its outcomes among elderly patients admitted for hip fracture repair.

METHODS

RESULTS

In the cohort of 1212 with hip fracture surgeries, 167 (13.8%) cases of PMI occurred in the first 7 days, of which 153 (92%) occurred within the first 48 hours. A total of 334 controls were matched with 167 cases of PMI. Table 1 summarizes the demographic characteristics of the study participants. Of the patients with PMI, 25.2% experienced symptoms of ischemia; 7% reported chest pain, and 12% reported dyspnea. Only 22.8% of patients with PMI had ECG changes consistent with ischemia. ST elevation MI was present in 7.2% patients. PMI patients had a lower mean hemoglobin compared to the patients without PMI (8.9 mg/dL vs 9.4 mg/dL, P < 0.001). Median length of stay (LOS) in the hospital was higher among patients who experienced PMI (11.6 vs 7.4 days, P < 0.001). Overall in‐hospital mortality was 5.6%. There were 24 deaths (14.4%) in the PMI group compared to 4 (1.2%) in‐hospital deaths in patients without PMI (P < 0.001). A total of 473 (94%) patients survived to discharge. At 30‐day follow‐up, there were 29 (17.4%) deaths in the PMI group and 14 (4.2%) deaths in non‐PMI group. During the follow‐up for 1 year, there were 143 (29%) deaths: PMI 66 (39.5%) and 77 (23%) non‐PMI group (P < 0.01).

Table 2 describes the risk factors associated with PMI in‐hospital, 30‐day, and 1‐year mortality. Risk factors for PMI were coronary artery disease (CAD) (odds ratio [OR], 3.5; confidence interval [CI], 2.25.6), and serum creatinine >2 mg/dL (OR, 2.4; CI, 1.34.4). Risk factors for in‐hospital mortality were age 8589 (OR, 5.3; CI, 1.617.7), age 90 (OR, 8.9; CI, 2.630.8), PMI (OR 15.1; CI, 4.648.8), male gender (OR 5.8; CI, 2.215.2), dyspnea (OR 5.4; CI, 1.816.9), and hemoglobin <8.0 gm/dL (OR, 3.5; CI, 1.29.9). PMI was a strong predictor for 30‐day mortality (hazard ratio [HR], 4.3; CI, 2.18.9). Risk factors for 1‐year mortality were: age 90 (HR, 2.0; CI, 1.43.1), male gender (HR, 2.1; CI, 1.53.0), and PMI (HR, 1.9; CI, 1.42.7). Figures 1 and 2 describe the Kaplan‐Meier survival curves for patients with and without PMI.

Figure 1

Figure 2

DISCUSSION

We report the high incidence of PMI (13.8%) in the cohort of 1212 elderly patients (mean age 85 years) undergoing hip fracture surgery. Most PMI events (92%) occurred within the first 48 hours of surgery. Most of the events (75%) were asymptomatic. Elderly patients with PMI had an increased hospital LOS by 4.2 days, with high in‐hospital mortality (13.8%), 30‐day mortality (17.4%), and 1‐year mortality (39.5%).

Most of the PMI patients were identified with cardiac biomarkers on the basis of universal definition of MI within the first 48 hours. Although universal definition of MI does not define PMI as a separate type, PMI shares common pathophysiological pathways of Type 1 MI (primary coronary event) and Type 2 MI (myocardial oxygen supplydemand imbalance). Postoperative tachycardia, hemodynamic instability, anemia, and hypoxemia may initiate pathways causing more Type 2 MI. Our study highlights the continued need for active surveillance of clinical symptoms, postoperative ECG monitoring for STT changes, and utilizing cardiac troponin in older postoperative patients to improve diagnostic accuracy of PMI.

The current study has higher asymptomatic PMI events when compared to a study of Devereaux et al.11 The current study had an older population undergoing urgent hip fracture surgery, with a higher burden of CAD (60%) and renal failure (20%) with serum creatinine >2 gm/dL (see Supporting Information, Appendix 1, in the online version of this article). Older age and a higher burden of these risk factors may explain the higher incidence of PMI in the current study. Perioperative liberal use of analgesics in hip fracture surgery may explain more asymptomatic patients.

In light of the recently published FOCUS12 trial, an important finding from our study is that postoperative anemia among elderly (<8.0 gm/dL) is associated with a 3.5‐fold increased in‐hospital mortality. It is critical to maintain perioperative hemoglobin above 8.0 gm/dL in very elderly patients, due to asymptomatic presentation of PMI.

In the current study, PMI is associated with a 15‐fold increased risk of in‐hospital death and a 4.3‐fold increased risk of 30‐day mortality in the elderly. Advanced age (85 years) is a well known strong predictor of initial hospital admission and death in elderly patients after outpatient surgery.13 Furthermore, the odds for an in‐hospital death increase by 70% for each 10‐year increase in age.14 Therefore, early detection of silent PMI among at‐risk elderly patients by cardiac biomarkers may help in optimization of cardiac pharmacotherapy known to decrease short‐ and long‐term mortality.

There are limitations inherent to the retrospective design and methodology. Data collection was done through the year 2002. CK was used for the period that spans from 1988 to mid‐2000. Troponin was used from 2000 to 2002. Statin use was not analyzed for lack of significant data. Limited use of beta‐blockers (15%) and angiotensin‐converting‐enzyme (ACE) inhibitors (25%) may also contribute to higher events (see Supporting Information, Appendix 1, in the online version of this article).

CONCLUSIONS

Elderly patients have a higher incidence of PMI and mortality after hip fracture surgery than what guidelines indicate. The majority of the elderly patients with PMI did not experience ischemic symptoms and required cardiac biomarkers for diagnosis. The results of our study support the measurement of troponin in postoperative elderly patients for the diagnosis of PMI to implement in‐hospital preventive strategies to reduce PMI‐associated mortality.

Perioperative myocardial infarction (PMI) often remains unrecognized with higher mortality in the aged.13 Perioperative ischemic symptoms are often masked by analgesia, sedation, and transient and subtle electrocardiographic (ECG) changes. Postoperative troponin measurement is not routinely done for PMI diagnosis. Hip fracture surgery is the most common non‐cardiac surgical procedure in the elderly, with limited data on clinical presentation of PMI.46 Moreover, the elderly are significantly underrepresented in clinical studies.7 We therefore examined the clinical presentation of PMI and its outcomes among elderly patients admitted for hip fracture repair.

METHODS

RESULTS

In the cohort of 1212 with hip fracture surgeries, 167 (13.8%) cases of PMI occurred in the first 7 days, of which 153 (92%) occurred within the first 48 hours. A total of 334 controls were matched with 167 cases of PMI. Table 1 summarizes the demographic characteristics of the study participants. Of the patients with PMI, 25.2% experienced symptoms of ischemia; 7% reported chest pain, and 12% reported dyspnea. Only 22.8% of patients with PMI had ECG changes consistent with ischemia. ST elevation MI was present in 7.2% patients. PMI patients had a lower mean hemoglobin compared to the patients without PMI (8.9 mg/dL vs 9.4 mg/dL, P < 0.001). Median length of stay (LOS) in the hospital was higher among patients who experienced PMI (11.6 vs 7.4 days, P < 0.001). Overall in‐hospital mortality was 5.6%. There were 24 deaths (14.4%) in the PMI group compared to 4 (1.2%) in‐hospital deaths in patients without PMI (P < 0.001). A total of 473 (94%) patients survived to discharge. At 30‐day follow‐up, there were 29 (17.4%) deaths in the PMI group and 14 (4.2%) deaths in non‐PMI group. During the follow‐up for 1 year, there were 143 (29%) deaths: PMI 66 (39.5%) and 77 (23%) non‐PMI group (P < 0.01).

Table 2 describes the risk factors associated with PMI in‐hospital, 30‐day, and 1‐year mortality. Risk factors for PMI were coronary artery disease (CAD) (odds ratio [OR], 3.5; confidence interval [CI], 2.25.6), and serum creatinine >2 mg/dL (OR, 2.4; CI, 1.34.4). Risk factors for in‐hospital mortality were age 8589 (OR, 5.3; CI, 1.617.7), age 90 (OR, 8.9; CI, 2.630.8), PMI (OR 15.1; CI, 4.648.8), male gender (OR 5.8; CI, 2.215.2), dyspnea (OR 5.4; CI, 1.816.9), and hemoglobin <8.0 gm/dL (OR, 3.5; CI, 1.29.9). PMI was a strong predictor for 30‐day mortality (hazard ratio [HR], 4.3; CI, 2.18.9). Risk factors for 1‐year mortality were: age 90 (HR, 2.0; CI, 1.43.1), male gender (HR, 2.1; CI, 1.53.0), and PMI (HR, 1.9; CI, 1.42.7). Figures 1 and 2 describe the Kaplan‐Meier survival curves for patients with and without PMI.

Figure 1

Figure 2

DISCUSSION

We report the high incidence of PMI (13.8%) in the cohort of 1212 elderly patients (mean age 85 years) undergoing hip fracture surgery. Most PMI events (92%) occurred within the first 48 hours of surgery. Most of the events (75%) were asymptomatic. Elderly patients with PMI had an increased hospital LOS by 4.2 days, with high in‐hospital mortality (13.8%), 30‐day mortality (17.4%), and 1‐year mortality (39.5%).

Most of the PMI patients were identified with cardiac biomarkers on the basis of universal definition of MI within the first 48 hours. Although universal definition of MI does not define PMI as a separate type, PMI shares common pathophysiological pathways of Type 1 MI (primary coronary event) and Type 2 MI (myocardial oxygen supplydemand imbalance). Postoperative tachycardia, hemodynamic instability, anemia, and hypoxemia may initiate pathways causing more Type 2 MI. Our study highlights the continued need for active surveillance of clinical symptoms, postoperative ECG monitoring for STT changes, and utilizing cardiac troponin in older postoperative patients to improve diagnostic accuracy of PMI.

The current study has higher asymptomatic PMI events when compared to a study of Devereaux et al.11 The current study had an older population undergoing urgent hip fracture surgery, with a higher burden of CAD (60%) and renal failure (20%) with serum creatinine >2 gm/dL (see Supporting Information, Appendix 1, in the online version of this article). Older age and a higher burden of these risk factors may explain the higher incidence of PMI in the current study. Perioperative liberal use of analgesics in hip fracture surgery may explain more asymptomatic patients.

In light of the recently published FOCUS12 trial, an important finding from our study is that postoperative anemia among elderly (<8.0 gm/dL) is associated with a 3.5‐fold increased in‐hospital mortality. It is critical to maintain perioperative hemoglobin above 8.0 gm/dL in very elderly patients, due to asymptomatic presentation of PMI.

In the current study, PMI is associated with a 15‐fold increased risk of in‐hospital death and a 4.3‐fold increased risk of 30‐day mortality in the elderly. Advanced age (85 years) is a well known strong predictor of initial hospital admission and death in elderly patients after outpatient surgery.13 Furthermore, the odds for an in‐hospital death increase by 70% for each 10‐year increase in age.14 Therefore, early detection of silent PMI among at‐risk elderly patients by cardiac biomarkers may help in optimization of cardiac pharmacotherapy known to decrease short‐ and long‐term mortality.

There are limitations inherent to the retrospective design and methodology. Data collection was done through the year 2002. CK was used for the period that spans from 1988 to mid‐2000. Troponin was used from 2000 to 2002. Statin use was not analyzed for lack of significant data. Limited use of beta‐blockers (15%) and angiotensin‐converting‐enzyme (ACE) inhibitors (25%) may also contribute to higher events (see Supporting Information, Appendix 1, in the online version of this article).

CONCLUSIONS

Elderly patients have a higher incidence of PMI and mortality after hip fracture surgery than what guidelines indicate. The majority of the elderly patients with PMI did not experience ischemic symptoms and required cardiac biomarkers for diagnosis. The results of our study support the measurement of troponin in postoperative elderly patients for the diagnosis of PMI to implement in‐hospital preventive strategies to reduce PMI‐associated mortality.

Perioperative myocardial infarction (PMI) often remains unrecognized with higher mortality in the aged.13 Perioperative ischemic symptoms are often masked by analgesia, sedation, and transient and subtle electrocardiographic (ECG) changes. Postoperative troponin measurement is not routinely done for PMI diagnosis. Hip fracture surgery is the most common non‐cardiac surgical procedure in the elderly, with limited data on clinical presentation of PMI.46 Moreover, the elderly are significantly underrepresented in clinical studies.7 We therefore examined the clinical presentation of PMI and its outcomes among elderly patients admitted for hip fracture repair.

METHODS

RESULTS

In the cohort of 1212 with hip fracture surgeries, 167 (13.8%) cases of PMI occurred in the first 7 days, of which 153 (92%) occurred within the first 48 hours. A total of 334 controls were matched with 167 cases of PMI. Table 1 summarizes the demographic characteristics of the study participants. Of the patients with PMI, 25.2% experienced symptoms of ischemia; 7% reported chest pain, and 12% reported dyspnea. Only 22.8% of patients with PMI had ECG changes consistent with ischemia. ST elevation MI was present in 7.2% patients. PMI patients had a lower mean hemoglobin compared to the patients without PMI (8.9 mg/dL vs 9.4 mg/dL, P < 0.001). Median length of stay (LOS) in the hospital was higher among patients who experienced PMI (11.6 vs 7.4 days, P < 0.001). Overall in‐hospital mortality was 5.6%. There were 24 deaths (14.4%) in the PMI group compared to 4 (1.2%) in‐hospital deaths in patients without PMI (P < 0.001). A total of 473 (94%) patients survived to discharge. At 30‐day follow‐up, there were 29 (17.4%) deaths in the PMI group and 14 (4.2%) deaths in non‐PMI group. During the follow‐up for 1 year, there were 143 (29%) deaths: PMI 66 (39.5%) and 77 (23%) non‐PMI group (P < 0.01).

Table 2 describes the risk factors associated with PMI in‐hospital, 30‐day, and 1‐year mortality. Risk factors for PMI were coronary artery disease (CAD) (odds ratio [OR], 3.5; confidence interval [CI], 2.25.6), and serum creatinine >2 mg/dL (OR, 2.4; CI, 1.34.4). Risk factors for in‐hospital mortality were age 8589 (OR, 5.3; CI, 1.617.7), age 90 (OR, 8.9; CI, 2.630.8), PMI (OR 15.1; CI, 4.648.8), male gender (OR 5.8; CI, 2.215.2), dyspnea (OR 5.4; CI, 1.816.9), and hemoglobin <8.0 gm/dL (OR, 3.5; CI, 1.29.9). PMI was a strong predictor for 30‐day mortality (hazard ratio [HR], 4.3; CI, 2.18.9). Risk factors for 1‐year mortality were: age 90 (HR, 2.0; CI, 1.43.1), male gender (HR, 2.1; CI, 1.53.0), and PMI (HR, 1.9; CI, 1.42.7). Figures 1 and 2 describe the Kaplan‐Meier survival curves for patients with and without PMI.

Figure 1

Figure 2

DISCUSSION

We report the high incidence of PMI (13.8%) in the cohort of 1212 elderly patients (mean age 85 years) undergoing hip fracture surgery. Most PMI events (92%) occurred within the first 48 hours of surgery. Most of the events (75%) were asymptomatic. Elderly patients with PMI had an increased hospital LOS by 4.2 days, with high in‐hospital mortality (13.8%), 30‐day mortality (17.4%), and 1‐year mortality (39.5%).

Most of the PMI patients were identified with cardiac biomarkers on the basis of universal definition of MI within the first 48 hours. Although universal definition of MI does not define PMI as a separate type, PMI shares common pathophysiological pathways of Type 1 MI (primary coronary event) and Type 2 MI (myocardial oxygen supplydemand imbalance). Postoperative tachycardia, hemodynamic instability, anemia, and hypoxemia may initiate pathways causing more Type 2 MI. Our study highlights the continued need for active surveillance of clinical symptoms, postoperative ECG monitoring for STT changes, and utilizing cardiac troponin in older postoperative patients to improve diagnostic accuracy of PMI.

The current study has higher asymptomatic PMI events when compared to a study of Devereaux et al.11 The current study had an older population undergoing urgent hip fracture surgery, with a higher burden of CAD (60%) and renal failure (20%) with serum creatinine >2 gm/dL (see Supporting Information, Appendix 1, in the online version of this article). Older age and a higher burden of these risk factors may explain the higher incidence of PMI in the current study. Perioperative liberal use of analgesics in hip fracture surgery may explain more asymptomatic patients.

In light of the recently published FOCUS12 trial, an important finding from our study is that postoperative anemia among elderly (<8.0 gm/dL) is associated with a 3.5‐fold increased in‐hospital mortality. It is critical to maintain perioperative hemoglobin above 8.0 gm/dL in very elderly patients, due to asymptomatic presentation of PMI.

In the current study, PMI is associated with a 15‐fold increased risk of in‐hospital death and a 4.3‐fold increased risk of 30‐day mortality in the elderly. Advanced age (85 years) is a well known strong predictor of initial hospital admission and death in elderly patients after outpatient surgery.13 Furthermore, the odds for an in‐hospital death increase by 70% for each 10‐year increase in age.14 Therefore, early detection of silent PMI among at‐risk elderly patients by cardiac biomarkers may help in optimization of cardiac pharmacotherapy known to decrease short‐ and long‐term mortality.

There are limitations inherent to the retrospective design and methodology. Data collection was done through the year 2002. CK was used for the period that spans from 1988 to mid‐2000. Troponin was used from 2000 to 2002. Statin use was not analyzed for lack of significant data. Limited use of beta‐blockers (15%) and angiotensin‐converting‐enzyme (ACE) inhibitors (25%) may also contribute to higher events (see Supporting Information, Appendix 1, in the online version of this article).

CONCLUSIONS

Elderly patients have a higher incidence of PMI and mortality after hip fracture surgery than what guidelines indicate. The majority of the elderly patients with PMI did not experience ischemic symptoms and required cardiac biomarkers for diagnosis. The results of our study support the measurement of troponin in postoperative elderly patients for the diagnosis of PMI to implement in‐hospital preventive strategies to reduce PMI‐associated mortality.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

As the population ages, hip fractures and heart failure increase in prevalence.1, 2 Heart failure prevalence is also increasing in hospitalized patients.3 Indeed, hospitalizations involving heart failure as an active issue tripled in the last 30 years.4 Heart failure has been associated with an increased risk for hip fracture,5, 6 and previous studies report a 6%20% prevalence of preoperative heart failure in hip fracture patients.710 While exacerbation of heart failure increases the mortality risk in patients admitted for hip fractures,8 the incidence of new heart failure, as well as the preoperative factors that predict postoperative heart failure in this patient population remain unclear.

American College of Cardiology/American Heart Association (ACC/AHA) perioperative guidelines identify orthopedic surgeries, including hip fracture repair, as intermediate risk procedures.11 Compared to other intermediate risk operations, however, postoperative outcomes following hip fracture repair differ significantly.1216 Overall mortality in hip fracture patients has been reported at 29% at one year,8 with the excess mortality from hip fracture alone at nearly 20%.10, 13 However, the exact factors that contribute to this excess mortality, particularly with regard to heart failure, remain unclear.

To examine the preoperative prevalence, subsequent incidence, and predictors of heart failure in patients undergoing hip fracture repair operations, this study used an established, population‐based database to compare the postoperative consequences in hip fracture repair patients with and without preexisting heart failure. We hypothesized that preoperative heart failure worsens postoperative outcomes in hip fracture patients.

METHODS

RESULTS

From among 1327 potential hip fracture repairs, we excluded 115 cases involving multiple injuries or operations (19), pathological fractures (20), in‐hospital fractures (3), or an operation >72 hours after the initial fracture (5). Three patients under 65 years of age were also excluded, as were cases with missing information (9) or cases managed nonoperatively (56). The final analysis included 1212 surgical cases in 1116 subjects. No subjects were lost to surveillance for 1 year following their hip fracture repair.

Table 1 summarizes the baseline characteristics of the study population. The overall prevalence of preoperative heart failure was 27.0% (327 of 1212). Those with preoperative heart failure were older, heavier, more likely male and white, and less likely to live independently preoperatively. They were also more likely to suffer from preexisting cardiovascular comorbidities.

Table 1 also summarizes the main outcome characteristics of the study population. Those with preoperative heart failure had longer mean lengths of stay (LOS), were more often discharged to a skilled facility, and demonstrated higher inpatient mortality rates.

Table 2 summarizes the outcomes associated with preoperative heart failure. The overall rate of postoperative heart failure was 6.7% within 7 postoperative days and 21.3% within 1 postoperative year. Postoperative heart failure was significantly more common among those with preoperative heart failure (hazard ratio [HR], 3.0; 95% confidence interval [CI], 2.3 to 3.9; P < 0.001). Among those without preoperative heart failure, rates of postoperative incident heart failure were 4.8% at 7 days and 15.0% at 1 year. Compared to patients without preoperative heart failure, those with preoperative heart failure demonstrated higher one year mortality rates and higher rates of postoperative heart failure at 7 days and 1 year.

Figure 1 displays the outcomes to 1 year of surveillance. Rates of postoperative heart failure and postoperative mortality were consistently higher among those with, versus without, preoperative heart failure. Figure 2 displays similar data stratified by gender. Postoperative heart failure rates did not differ significantly between genders (HR, 1.0; 95% CI, 0.8 to 1.4), but postoperative mortality rates were significantly higher among males than females (HR, 1.9; 95% CI, 1.5 to 2.5; P < 0.001).

Figure 1

Figure 2

Figure 3 displays survival rates to 1 year based on the occurrence of incident or recurrent heart failure within the first 7 postoperative days. Survival rates were lowest among patients with recurrent heart failure in the first 7 postoperative days and highest among those with no preoperative or postoperative heart failure. Subjects with incident heart failure in the first postoperative week, and those with preoperative heart failure who did not suffer a recurrence, demonstrated intermediate survival rates (P < 0.001 for trend across all four groups).

Figure 3

DISCUSSION

This population‐based study found that heart failure represents a highly prevalent condition in elderly patients undergoing hip fracture repairs. It demonstrates that those with preoperative heart failure typically suffer from more cardiovascular comorbidities and carry a higher risk of postoperative heart failure and postoperative mortality.

While many studies have focused on the epidemiology of hip fractures,21 population‐based data on cardiac complications following hip fracture repair are significantly less common. The ACC/AHA preoperative cardiac evaluation guidelines classify orthopedic procedures, including hip fracture repair, as intermediate risk.11 Consequently, some may assume that all orthopedic patients will have a mortality rate less than 5%. Indeed, the 30‐day postoperative mortality rate published from our institution's Total Joint Registry was 0.6% following elective total hip arthroplasty.22 However, the present study demonstrates that current ACC/AHA preoperative cardiac evaluation guidelines may not apply to the population of frail patients undergoing hip fracture repair. Particularly among those who experience new heart failure within the first seven days following surgery, outcomes are substantially worse than the ACC/AHA perioperative guidelines may suggest.11

Preoperative heart failure has been associated with adverse risk for postoperative mortality after hip fracture.9, 10, 12 However, these studies did not report heart failure as a complication of hip fracture repair. A prospective cohort study of 2448 hip fracture patients at an academic hospital in Great Britain found a 5% rate of inpatient heart failure as a postoperative complication.23 The hazard ratio for one‐year mortality was 11.3 with postoperative heart failure.23 However, the British study did not distinguish heart failure from other cardiovascular diseases as a preoperative comorbidity or stratify the risk for postoperative mortality by preoperative heart failure status.23 Our findings add to previous literature by measuring heart failure as a specific complication of hip fracture repair and examining the association of preoperative heart failure with postoperative heart failure and mortality.

Length of stay after hip fracture repair varies in the literature, but previous work has not clearly associated heart failure with length of hospitalization in the setting of hip fracture repair.24, 25 Our study found a significantly higher mean length of stay among those with preoperative heart failure. This adds to previous work by delineating an association between heart failure and increased length of stay after hip fracture repair.

We found a higher rate of postoperative mortality among men compared to women. Rates of postoperative heart failure, however, were more similar (Figure 2). Previous studies have found a consistently higher mortality rate among men versus women after hip fracture.9, 23, 2529 Possible explanations for these findings include the overall increased burden of cardiovascular disease among men, lower treatment rates of osteoporosis in men,30 and increased susceptibility to other postoperative complications, such as infection.25

The findings of this study carry important clinical implications for the perioperative care of hip fracture patients with, or at risk for, heart failure. They suggest that current risk stratification guidelines classifying orthopedic operations as intermediate risk procedures do not reflect the high risk for morbidity that hip fracture patients face.11 The association of heart failure with adverse outcomes implies the need for heightened surveillance in the perioperative period, particularly with regard to volume status and medication reconciliation. Hip fracture patients and their families must be counseled about the ramifications of perioperative heart failure, including higher rates of postoperative heart failure, longer hospitalizations, and ultimate mortality.

This research carries several limitations and remains subject to biases inherent in retrospective cohort studies. The reported effects of heart failure on outcomes after hip fracture repair may be due to confounding from age, functional status, and other comorbidities. We attempted to minimize sampling bias through complete enumeration of hip fracture surgeries among Olmsted County residents. Completeness of follow‐up (100% at one year) was possible given the availability of documentation of all inpatient and outpatient medical care in the community.17 We used objectively defined outcomes to minimize measurement bias. Applicability to a more diverse population may be limited because >95% of the research population was from a single, predominantly white community. However, prior studies have documented that hip fracture incidence rates31 and socioeconomic factors17 in Olmsted County are similar to those for other white residents of the United States. Heart failure rates were determined clinically according to the Framingham criteria. However, the Framingham criteria may inappropriately diagnose individuals with heart failure32 and falsely elevate the prevalence of heart failure as a preoperative comorbidity or postoperative complication.

The statistical analysis included patients counted multiple times if they underwent subsequent hip fracture repair during the study period. Including these patients may inaccurately inflate event rates or contribute to incorrect estimates of standard error. However, we felt it was appropriate to include recurrent hip fracture repair cases in the analysis because they represent a clinically distinct patient from both a medical and functional perspective. We used a robust variance estimator in the Cox proportional hazards models to provide an accurate calculation of the standard error given the possibility for correlation within subjects.20 Finally, the proportion of these patients was low (94 of 1116 unique patients; 8.4%).

Future work must involve further risk stratification and therapeutic interventions in perioperative hip fracture patients. A more robust analysis of heart failure, with differentiation between systolic and diastolic dysfunction, may facilitate risk stratification. Assessment of compliance with standard preoperative heart failure medications and the impact of heightened clinical vigilance may enlighten means to improve postoperative outcomes. Studies on risk stratification and therapeutic interventions may then inform policy regarding length of stay and reimbursement in hip fracture patients.

CONCLUSION

In summary, our population‐based findings reveal that heart failure represents a prevalent and serious comorbidity in patients undergoing hip fracture repair. Clinicians caring for perioperative hip fracture patients must pay particular attention to risk for, and implications of, new or recurrent heart failure.

The decision to transfer a patient to the intensive care unit (ICU) from a general care setting is complex and based not only on clinical findings and patient wishes but also on the understanding that ICU resources are limited and costly.1 Adding to the decision‐making complexity is the knowledge that patients who transfer to an ICU from a general medical unit comprise the highest mortality group of ICU patients, with the mortality rate directly proportional to both the time spent on the general medical unit1, 2 and the number of physiologic abnormalities before ICU admission.3, 4

Prior studies have shown that cardiac arrest and unplanned (unexpected) transfers to the ICU are preceded by a period of physiologic instability reflected in the vital signs.510 However, vital signs alone may not accurately indicate clinical condition. For example, a person may be able to maintain normal blood pressure and heart rate despite severe illness or may have abnormal vital signs at baseline, which may be the case for an otherwise healthy young woman who has baseline low systolic blood pressure. Also, noncritical conditions commonly seen in hospitalized medical or surgical patients, such as anxiety or pain, may increase the respiratory rate or heart rate. Conversely, certain common medications, such as ‐blockers, may mask or blunt the normal physiologic response to illness. Overall, the prevalence of abnormal physiologic variables is high among hospitalized adult patients irrespective of the presence of serious adverse events.11 This prevalence may be a reason why 2 recent studies of inpatient medical emergency teams (METs) or rapid response teams (RRTs), which generally rely on vital signs for activation, failed to show a decrease in adult mortality rates.12, 13

Given the complexity of interpreting single vital sign readings, we evaluated a simple and clinically intuitive variable, the shock index (SI) (heart rate/systolic blood pressure, a noninvasive indication of left ventricular function),14 as a potential marker of the need for intensive care. Allgwer and Burri14 first developed the SI in studies of patients with acute blood loss, intraabdominal bleeding, fat emboli, and severe infections. They observed that a healthy adult had a mean SI of 0.54 (standard deviation [SD], 0.021), while an index of 1.0 indicated threatened shock and indices greater than 1.5 were seen in volume‐deficient shock.

We hypothesized that an elevated SI is a differentiating factor between a patient who had an unplanned ICU transfer and a general medical patient who did not require this higher level of care. To our knowledge, the SI has not been studied for this application previously.

Patients and Methods

Results

A total of 50 pairs of matching cases and controls was included in this study. Table 1 lists the source of admission, demographic characteristics, and numbers of deaths for cases and controls. There were no statistically significant differences in admission source, age, sex, ethnicity, admission care unit, or Charlson Comorbidity Index. Mean length of stay was 14.8 days (SD, 9.7 days) for the cases and 5.7 days (SD, 6.3 days; P < 0.001) for the controls. Admission diagnoses were classified on the basis of the organ system of involvement (Table 2). In 30 of 50 cases, the admission diagnosis and the reason for ICU transfer were related.

We reviewed the vital signs and shock indices for the 24 hours before ICU transfer for each case and over the entire hospitalization for each control, to determine the worst set (the lowest systolic blood pressure and the highest heart rate, respiratory rate, and SI). The cases had 1 to 22 complete sets of vitals for the 24 hours before ICU transfer; the median number of sets was 3 and the mean was 4. The controls had 1 to 12 complete sets for the 24 hours before the worst SI: the median was 3 sets and the mean was 3. In 26 of 50 controls, the worst SI occurred within the first 24 hours after admission. There was a significant difference between the median values of the worst shock indices of the cases and the controls (0.87 vs. 0.72; P < 0.005).

Table 3 shows the different values of the SI and the corresponding odds ratio of unplanned ICU transfer for cases compared with controls. The difference was significant at an SI of 0.85 and greater, indicating a strong association with unplanned ICU transfer.

We also found that the patients who transferred to the ICU had a greater number of inpatient deaths (9 cases vs. 1 control; P = 0.008), which would be expected, but there was no difference in 30‐day or 6‐month mortality rate (Table 1). One patient died after 30 days and while still hospitalized.

Comparison between the temporal trend of vital signs and the SI of the cases for the 24 hours before ICU transfer is shown in Figure 1. This graph shows the median of all the worst values (minimum systolic blood pressure and maximum SI, heart rate, and respiratory rate) over the four 6‐hour time periods (24 hours) preceding ICU transfer. Of note, the change in vital signs is subtle even while the SI increased to more than 0.8 as the patients clinically worsened before transfer.

Figure 1

Discussion

In our comparison of the SI of 50 patients who required unplanned (unexpected) transfer to the ICU with the SI of 50 matched controls who did not require this higher level of care, we found that a SI of 0.85 or greater was significantly associated with unplanned transfer to the ICU. The cases had a significantly higher worst SI than the controls, and they also had a significantly longer hospital stay and higher inpatient mortality rate, as would be expected for a sicker patient population. These findings are important given that the SI may be useful for assessing illness severity, for helping determine the need for transfer to the ICU, or for activating METs or RRTs.

A major problem with providing optimal care for hospitalized general medical patients is the inherent difficulty in determining illness severity and clinical decline, especially when the decline occurs gradually. Existing consensus recommendations for ICU admission include both specific diagnoses and arbitrary objective criteria based on abnormal vital signs and laboratory values.27 Also, individual institutions may have their own ICU admission requirements, which may differ from these or RRT criteria. Although vital signs are important as a snapshot of basic physiologic function, a number of noncritical conditions may lead to abnormal vital signs, and not all abnormal vital signs are associated with an adverse clinical event. By relying solely on vital signs, clinicians may not recognize critical illness and therefore not transfer a patient to the ICU or may inappropriately transfer a patient who does not need ICU‐level care.

Markers of illness severity other than vital signs, such as the Acute Physiology and Chronic Health Evaluation (APACHE) score, have been shown to predict the death of ICU patients17, 18 but have been rarely studied outside the ICU setting.19 Also, calculating the APACHE score is cumbersome, and there is no cutoff score that defines when a patient should be transferred to the ICU. Subbe et al.,20 in their study to identify critically ill patients, found that introduction of a physiological scoring system (including MET or RRT activation scores) would have identified only a small number of additional patients as critically ill. Another common marker of illness severity, the 4 criteria of the systemic inflammatory response syndrome (temperature <36C or >38C; heart rate >90 beats per minute; respiratory rate >20 breaths per minute or PCO₂ <32 mm Hg; and white blood cell count >12,000/L or <4000/L or with more than 10% band cells)21 may be too sensitive to use as a decision aid, since even a healthy person running after a bus could have 2 of the 4 criteria.22 Likewise, surgical patients may have transient leukocytosis due to a stress response independent of an infection.23

The SI may be more accurate than vital signs alone to determine illness severity and who is at risk for an unplanned transfer to the ICU. Birkhahn et al.24 concluded that the SI may be more useful in early hemorrhage than either heart rate or systolic blood pressure alone. Rady et al.25 showed that the SI used in the emergency department can identify critical illness with apparently stable vital signs, where an elevation of the SI above 0.9 was associated with an illness that was treated immediately with admission to the hospital and intensive therapy on admission. However, it is unclear whether the SI can be used to monitor ongoing treatment, because a previous study showed that the SI may be of limited value in the assessment of systemic oxygen transport and response to therapy in clinical septic shock.26 Of note, the SI is mostly independent of the effects of pain or anxiety, which cause a concurrent rise in heart rate and systolic blood pressure. Because the heart's left ventricular work is unchanged or may increase from the underlying catecholamine surge, the SI will be unchanged or may actually decrease.

Our study adds to the medical literature the findings that: (1) the SI may be useful as an indicator of illness severity and a triage tool in patients with no trauma but with various medical conditions, and (2) the SI showed a strong association with unplanned ICU transfer.

The main strength of our study is its case‐control design with matched controls. Also, by comparing groups from the same patient care unit, we sought to minimize the selection bias that can be inherent in case‐control studies. Limitations include the retrospective, nonrandomized study design and the fact that there may have been variations in vital sign measurements by the multiple caregivers. However, the vital signs were taken according to standard hospital practice and reflect real‐world conditions. Although generalizability may be somewhat limited because of our homogeneous patient population, our patients had a wide range of various medical illnesses, so our study should be applicable to other hospital settings, both academic and community‐based.

One of the main weaknesses of our study is that the results were not adjusted for the burden of comorbid conditions, although there were no statistically significant differences in the number of comorbid conditions among the cases and the controls (P = 0.96). Also, we did not directly compare the SI with vital signs alone to determine superiority.

The SI may be an important objective measure to help clinicians decide when patients need treatment that is more aggressive, assistance from a MET or an RRT, or a preemptive, rather than unplanned, transfer to an ICU. Although it is unlikely that a single measure will allow accurate triage of all medical or surgical patients, the SI may be a useful adjunct to clinical judgment and other objective measures in determining illness severity and clinical decline. Further prospective studies are needed to compare the role of the SI specifically with MET or RRT activation criteria, to clarify the role of comorbid conditions in unplanned transfers to the ICU, to validate the cut point for the SI in various disease states, and to assess its utility in patients with septic shock. Depending on these results, it may be beneficial to incorporate the SI into the electronic medical record as an automatic alert to identify patients at risk for ICU transfer.

Conclusions

The SI is an easily calculated composite index of heart rate and systolic blood pressure. An elevated SI of 0.85 can identify patients who are at risk for unplanned transfer to the ICU from general patient care units. Future studies will determine whether the SI is more accurate than simple vital signs as an indicator of clinical decline. If so, it may be useful as a trigger to activate METs or RRTs for treatment.

The decision to transfer a patient to the intensive care unit (ICU) from a general care setting is complex and based not only on clinical findings and patient wishes but also on the understanding that ICU resources are limited and costly.1 Adding to the decision‐making complexity is the knowledge that patients who transfer to an ICU from a general medical unit comprise the highest mortality group of ICU patients, with the mortality rate directly proportional to both the time spent on the general medical unit1, 2 and the number of physiologic abnormalities before ICU admission.3, 4

Prior studies have shown that cardiac arrest and unplanned (unexpected) transfers to the ICU are preceded by a period of physiologic instability reflected in the vital signs.510 However, vital signs alone may not accurately indicate clinical condition. For example, a person may be able to maintain normal blood pressure and heart rate despite severe illness or may have abnormal vital signs at baseline, which may be the case for an otherwise healthy young woman who has baseline low systolic blood pressure. Also, noncritical conditions commonly seen in hospitalized medical or surgical patients, such as anxiety or pain, may increase the respiratory rate or heart rate. Conversely, certain common medications, such as ‐blockers, may mask or blunt the normal physiologic response to illness. Overall, the prevalence of abnormal physiologic variables is high among hospitalized adult patients irrespective of the presence of serious adverse events.11 This prevalence may be a reason why 2 recent studies of inpatient medical emergency teams (METs) or rapid response teams (RRTs), which generally rely on vital signs for activation, failed to show a decrease in adult mortality rates.12, 13

Given the complexity of interpreting single vital sign readings, we evaluated a simple and clinically intuitive variable, the shock index (SI) (heart rate/systolic blood pressure, a noninvasive indication of left ventricular function),14 as a potential marker of the need for intensive care. Allgwer and Burri14 first developed the SI in studies of patients with acute blood loss, intraabdominal bleeding, fat emboli, and severe infections. They observed that a healthy adult had a mean SI of 0.54 (standard deviation [SD], 0.021), while an index of 1.0 indicated threatened shock and indices greater than 1.5 were seen in volume‐deficient shock.

We hypothesized that an elevated SI is a differentiating factor between a patient who had an unplanned ICU transfer and a general medical patient who did not require this higher level of care. To our knowledge, the SI has not been studied for this application previously.

Patients and Methods

Results

A total of 50 pairs of matching cases and controls was included in this study. Table 1 lists the source of admission, demographic characteristics, and numbers of deaths for cases and controls. There were no statistically significant differences in admission source, age, sex, ethnicity, admission care unit, or Charlson Comorbidity Index. Mean length of stay was 14.8 days (SD, 9.7 days) for the cases and 5.7 days (SD, 6.3 days; P < 0.001) for the controls. Admission diagnoses were classified on the basis of the organ system of involvement (Table 2). In 30 of 50 cases, the admission diagnosis and the reason for ICU transfer were related.

We reviewed the vital signs and shock indices for the 24 hours before ICU transfer for each case and over the entire hospitalization for each control, to determine the worst set (the lowest systolic blood pressure and the highest heart rate, respiratory rate, and SI). The cases had 1 to 22 complete sets of vitals for the 24 hours before ICU transfer; the median number of sets was 3 and the mean was 4. The controls had 1 to 12 complete sets for the 24 hours before the worst SI: the median was 3 sets and the mean was 3. In 26 of 50 controls, the worst SI occurred within the first 24 hours after admission. There was a significant difference between the median values of the worst shock indices of the cases and the controls (0.87 vs. 0.72; P < 0.005).

Table 3 shows the different values of the SI and the corresponding odds ratio of unplanned ICU transfer for cases compared with controls. The difference was significant at an SI of 0.85 and greater, indicating a strong association with unplanned ICU transfer.

We also found that the patients who transferred to the ICU had a greater number of inpatient deaths (9 cases vs. 1 control; P = 0.008), which would be expected, but there was no difference in 30‐day or 6‐month mortality rate (Table 1). One patient died after 30 days and while still hospitalized.

Comparison between the temporal trend of vital signs and the SI of the cases for the 24 hours before ICU transfer is shown in Figure 1. This graph shows the median of all the worst values (minimum systolic blood pressure and maximum SI, heart rate, and respiratory rate) over the four 6‐hour time periods (24 hours) preceding ICU transfer. Of note, the change in vital signs is subtle even while the SI increased to more than 0.8 as the patients clinically worsened before transfer.

Figure 1

Discussion

In our comparison of the SI of 50 patients who required unplanned (unexpected) transfer to the ICU with the SI of 50 matched controls who did not require this higher level of care, we found that a SI of 0.85 or greater was significantly associated with unplanned transfer to the ICU. The cases had a significantly higher worst SI than the controls, and they also had a significantly longer hospital stay and higher inpatient mortality rate, as would be expected for a sicker patient population. These findings are important given that the SI may be useful for assessing illness severity, for helping determine the need for transfer to the ICU, or for activating METs or RRTs.

A major problem with providing optimal care for hospitalized general medical patients is the inherent difficulty in determining illness severity and clinical decline, especially when the decline occurs gradually. Existing consensus recommendations for ICU admission include both specific diagnoses and arbitrary objective criteria based on abnormal vital signs and laboratory values.27 Also, individual institutions may have their own ICU admission requirements, which may differ from these or RRT criteria. Although vital signs are important as a snapshot of basic physiologic function, a number of noncritical conditions may lead to abnormal vital signs, and not all abnormal vital signs are associated with an adverse clinical event. By relying solely on vital signs, clinicians may not recognize critical illness and therefore not transfer a patient to the ICU or may inappropriately transfer a patient who does not need ICU‐level care.

Markers of illness severity other than vital signs, such as the Acute Physiology and Chronic Health Evaluation (APACHE) score, have been shown to predict the death of ICU patients17, 18 but have been rarely studied outside the ICU setting.19 Also, calculating the APACHE score is cumbersome, and there is no cutoff score that defines when a patient should be transferred to the ICU. Subbe et al.,20 in their study to identify critically ill patients, found that introduction of a physiological scoring system (including MET or RRT activation scores) would have identified only a small number of additional patients as critically ill. Another common marker of illness severity, the 4 criteria of the systemic inflammatory response syndrome (temperature <36C or >38C; heart rate >90 beats per minute; respiratory rate >20 breaths per minute or PCO₂ <32 mm Hg; and white blood cell count >12,000/L or <4000/L or with more than 10% band cells)21 may be too sensitive to use as a decision aid, since even a healthy person running after a bus could have 2 of the 4 criteria.22 Likewise, surgical patients may have transient leukocytosis due to a stress response independent of an infection.23

The SI may be more accurate than vital signs alone to determine illness severity and who is at risk for an unplanned transfer to the ICU. Birkhahn et al.24 concluded that the SI may be more useful in early hemorrhage than either heart rate or systolic blood pressure alone. Rady et al.25 showed that the SI used in the emergency department can identify critical illness with apparently stable vital signs, where an elevation of the SI above 0.9 was associated with an illness that was treated immediately with admission to the hospital and intensive therapy on admission. However, it is unclear whether the SI can be used to monitor ongoing treatment, because a previous study showed that the SI may be of limited value in the assessment of systemic oxygen transport and response to therapy in clinical septic shock.26 Of note, the SI is mostly independent of the effects of pain or anxiety, which cause a concurrent rise in heart rate and systolic blood pressure. Because the heart's left ventricular work is unchanged or may increase from the underlying catecholamine surge, the SI will be unchanged or may actually decrease.

Our study adds to the medical literature the findings that: (1) the SI may be useful as an indicator of illness severity and a triage tool in patients with no trauma but with various medical conditions, and (2) the SI showed a strong association with unplanned ICU transfer.

The main strength of our study is its case‐control design with matched controls. Also, by comparing groups from the same patient care unit, we sought to minimize the selection bias that can be inherent in case‐control studies. Limitations include the retrospective, nonrandomized study design and the fact that there may have been variations in vital sign measurements by the multiple caregivers. However, the vital signs were taken according to standard hospital practice and reflect real‐world conditions. Although generalizability may be somewhat limited because of our homogeneous patient population, our patients had a wide range of various medical illnesses, so our study should be applicable to other hospital settings, both academic and community‐based.

One of the main weaknesses of our study is that the results were not adjusted for the burden of comorbid conditions, although there were no statistically significant differences in the number of comorbid conditions among the cases and the controls (P = 0.96). Also, we did not directly compare the SI with vital signs alone to determine superiority.

The SI may be an important objective measure to help clinicians decide when patients need treatment that is more aggressive, assistance from a MET or an RRT, or a preemptive, rather than unplanned, transfer to an ICU. Although it is unlikely that a single measure will allow accurate triage of all medical or surgical patients, the SI may be a useful adjunct to clinical judgment and other objective measures in determining illness severity and clinical decline. Further prospective studies are needed to compare the role of the SI specifically with MET or RRT activation criteria, to clarify the role of comorbid conditions in unplanned transfers to the ICU, to validate the cut point for the SI in various disease states, and to assess its utility in patients with septic shock. Depending on these results, it may be beneficial to incorporate the SI into the electronic medical record as an automatic alert to identify patients at risk for ICU transfer.

Conclusions

The SI is an easily calculated composite index of heart rate and systolic blood pressure. An elevated SI of 0.85 can identify patients who are at risk for unplanned transfer to the ICU from general patient care units. Future studies will determine whether the SI is more accurate than simple vital signs as an indicator of clinical decline. If so, it may be useful as a trigger to activate METs or RRTs for treatment.

The decision to transfer a patient to the intensive care unit (ICU) from a general care setting is complex and based not only on clinical findings and patient wishes but also on the understanding that ICU resources are limited and costly.1 Adding to the decision‐making complexity is the knowledge that patients who transfer to an ICU from a general medical unit comprise the highest mortality group of ICU patients, with the mortality rate directly proportional to both the time spent on the general medical unit1, 2 and the number of physiologic abnormalities before ICU admission.3, 4

Prior studies have shown that cardiac arrest and unplanned (unexpected) transfers to the ICU are preceded by a period of physiologic instability reflected in the vital signs.510 However, vital signs alone may not accurately indicate clinical condition. For example, a person may be able to maintain normal blood pressure and heart rate despite severe illness or may have abnormal vital signs at baseline, which may be the case for an otherwise healthy young woman who has baseline low systolic blood pressure. Also, noncritical conditions commonly seen in hospitalized medical or surgical patients, such as anxiety or pain, may increase the respiratory rate or heart rate. Conversely, certain common medications, such as ‐blockers, may mask or blunt the normal physiologic response to illness. Overall, the prevalence of abnormal physiologic variables is high among hospitalized adult patients irrespective of the presence of serious adverse events.11 This prevalence may be a reason why 2 recent studies of inpatient medical emergency teams (METs) or rapid response teams (RRTs), which generally rely on vital signs for activation, failed to show a decrease in adult mortality rates.12, 13

Given the complexity of interpreting single vital sign readings, we evaluated a simple and clinically intuitive variable, the shock index (SI) (heart rate/systolic blood pressure, a noninvasive indication of left ventricular function),14 as a potential marker of the need for intensive care. Allgwer and Burri14 first developed the SI in studies of patients with acute blood loss, intraabdominal bleeding, fat emboli, and severe infections. They observed that a healthy adult had a mean SI of 0.54 (standard deviation [SD], 0.021), while an index of 1.0 indicated threatened shock and indices greater than 1.5 were seen in volume‐deficient shock.

We hypothesized that an elevated SI is a differentiating factor between a patient who had an unplanned ICU transfer and a general medical patient who did not require this higher level of care. To our knowledge, the SI has not been studied for this application previously.

Patients and Methods

Results

A total of 50 pairs of matching cases and controls was included in this study. Table 1 lists the source of admission, demographic characteristics, and numbers of deaths for cases and controls. There were no statistically significant differences in admission source, age, sex, ethnicity, admission care unit, or Charlson Comorbidity Index. Mean length of stay was 14.8 days (SD, 9.7 days) for the cases and 5.7 days (SD, 6.3 days; P < 0.001) for the controls. Admission diagnoses were classified on the basis of the organ system of involvement (Table 2). In 30 of 50 cases, the admission diagnosis and the reason for ICU transfer were related.

We reviewed the vital signs and shock indices for the 24 hours before ICU transfer for each case and over the entire hospitalization for each control, to determine the worst set (the lowest systolic blood pressure and the highest heart rate, respiratory rate, and SI). The cases had 1 to 22 complete sets of vitals for the 24 hours before ICU transfer; the median number of sets was 3 and the mean was 4. The controls had 1 to 12 complete sets for the 24 hours before the worst SI: the median was 3 sets and the mean was 3. In 26 of 50 controls, the worst SI occurred within the first 24 hours after admission. There was a significant difference between the median values of the worst shock indices of the cases and the controls (0.87 vs. 0.72; P < 0.005).

Table 3 shows the different values of the SI and the corresponding odds ratio of unplanned ICU transfer for cases compared with controls. The difference was significant at an SI of 0.85 and greater, indicating a strong association with unplanned ICU transfer.

We also found that the patients who transferred to the ICU had a greater number of inpatient deaths (9 cases vs. 1 control; P = 0.008), which would be expected, but there was no difference in 30‐day or 6‐month mortality rate (Table 1). One patient died after 30 days and while still hospitalized.

Comparison between the temporal trend of vital signs and the SI of the cases for the 24 hours before ICU transfer is shown in Figure 1. This graph shows the median of all the worst values (minimum systolic blood pressure and maximum SI, heart rate, and respiratory rate) over the four 6‐hour time periods (24 hours) preceding ICU transfer. Of note, the change in vital signs is subtle even while the SI increased to more than 0.8 as the patients clinically worsened before transfer.

Figure 1

Discussion

In our comparison of the SI of 50 patients who required unplanned (unexpected) transfer to the ICU with the SI of 50 matched controls who did not require this higher level of care, we found that a SI of 0.85 or greater was significantly associated with unplanned transfer to the ICU. The cases had a significantly higher worst SI than the controls, and they also had a significantly longer hospital stay and higher inpatient mortality rate, as would be expected for a sicker patient population. These findings are important given that the SI may be useful for assessing illness severity, for helping determine the need for transfer to the ICU, or for activating METs or RRTs.

A major problem with providing optimal care for hospitalized general medical patients is the inherent difficulty in determining illness severity and clinical decline, especially when the decline occurs gradually. Existing consensus recommendations for ICU admission include both specific diagnoses and arbitrary objective criteria based on abnormal vital signs and laboratory values.27 Also, individual institutions may have their own ICU admission requirements, which may differ from these or RRT criteria. Although vital signs are important as a snapshot of basic physiologic function, a number of noncritical conditions may lead to abnormal vital signs, and not all abnormal vital signs are associated with an adverse clinical event. By relying solely on vital signs, clinicians may not recognize critical illness and therefore not transfer a patient to the ICU or may inappropriately transfer a patient who does not need ICU‐level care.

Markers of illness severity other than vital signs, such as the Acute Physiology and Chronic Health Evaluation (APACHE) score, have been shown to predict the death of ICU patients17, 18 but have been rarely studied outside the ICU setting.19 Also, calculating the APACHE score is cumbersome, and there is no cutoff score that defines when a patient should be transferred to the ICU. Subbe et al.,20 in their study to identify critically ill patients, found that introduction of a physiological scoring system (including MET or RRT activation scores) would have identified only a small number of additional patients as critically ill. Another common marker of illness severity, the 4 criteria of the systemic inflammatory response syndrome (temperature <36C or >38C; heart rate >90 beats per minute; respiratory rate >20 breaths per minute or PCO₂ <32 mm Hg; and white blood cell count >12,000/L or <4000/L or with more than 10% band cells)21 may be too sensitive to use as a decision aid, since even a healthy person running after a bus could have 2 of the 4 criteria.22 Likewise, surgical patients may have transient leukocytosis due to a stress response independent of an infection.23

The SI may be more accurate than vital signs alone to determine illness severity and who is at risk for an unplanned transfer to the ICU. Birkhahn et al.24 concluded that the SI may be more useful in early hemorrhage than either heart rate or systolic blood pressure alone. Rady et al.25 showed that the SI used in the emergency department can identify critical illness with apparently stable vital signs, where an elevation of the SI above 0.9 was associated with an illness that was treated immediately with admission to the hospital and intensive therapy on admission. However, it is unclear whether the SI can be used to monitor ongoing treatment, because a previous study showed that the SI may be of limited value in the assessment of systemic oxygen transport and response to therapy in clinical septic shock.26 Of note, the SI is mostly independent of the effects of pain or anxiety, which cause a concurrent rise in heart rate and systolic blood pressure. Because the heart's left ventricular work is unchanged or may increase from the underlying catecholamine surge, the SI will be unchanged or may actually decrease.

Our study adds to the medical literature the findings that: (1) the SI may be useful as an indicator of illness severity and a triage tool in patients with no trauma but with various medical conditions, and (2) the SI showed a strong association with unplanned ICU transfer.

The main strength of our study is its case‐control design with matched controls. Also, by comparing groups from the same patient care unit, we sought to minimize the selection bias that can be inherent in case‐control studies. Limitations include the retrospective, nonrandomized study design and the fact that there may have been variations in vital sign measurements by the multiple caregivers. However, the vital signs were taken according to standard hospital practice and reflect real‐world conditions. Although generalizability may be somewhat limited because of our homogeneous patient population, our patients had a wide range of various medical illnesses, so our study should be applicable to other hospital settings, both academic and community‐based.

One of the main weaknesses of our study is that the results were not adjusted for the burden of comorbid conditions, although there were no statistically significant differences in the number of comorbid conditions among the cases and the controls (P = 0.96). Also, we did not directly compare the SI with vital signs alone to determine superiority.

The SI may be an important objective measure to help clinicians decide when patients need treatment that is more aggressive, assistance from a MET or an RRT, or a preemptive, rather than unplanned, transfer to an ICU. Although it is unlikely that a single measure will allow accurate triage of all medical or surgical patients, the SI may be a useful adjunct to clinical judgment and other objective measures in determining illness severity and clinical decline. Further prospective studies are needed to compare the role of the SI specifically with MET or RRT activation criteria, to clarify the role of comorbid conditions in unplanned transfers to the ICU, to validate the cut point for the SI in various disease states, and to assess its utility in patients with septic shock. Depending on these results, it may be beneficial to incorporate the SI into the electronic medical record as an automatic alert to identify patients at risk for ICU transfer.

Conclusions

The SI is an easily calculated composite index of heart rate and systolic blood pressure. An elevated SI of 0.85 can identify patients who are at risk for unplanned transfer to the ICU from general patient care units. Future studies will determine whether the SI is more accurate than simple vital signs as an indicator of clinical decline. If so, it may be useful as a trigger to activate METs or RRTs for treatment.

Public health concerns such as the aging population¹ and the increasing prevalence of obesity² are also important issues to hospitals. However, little attention has been given to the interface of obesity and the elderly, largely due to the dearth of studies that include elderly patients. An aging population leads to an increase in geriatric syndromes, such as osteoporosis³ and its most devastating complication, hip fracture.⁴ These frail, hip‐fracture patients pose management challenges to practicing geriatricians and hospitalists.^5,6 Furthermore, although fracture risk is inversely correlated to body mass index (BMI),⁷ this relationship has yet to be fully examined in the postoperative hip‐fracture population. In other surgical settings, there is disagreement as to whether underweight or obese patients are at higher risk of developing medical complications,^8‐11 but for orthopedic patients, data have been limited to elective orthopedic populations.^12‐14 We previously demonstrated that underweight hip‐fracture patients are at higher risk of postoperative cardiac complications at 1 year,¹⁵ consistent with studies of cardiac risk indices determining long‐term events. Because of different pathophysiologic mechanisms, the purpose of this study was to ascertain the influence of BMI on inpatient postoperative noncardiac medical complications and to assess predictors of such complications following urgent hip fracture repair.

Patients and Methods

All Olmsted County, Minnesota, residents undergoing urgent hip repair due to fracture were identified using the Rochester Epidemiology Project, a medical‐record linkage system funded by the Federal government since 1966 to support disease‐related epidemiology studies.¹⁶ All patient medical care is indexed, and both inpatient and outpatient visits are captured and available for review, allowing for complete case ascertainment. Medical care in Olmsted County is primarily provided by Mayo Clinic with its affiliated hospitals (St. Mary's and Rochester Methodist) and the Olmsted Medical Center, in addition to a few individual providers. Over 95% of all Olmsted County hip fracture surgeries are ultimately managed at St. Mary's Hospital.

Following approval by the Institutional Review Board we used this unique data resource to identify all residents with an International Classification of Diseases, 9th edition (ICD‐9) diagnosis code of 820 to 829 for hip fracture (n = 1310). Both sexes were included, and all patients included in the study provided research authorization for use of their medical records for research purposes.¹⁷ We excluded patients who were managed conservatively (n = 56), had a pathological fracture (n = 20), had multiple injuries (n = 19), were operated on >72 hours after fracture (n = 5), were aged <65 years (n = 2), or were admitted for reasons other than a fracture and experienced an in‐hospital fracture (n = 3). We subsequently excluded patients with missing information (n = 10). World Health Organization (WHO) criteria were used for classifying BMI: underweight (BMI < 18.5); normal (BMI = 18.5‐24.9); overweight (BMI = 25.0‐29.9); and obese (BMI 30.0).¹⁸

All data were abstracted using standardized collection forms by trained nurse abstractors blinded to the study hypothesis. Patients' admission height and weight were documented; if unavailable, the nearest data within 2 months prior to surgery were recorded. Patients' preadmission residence, functional status, baseline comorbidities, admission medications, discharge destination, as well as whether patients had an intensive care unit stay or any major surgeries in the past 90 days were abstracted. In addition, American Society of Anesthesia (ASA) class, type of anesthesia, and length of stay were also obtained. Inpatient complications that had been identified by the treating physicians and documented in the medical record or identified on imaging studies were assessed from the time of hip fracture repair to the time of discharge using standardized clinical criteria (Table 1). For criteria that were based on either objective findings or clinical documentation/suspicion, the patient was considered to meet the criteria of having a complication if they fulfilled either one. We did not include any cardiac outcomes, including congestive heart failure, angina, myocardial infarction, or arrhythmias that had been previously reported.¹⁵ Noncardiac complications were classified broadly: respiratory (respiratory failure, respiratory depression, or pulmonary hypoxemia); neurologic (any cerebral event including hemorrhagic or ischemic stroke, transient ischemic attack, or delirium); gastrointestinal (ileus or gastrointestinal bleeding); vascular (pulmonary embolus, or deep vein thrombosis); infectious (pneumonia, sepsis, urinary tract, wound, or cellulitis); renal/metabolic (acute renal failure, dehydration, or electrolyte abnormalities); or other (fractures or falls).

Continuous data are presented as means standard deviation and categorical data as counts and percentages. In testing for differences in patient demographics, past medical history, and baseline clinical data among BMI groups, Kruskal‐Wallis tests were performed for continuous variables and Fisher's Exact or Cochran‐Mantel‐Haenszel tests were used for discrete variables. Bonferroni adjustments were performed where appropriate. The primary outcome was the risk of any noncardiac medical complication during the postoperative hospitalization, based on patients with complications. Incidence rates were calculated for the overall group as well as for each BMI category. BMI was evaluated categorically according to the WHO criteria, as a continuous variable dichotomized as a BMI 18.5 kg/m² to 24.9 kg/m² (normal) vs. all others, and above/below 25.0 kg/m². The effect of BMI and other potential risk factors on the complication rate was evaluated using logistic regression. The effect of BMI category on the overall complication rate was adjusted for the a priori risk factors of age, sex, surgical year, and ASA class both univariately (Model 1) and multivariately (Model 2). In addition to these variables, we also evaluated other potential risk factors, including baseline demographic and baseline clinical variables that were significant (P < 0.05) univariately using a stepwise selection; first forcing in BMI as a categorical variable (Model 3), then repeating the stepwise selection process without forcing in BMI (Model 4). Using data from Lawrence et al.,¹⁹ we estimated that we would have 80% power to detect differences in rates of inpatient noncardiac complications equal to an odds ratio (OR) = 2.2 (normal vs. underweight), OR = 2.0 (normal vs. overweight), and OR = 2.4 (normal vs. obese). Finally, because of power considerations, as an exploratory analysis, we additionally identified predictors of inpatient complications within each BMI category using stepwise selection. All statistical tests were 2‐sided, and P values <0.05 were considered significant. All analyses were performed using SAS for UNIX (version 9.1.3; SAS Institute, Inc., Cary, NC).

Results

Between 1988 and 2002, 1195 urgent repairs for hip fracture met our inclusion/exclusion criteria. We subsequently excluded 15 repairs with missing BMI data, and, of the 7 patients with >1 repair, we included only their first fracture episode in our analysis. Two were subsequently excluded due to an administrative error. Ultimately, 1180 hip fracture repairs were included in the analysis cohort. There were 184 (15.6%) patients in the underweight group, 640 (54.2%) with normal BMI, 251 (21.3%) with a BMI 25.0 to 29.9 kg/m², and 105 (8.9%) with a BMI 30 kg/m². Baseline characteristics are otherwise shown in Table 2. Normal BMI patients were significantly older than the other groups, and underweight patients were less likely to be admitted from home. Past history of having a cardiovascular risk factor or a cardiovascular diagnosis appeared to increase with increasing BMI. Underweight patients were more likely to have chronic obstructive pulmonary disease (COPD) than patients with normal BMI (P = 0.03) or overweight patients (P = 0.009), but not more than obese patients (P = 0.21). There were no differences across BMI groups in ASA class, type of anesthesia, intensive care unit stay, or length of stay.

There were 77 (41.8%) postoperative inpatient noncardiac complications in the underweight group, 234 (36.6%) in the normal BMI group, 90 (35.9%) in the overweight group, and 42 (40.0%) in the obese group (P = 0.49). Figure 1 demonstrates the main subcategory complication rates by BMI group, and Table 3 outlines the univariate unadjusted complication rates. Other than gastrointestinal complications being more prevalent as BMI increases (P = 0.005), there were no significant differences in crude complication rates across BMI categories (all P > 0.05) for the other complication subcategories. A multiple comparisons analysis did not demonstrate any differences between normal and any of the other BMI categories for ileus. Normal BMI patients were more likely to be discharged to a nursing facility than overweight or obese patients (85.5% vs. 79.3%, P = 0.03; and 85.5% vs. 79.0%, P = 0.03, respectively). The proportion of in‐hospital deaths among underweight patients was significantly higher than in any of the other groups (9.3% vs. 4.4%; P = 0.01), but mean length of stay was not significantly different.

Figure 1

Significant univariate predictors of the composite outcome of any noncardiac complication included: age (OR, 1.04 95% confidence interval [CI>], 1.02‐1.06; P < 0.001), age 75 years (OR, 2.25; 95% CI, 1.52‐3.33; P < 0.001), age 85 years (OR, 1.49; 95% CI, 1.17‐1.89; P < 0.001), male sex (OR, 1.41; 95% CI, 1.05‐1.90; P = 0.02), admission from home (OR, 0.77; 95% CI, 0.61‐0.98; P = 0.03), a history of cerebrovascular disease (OR, 1.41; 95% CI, 1.08‐1.83; P = 0.01), myocardial infarction (OR, 1.41; 95% CI, 1.07‐1.86; P = 0.02), angina (OR, 1.32; 95% CI, 1.03‐1.69; P = 0.03), congestive heart failure (OR, 1.45; 95% CI, 1.11‐1.89; P = 0.006), dementia (OR, 1.39; 95% CI, 1.08‐1.78; P = 0.01), peripheral vascular disease (OR, 1.47; 95% CI, 1.06‐2.03; P = 0.02), COPD/asthma (OR, 1.56; 95% CI, 1.18‐2.08; P = 0.002), osteoarthritis (OR, 1.29; 95% CI, 1.01‐1.65; P = 0.04), code status as Do Not Resuscitate (OR, 0.74; 95% CI, 0.58‐0.94; P = 0.015), or ASA class III‐V (OR, 2.24; 95% CI, 1.53‐3.29; P < 0.001). Results were no different after using the Charlson comorbidity index in place of ASA class (data not shown). No significant differences in overall noncardiac complications were observed when examining BMI as a continuous variable, as a categorical variable, as 25 kg/m² vs. <25 kg/m², or as 18.5 kg/m² to 24.9 kg/m² vs. all others. Examining renal, respiratory, peripheral vascular, or neurologic complications univariately within these aforementioned strata also did not demonstrate any significant differences among BMI categories (data not shown).

Multivariable analyses (Models 1‐4) are shown for any overall noncardiac inpatient medical complication in Table 4. BMI was not a significant predictor in any of our models, specifically in our main model that examined the effect of BMI adjusting for a priori variables (Model 2). However, older age, male sex, and ASA class were highly significant predictors of complications in all four models; however, surgical year was nonsignificant. Notably, after stepwise selection for other demographic and premorbid variables, a history of COPD or asthma was found to be an additional significant factor both in Model 3 (forcing BMI in the model) and Model 4 (without BMI in the model). Exploratory analysis of individual predictors of inpatient noncardiac complications within each BMI category demonstrated that, in underweight patients, admission use of ‐blockers was a significant predictor of having any medical complication (OR, 3.1; 95% CI, 1.1‐8.60; P = 0.03). In normal BMI patients, age 75 years (OR, 2.6; 95% CI, 1.4‐4.9; P = 0.003), ASA class III‐V (OR, 2.3; 95% CI, 1.3‐3.9; P = 0.003), and a history of cerebrovascular disease (OR, 1.5; 95%CI, 1.04‐2.1; P = 0.03) were predictors; and, in obese patients, only age (OR, 1.1; 95% CI, 1.00‐1.12; P = 0.05) was significant. There were no significant predictors of having a medical complication in the overweight group.

Discussion

Most research describing the association of BMI with postoperative outcomes has concentrated on cardiac surgery, general surgical procedures, and intensive care unit utilization.^8‐11,20 In the orthopedic literature, an elevated BMI has been associated with a higher number of short‐term complications, but this was limited to elective knee arthroplasty and spine surgery populations.^12,13,21 Conversely, no differences were observed in obese patients undergoing hip arthroplasties.^14,22 To the best of our knowledge, this study may be the first to examine the impact of BMI on inpatient hospital outcomes following urgent hip fracture repair. Our results suggest the risk of developing a noncardiac medical complication is the same regardless of BMI.

Our overall complication rate was higher (38%) than previous reports by others.^19,23‐26 Thus, Lawrence et al.,¹⁹ in their retrospective study of 20 facilities, demonstrated an overall complication rate of 17%, even though they also included postoperative cardiac complications. Although their study period overlapped our own (1982‐1993), they additionally included patients aged 60 to 65 years, a population known to have fewer comorbidities and fewer postoperative complications than the elderly hip‐fracture patients studied here. In addition, their population may have been healthier at baseline, in that a higher proportion lived at home (73%) and a lower percentage were ASA class III‐V (71%) than our cohort. These differences in baseline characteristics may explain the higher complication rates observed in our study.

Our findings did not suggest any relationship of BMI with noncardiac postoperative medical complications in any of the 4 methods we used to stratify BMI (continuous, categorical, normal vs. abnormal, and 25 kg/m²). Evidence is contradictory as to what the effect of BMI has on postoperative complications. An elevated BMI (30 kg/m²) has been shown to lead to increased sternal wound infection and saphenous vein harvest infection in a cardiac surgery population,²⁷ but other studies^10,28,29 have demonstrated the opposite effect. Among 6336 patients undergoing elective general surgery procedures, the incidence of complications were similar by body mass.³⁰ A matched study design that included urgent and emergent surgeries also did not find any appreciable increased perioperative risk in noncardiac surgery.²⁸ Whether this may be due to the elective nature of the surgeries in these studies, hence leading to selection bias, is unknown.

In geriatric patients, multiple baseline comorbid conditions often are reflected in a higher ASA class, which increases the risk of significant perioperative complications. Our multivariate modeling showed that a high ASA class strongly predicts morbidity and mortality following hip fracture repair, in line with other studies.^19,31,32 Although the Charlson comorbidity index could alternatively been used, we elected to adjust for ASA class as it is more commonly used and is simple to use. Interestingly, surgical year did not significantly predict any complication, which can suggest that practice changes play a minimal impact on patient outcomes. However, we caution that because the individual event rates, particularly vascular, were low, we were unable to fully determine whether changes in practice management, such as improved thromboprophylaxis, would impact event rates over time. Finally, other predictors such as older age³³ and a concomitant history of either COPD or asthma,³⁴ are well‐accepted predictors of inpatient complications. Our attempt to examine specific predictors of complications in each BMI category revealed differing results, making interpretations difficult. Because of power considerations, this was meant solely as an exploratory analysis, and larger cohorts are needed to further ascertain whether predictors are different in these groups. Such a study may in fact identify perioperative issues that allow practitioners caring for this population to modify these factors.

One of the major limitations in our study was our inability to adjust for individual complications using multivariable models, such as deep vein thrombosis or delirium, within each BMI stratum, because of statistical power issues. Such a study would require large numbers of individual complications or events to allow for appropriate adjustments. The authors acknowledge that such individual complication rates may vary dramatically. We were aware of this potential problem, and therefore a priori ascertained a composite outcome of any noncardiac medical complication. However, our results do provide preliminary information regarding the impact of BMI on noncardiac medical complications. Further studies would be needed, though, to fully determine the effect of BMI on the number of cases with each complication.

Obesity (or BMI) is a known cardiovascular risk factor, and our previous study's aim was to determine cardiovascular events in a comparable manner to the way risk indices, such as the Goldman, Lee, or the AHA preoperative algorithm function. The surgical literature often presents noncardiac complications separately, allowing us to directly compare our own data to other published studies. We used 2 separate approaches, focusing on the inpatient stay (ascertaining noncardiac complications) and 1‐year cardiac outcomes (cardiac complications), as these are mediated by different mechanisms and factors. Furthermore, the intent of both studies was to dispel any concerns that an elevated BMI would in fact lead to an increased number of complications. Whether cardiac complications, though, would impact noncardiac complications, or vice‐versa, is unknown, and would require further investigation.

Although we relied on well‐established definitions for body mass, they have often been challenged, as they may underestimate adiposity in the elderly population due to age‐related reductions in lean mass.^35,36 Studies have demonstrated a poor correlation between percent body fat and BMI in the >65 year age group,³⁷ which could impact our results and outcomes by misclassifying patients. Yet, as an anthropometric measurement, BMI is easily obtainable and its variables are routinely documented in patients' medical records, as compared to other anthropometric measurements. Other means of estimating adiposity, such as densitometry or computed tomography (CT) scanning, are impractical, expensive, and not used clinically but routinely in research settings. The lack of standardization in obtaining height and weight, despite nurse‐initiated protocols for bed calibration, may have introduced a degree of measurement bias. Furthermore, the extent of lean mass lost and volume status changes lead to further challenges of using BMI in hospital settings. Whether other anthropometric measurements, including hip circumference, waist circumference, or waist‐hip ratio, should be used in this group of patients requires further examination. However, despite its shortcomings in elderly patients, BMI is still deemed an appropriate surrogate for obesity.

Our main strength was the use of the Rochester Epidemiology Project medical record linkage system to ascertain all patient data. This focuses on patients from a single geographically‐defined community minimizing referral biases often observed in studies originating from a tertiary care referral center. Previous disease‐related epidemiology studies using the Olmsted County population have demonstrated excellent external validity to the U.S. white population.¹⁶ We relied on the medical documentation of the treating clinician for many diagnoses in our data abstraction. Although we attempted to use standardized definitions, clinicians may have inadvertently forgotten to document subjective signs or symptoms that would assist in the categorization of these complications. Hence, added inpatient complications may have been overlooked, suggesting that our results may slightly underestimate the true incidence in this population. Additionally, certain complications may overlap categories, such as pneumonia and infections. We agree with Lawrence et al.¹⁹ that long periods of time are necessary to accumulate data of this kind in an effort to describe complication rates epidemiologically.

Despite no difference in outcomes among BMI categories, our results have striking implications for the hospitalized patient. Thus, underweight elderly patients, often considered frail with minimal functional reserve, are at no higher risk for developing inpatient medical complications than patients with higher BMIs. This is contrary to our study focusing on cardiac complications, where underweight patients were at higher risk.¹⁵ Conversely, obese patients, who have been demonstrated to be at higher risk of medical complications (particularly pulmonary), had no greater risk than patients with normal BMI. To the practicing geriatrician and hospitalist, this information provides important prognostication regarding additional perioperative measures that need to be implemented in these different groups. Based on our results, BMI does not play a particular role in noncardiac medical complications, dispelling any myths of the added burden of excess weight on surgical outcomes in this population. From a hospital perspective, this may be important since additional testing or preventative management in these patients may lead to additional resource use. However, in‐hospital deaths were higher in underweight patients than in patients with a normal BMI. Although we were underpowered to detect any differences in mortality between groups and could therefore not adjust for additional variables, it is unknown whether cardiac or noncardiac complications may be a stronger predictor of death in the underweight patient population. Further studies would be needed to better ascertain this relationship.

Conclusions

In elderly patients undergoing urgent hip fracture repair, BMI does not appear to lead to an excess rate of inpatient noncardiac complications. Our results are the first to demonstrate that BMI has no impact on morbidity in this patient population. Further research on the influence of body composition on inpatient complications in this population is needed to accurately allow for appropriate perioperative prophylaxis. Whether BMI impacts specific complications or in‐patient mortality in this population still requires investigation.