Research
Original Investigation | SURGICAL CARE OF THE AGING POPULATION
The Association Between Hospital Care Intensity and Surgical Outcomes in Medicare Patients Kyle H. Sheetz, MD, MS; Justin B. Dimick, MD, MPH; Amir A. Ghaferi, MD, MS
IMPORTANCE Hospitals’ care intensity varies widely across the United States. Payers and
policy makers have become focused on promoting quality, low-cost, efficient health care. OBJECTIVE To evaluate whether increased hospital care intensity (HCI) is associated with improved outcomes following major surgery.
Supplemental content at jamasurgery.com CME Quiz at jamanetworkcme.com and CME Questions page 1344
DESIGN, SETTING, AND PARTICIPANTS Using national Medicare data in this retrospective cohort study, we identified 706 520 patients at 2544 hospitals who underwent 1 of 7 major cardiovascular, orthopedic, or general surgical operations. EXPOSURE The HCI Index, which is validated and publicly available through the Dartmouth Atlas of Healthcare. MAIN OUTCOMES AND MEASURES Risk- and reliability-adjusted mortality, major complication,
and failure-to-rescue rates. RESULTS Hospital care intensity varied 10-fold. High-HCI hospitals had greater rates of major complications when compared with low-HCI centers (risk ratio, 1.04; 95% CI, 1.03-1.05). There was a decrease in failure to rescue at high compared with low-HCI hospitals (risk ratio, 0.95; 95% CI, 0.94-0.97). Using multilevel-models, HCI reduced the variation in failure-to-rescue rates between hospitals by 2.7% after accounting for patient comorbidities and hospital resources. Patients treated at high-HCI hospitals had longer hospitalizations, more inpatient deaths, and lower hospice use during the last 2 years of life. CONCLUSIONS AND RELEVANCE Failure-to-rescue rates were lower at high–care intensity
hospitals. Conversely, care intensity explains a very small proportion of variation in failure-to-rescue rates across hospitals. JAMA Surg. 2014;149(12):1254-1259. doi:10.1001/jamasurg.2014.552 Published online October 1, 2014.
T
he overall intensity of medical care varies widely across the United States.1,2 Intensity, generally synonymous with aggressive treatment style, is implicated in rising health care costs. This observation is relevant during the endof-life period, where we accrue a high proportion of overall care expenditures.3 Inpatient surgical care also represents a substantial cost burden to our health care system.4 Some posit that innovative strategies to reduce variation in health care intensity and its associated inefficiencies may reduce the cost of care we provide to our sickest patients.5 Along these lines, provisions included in the Patient Protection and Affordable Care Act provide an impetus for bundling payments for health care services in an effort to reduce variation in episode costs.6 To our knowledge, most research on aggressive treatment style to date has focused on highlighting variation in the medical management of chronic disease.3,7 1254
Author Affiliations: Department of Surgery, University of Michigan, Ann Arbor. Corresponding Author: Kyle H. Sheetz, MD, MS, Center for Healthcare Outcomes and Policy, 2800 Plymouth Rd, Bldg 16, Floor 1, Ann Arbor, MI 48109 (ksheetz@med .umich.edu).
However, little is known about the relationship between hospital quality and care intensity for surgical patients. This may be particularly relevant given the acuity of surgical care and the importance of immediate decisions on patient outcomes. Previous work showed a modest outcome benefit for patients treated at high care intensity centers after general, vascular, and orthopedic surgical procedures.8 The extent to which addressing hospitals’ care intensity will modify postsurgical outcomes is unknown. Studies set in the more immediate health care reform climate indicate that high-intensity care may reduce amputation rates in patients with peripheral vascular disease.9 Variation in care intensity holds important policy and financial implications. Whether efforts to increase care intensity, particularly in the management of postoperative complications, will result in measureable outcome benefits remains unclear.10
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Hospital Care Intensity and Surgical Outcomes
We studied the relationship between a hospital’s intensity of care based on a validated index and their outcomes following 7 common major surgical procedures in Medicare beneficiaries.3 We also sought to characterize the relationship between indicators of intensive treatment style and a hospital’s access to resources used for the care of surgical patients. These data have the potential to improve our understanding of the relationship between intensity of care and surgical outcomes, with the possibility to better inform new payment structures for surgical episodes of care.
Methods Patient Population and Data Source We used data from the Medicare Provider Analysis and Review files from 2010. The Centers for Medicare and Medicaid Services maintains this database using claims submitted by hospitals where Medicare beneficiaries receive care. Patient-level data included age, sex, race/ethnicity, comorbidities (principal and secondary diagnosis codes), procedural codes, 30-day morbidity and mortality, and information on length of hospital stay. We selected patients who underwent 7 common major surgical operations using International Classification of Diseases, Ninth Revision, Clinical Modification codes. For this analysis, we included the following procedures: colectomy, lower extremity revascularization, lower extremity amputation, abdominal aortic aneurysm repair, coronary artery bypass graft, aortic valve repair, and hip fracture repair. We excluded patients younger than 65 years or those with incomplete clinical data in the registry. This study was approved by the University of Michigan institutional review board; patient consent was waived.
Hospital Care Intensity and Resource Data The Dartmouth Atlas of Healthcare maintains an extensive data repository focusing on defining and understanding the wide variation of health care resource use in the United States. Using Medicare data from patients with 1 of 9 chronic conditions (malignant cancer/leukemia, dementia, diabetes mellitus with endorgan damage, congestive heart failure, chronic pulmonary disease, peripheral vascular disease, severe chronic liver disease, coronary artery disease, and chronic renal failure), the Dartmouth Atlas generates metrics of health care intensity for beneficiaries in their last 2 years of life. For this analysis, we used the Hospital Care Intensity (HCI) Index as our primary exposure variable.3 The HCI Index is an age-, sex-, race/ethnicity–, and illness-standardized ratio of inpatient days and physician encounters. Adjustment is accomplished using ordinary least squares regression. This ratio is then normalized to the national average to provide a relative comparison of each hospital’s intensity of care. Additional metrics of care intensity were obtained using the hospital-specific data contained within the Dartmouth Atlas’ Care of Chronically Ill Patients During the Last Two Years of Life data registry for 2010. Data on hospital structure and resources were derived from the American Hospital Association Annual Survey Database. These data in-
Original Investigation Research
cluded hospital bed size and occupancy, annual surgical volumes, and staffing (full-time–equivalent nursing and technician support).
Outcomes Our primary outcomes for this analysis were 30-day mortality, major complications, and failure to rescue. Major complications were identified by International Classification of Diseases, Ninth Revision, Clinical Modification codes for the following occurrence categories: pulmonary failure (518.81, 518.4, 518.5, and 518.8), pneumonia (481, 482.0-482.9, 483, 484, 485, and 507.0), myocardial infarction (410.00-410.91), deep venous thrombosis/pulmonary embolism (415.1, 451.11, 451.19, 451.2, 451.81, and 453.8), renal failure (584), surgical site infection (958.3, 998.3, 998.5, 998.59, and 998.51), gastrointestinal bleeding (530.82, 531.00-531.21, 531.40, 531.41, 531.60, 531.61, 532.00-532.21, 532.40, 532.41, 532.60, 532.61, 533.00533.21, 533.40, 533.41, 533.60, 533.61, 534.00-534.21, 534.40, 534.41, 534.60, 534.61, 535.01, 535.11, 535.21, 535.31, 535.41, 535.51, 535.61, and 578.9), and hemorrhage (998.1). Overall complication rates were consistent with previously published work using similar patient populations and data sets. We defined failure to rescue as mortality in patients with at least 1 major complication (ie, the case fatality rate for patients sustaining a major complication), as has been previously described.10
Statistical Analysis We compared demographic, comorbidity, and operative differences between hospitals with the t test, χ2 test, and Fisher exact test, as appropriate. We compared differences between HCI as a continuous variable and postoperative outcomes in bivariate analysis using Pearson correlation coefficient. Hospitals were compiled into 3 groups based on HCI: low, average, and high intensity. We stratified reporting of all adjusted outcomes by these categories to provide a generalizable comparison of hospitals based on their relative care intensity. We constructed 3 separate logistic regression models using patient demographics, comorbidities, urgency of operation, and procedural factors to generate risk-adjusted rates of mortality, major complications, and failure to rescue for each hospital. Next, we used hierarchical logistic regression modeling to adjust all outcome rates for reliability to account for hospitallevel random effects.11 Reliability adjustment reduces statistical noise that can result from hospitals with lower surgical case volumes. The c statistic for all models ranged between 0.73 and 0.88, with good discriminatory power on the basis of the Hosmer-Lemeshow test. Outcome rates for low–, average–, and high–care intensity hospitals were calculated using the individual hospital’s risk- and reliability-adjusted rates.11 We then calculated risk ratios (RRs) and 95% CIs for mortality, major complications, and failure to rescue using low care intensity hospitals as a reference category. For each outcome, we conducted model testing to determine the relative contribution of patient-level covariates, hospital structural factors, and hospital care intensity to the variation observed among hospitals. We first quantified the variance ascribed to hospital-level random effects using an empty mixed-effects logistic regression model (xtmelogit in Stata ver-
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Research Original Investigation
Hospital Care Intensity and Surgical Outcomes
Table 1. Patient and Hospital Characteristics by Hospital Care Intensity Hospital Care Intensity Characteristic
Low (n = 239 449)
Average (n = 232 173)
High (n = 234 898) 75
Patient characteristics Age, median, y
75
74
Male, %
51.0
51.4
50.2
Nonwhite race/ ethnicity, %
10.8
15.0
20.7
5.2
4.8
4.3
Coexisting conditions, % Obesity Hypertension
57.8
59.6
59.2
Congestive heart failure
8.3
8.2
8.3
Pulmonary circulatory disease
1.5
1.5
1.5
Peripheral vascular disease
19.0
20.3
20.4
Diabetes mellitus
24.4
25.9
25.7
Liver disease
5.7
6.3
6.7
Metastatic cancer
3.0
2.9
3.0
Coagulopathy
6.5
7.0
7.2
Preoperative weight loss
6.2
6.1
5.9
Hospital characteristics Average daily census, No. Nurse to patient ratio, mean
191
190
231
3.3
4.1
3
18.5
30.6
36.3
Inpatient
2708.4
3937.9
4442.6
Outpatient
4832.2
6065.5
6586.7
318
473
537
Average total ICU beds, No. No. of surgical procedures/y
Full-time equivalent Registered nurses Radiologic technicians
24.9
36.2
38.9
Laboratory technicians
20.7
33.9
34.5
Respiratory technicians
13.5
21.1
33.6
62.1
62.3
59.5
Electronic medical record, %
Abbreviation: ICU, intensive care unit.
Results Hospital Care Intensity, Hospital Structure, and Patient Demographics Hospital care intensity varied widely across hospitals from 0.35 to 3.41. Consistent with its characteristic as a normalized ratio, the mean (1.04) and median (0.98) HCI for the entire cohort of 2544 hospitals contributing to this analysis was near 1.0. As expected, low–care intensity hospitals had a significantly lower HCI than high–care intensity hospitals (0.74 vs 1.41; P < .001). We identified 706 520 patients who underwent 1 of 7 designated procedures. Patient characteristics did not differ significantly across low–, average–, and high–care intensity hospitals with these 7 operations (Table 1). The single exception to this was nonwhite race, which was nearly twice as high at high- compared with low-intensity hospitals (P < .001). In contrast, hospital structural factors were significantly different across categories of care intensity. For example, high– care intensity hospitals had significantly greater average daily censuses, inpatient surgical case volumes, and full-time– equivalent technical support (P < .001 for all). Procedural volumes for the entire cohort were as follows: colectomy (n = 121 560), lower extremity revascularization (n = 169 930), lower extremity amputation (n = 48 658), abdominal aortic aneurysm repair (n = 41 327), coronary artery bypass graft (n = 128 510), aortic valve repair (n = 46 388), and hip fracture repair (n = 150 147). We found no differences in the overall procedural mix across low–, average–, and high–care intensity hospitals. The median length of stay for all patients was 6 days (interquartile range, 7 days).
Hospital Care Intensity and Surgical Outcomes
sion 12.1). We subsequently generated linear predictors of the outcome in question using patient-level covariates first. We sequentially added hospital structural factors and HCI, each time generating a new linear predictor. The relative decrease in variance attributed to hospital-level random effects was then calculated to determine each parameter’s influence on observed variation. Finally, we compared alternative metrics for HCI across low-, average-, and high-intensity centers, as identified by the HCI Index. We used the t test and Mann-Whitney U test to compare differences between high– and low–care intensity hospitals. We also conducted a sensitivity analysis using 2 alternative proxies for intensity of care (average inpatient Medicare spending and percentage of deaths occurring in the hospital). These metrics are compiled for Medicare ben1256
eficiaries with chronic illness, as just defined, in the last year of life. Hospitals were similarly stratified into categories of low, average, and high care intensity and outcomes were compared in an identical fashion, as just described using our primary exposure, HCI. A significance level of α = .05 was used. All statistical analyses were performed using Stata statistical software version 12.1.
The unadjusted 30-day mortality rate for the entire patient cohort was 6.4%. We observed the lowest unadjusted mortality after coronary artery bypass grafting (3.8%) and the highest after lower extremity amputation (9.8%). The unadjusted major complication rate for all patients was 31.2%. The lowest morbidity rates were observed after coronary artery bypass grafting (18.8%), whereas the highest rates were observed after hip fracture repair (44.8%). We first assessed the relationship between HCI and postoperative outcomes in bivariate analysis. With HCI treated as a continuous variable, we assessed the linear correlation between HCI and mortality (r = 0.024; P = .22), major complications (r = 0.163; P < .001), and failure to rescue (r = −0.052; P = .008). These results are graphically displayed in scatterplot format (eFigure in the Supplement). Hospital care intensity was significantly associated with major complications and failure to rescue. However, the strength of this relationship was
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Hospital Care Intensity and Surgical Outcomes
Original Investigation Research
Table 2. Surgical Outcomes by Hospital Care Intensity Hospital Care Intensity Low
Average RR (95% CI)
Rate, %
High RR (95% CI)
Rate, %
RR (95% CI)
Outcome
Rate, %
Morbidity
31.0
1 [Reference]
31.3
1.01 (1.00-1.02)
32.1
1.04 (1.03-1.05)
Mortality
6.5
1 [Reference]
6.5
1.00 (0.99-1.01)
6.4
0.99 (0.98-1.00)
14.2
1 [Reference]
14.2
1.01 (1.00-1.02)
13.5
0.95 (0.94-0.97)
Failure to rescue Abbreviation: RR, risk ratio.
Table 3. Model Testing for Postoperative Outcomes Model Parametersa Positive Outcome
Empty Model
Predicted Risk
Hospital Resources
Hospital Care Intensity
Morbidity Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.349 NA
0.299 14.3
0.291 16.6
0.288 17.5
Mortality Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.324 NA
0.252 22.2
0.225 30.6
0.225 30.6
Failure to rescue Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.33 NA
0.307 7.0
weakly positive for major complications and very weakly negative for failure to rescue. Next, we assessed the relationship between HCI and outcomes in multivariate analysis. Risk- and reliabilityadjusted mortality rates for low–, average–, and high–care intensity hospitals are reported in Table 2. There were no differences in postoperative mortality across low–, average–, or high–care intensity hospitals. We observed a small, but statistically significant, increase in major complication rates for patients who underwent surgery at high– vs low–care intensity hospitals (RR, 1.04; 95% CI, 1.03-1.05). In contrast, patients who underwent operations at high–care intensity centers were 5% less likely to die in the setting of a major complication (failure to rescue) (RR, 0.95; 95% CI, 0.940.97). We repeated all analyses within each of the 7 distinct procedural categories and obtained similar outcomes (eTable in the Supplement). Furthermore, we conducted a sensitivity analysis using average inpatient Medicare spending and percentage of deaths occurring in the hospital as alternative proxies for HCI. When stratifying centers by these variables, we obtained nearly identical results, indicating a high level of correlation between the HCI summary measure and other putative surrogates for intensive treatment styles. The results of model testing indicated that the addition of HCI to the multivariate model minimally reduced the magnitude of between-hospital variation in outcomes (Table 3). For example, the addition of HCI to the multivariate model reduced the magnitude of variation in failure-to-rescue rates between hospitals by 18.2% (patient and hospital structural factors) to 20.9% (including HCI).
0.270 18.2
0.261 20.9
Abbreviation: NA, not applicable. a
Model parameters are added cumulatively.
Factors Associated With Hospital Care Intensity Finally, we evaluated the relationship between HCI and other measures of care intensity and resource use. We compared overall differences in several metrics across low–, average–, and high–intensity hospitals as designated by HCI (Table 4). All outcomes were calculated for Medicare beneficiaries in the last 2 years of life. In general, we observed significantly higher overall and inpatient Medicare spending. Patients treated at high– care intensity hospitals had more physician contact and, on average, spent more days in the hospital and intensive care unit. In contrast, patients treated at high–care intensity hospitals were less likely to be enrolled in hospice and spent fewer days in hospice when compared with low–care intensity hospitals.
Discussion While previous studies have shown wide variation in the intensity of medical care provided by hospitals, to our knowledge, few have explicitly addressed the relationship between aggressive treatment style and patient outcomes with surgery. We investigated the relationship between HCI and outcomes in Medicare beneficiaries after 7 common operations. We observed a small, but statistically significant, increase in the rates of major complications for patients treated at high– compared with low–care intensity centers. In contrast, failureto-rescue rates were lower at high–care intensity hospitals, potentially indicating differences in complication management compared with low–care intensity centers. Despite this, HCI explained a small proportion of the overall variation in failure-
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Research Original Investigation
Hospital Care Intensity and Surgical Outcomes
Table 4. Alternative Metrics for Hospital Care Intensity Hospital Care Intensity Characteristica
Low
Average
High
P Value for Low vs High
Total
65 008.00
75 024.00
94 789.00
Original Investigation | SURGICAL CARE OF THE AGING POPULATION
The Association Between Hospital Care Intensity and Surgical Outcomes in Medicare Patients Kyle H. Sheetz, MD, MS; Justin B. Dimick, MD, MPH; Amir A. Ghaferi, MD, MS
IMPORTANCE Hospitals’ care intensity varies widely across the United States. Payers and
policy makers have become focused on promoting quality, low-cost, efficient health care. OBJECTIVE To evaluate whether increased hospital care intensity (HCI) is associated with improved outcomes following major surgery.
Supplemental content at jamasurgery.com CME Quiz at jamanetworkcme.com and CME Questions page 1344
DESIGN, SETTING, AND PARTICIPANTS Using national Medicare data in this retrospective cohort study, we identified 706 520 patients at 2544 hospitals who underwent 1 of 7 major cardiovascular, orthopedic, or general surgical operations. EXPOSURE The HCI Index, which is validated and publicly available through the Dartmouth Atlas of Healthcare. MAIN OUTCOMES AND MEASURES Risk- and reliability-adjusted mortality, major complication,
and failure-to-rescue rates. RESULTS Hospital care intensity varied 10-fold. High-HCI hospitals had greater rates of major complications when compared with low-HCI centers (risk ratio, 1.04; 95% CI, 1.03-1.05). There was a decrease in failure to rescue at high compared with low-HCI hospitals (risk ratio, 0.95; 95% CI, 0.94-0.97). Using multilevel-models, HCI reduced the variation in failure-to-rescue rates between hospitals by 2.7% after accounting for patient comorbidities and hospital resources. Patients treated at high-HCI hospitals had longer hospitalizations, more inpatient deaths, and lower hospice use during the last 2 years of life. CONCLUSIONS AND RELEVANCE Failure-to-rescue rates were lower at high–care intensity
hospitals. Conversely, care intensity explains a very small proportion of variation in failure-to-rescue rates across hospitals. JAMA Surg. 2014;149(12):1254-1259. doi:10.1001/jamasurg.2014.552 Published online October 1, 2014.
T
he overall intensity of medical care varies widely across the United States.1,2 Intensity, generally synonymous with aggressive treatment style, is implicated in rising health care costs. This observation is relevant during the endof-life period, where we accrue a high proportion of overall care expenditures.3 Inpatient surgical care also represents a substantial cost burden to our health care system.4 Some posit that innovative strategies to reduce variation in health care intensity and its associated inefficiencies may reduce the cost of care we provide to our sickest patients.5 Along these lines, provisions included in the Patient Protection and Affordable Care Act provide an impetus for bundling payments for health care services in an effort to reduce variation in episode costs.6 To our knowledge, most research on aggressive treatment style to date has focused on highlighting variation in the medical management of chronic disease.3,7 1254
Author Affiliations: Department of Surgery, University of Michigan, Ann Arbor. Corresponding Author: Kyle H. Sheetz, MD, MS, Center for Healthcare Outcomes and Policy, 2800 Plymouth Rd, Bldg 16, Floor 1, Ann Arbor, MI 48109 (ksheetz@med .umich.edu).
However, little is known about the relationship between hospital quality and care intensity for surgical patients. This may be particularly relevant given the acuity of surgical care and the importance of immediate decisions on patient outcomes. Previous work showed a modest outcome benefit for patients treated at high care intensity centers after general, vascular, and orthopedic surgical procedures.8 The extent to which addressing hospitals’ care intensity will modify postsurgical outcomes is unknown. Studies set in the more immediate health care reform climate indicate that high-intensity care may reduce amputation rates in patients with peripheral vascular disease.9 Variation in care intensity holds important policy and financial implications. Whether efforts to increase care intensity, particularly in the management of postoperative complications, will result in measureable outcome benefits remains unclear.10
JAMA Surgery December 2014 Volume 149, Number 12
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archsurg.jamanetwork.com/ by a University of Michigan User on 04/19/2015
jamasurgery.com
Hospital Care Intensity and Surgical Outcomes
We studied the relationship between a hospital’s intensity of care based on a validated index and their outcomes following 7 common major surgical procedures in Medicare beneficiaries.3 We also sought to characterize the relationship between indicators of intensive treatment style and a hospital’s access to resources used for the care of surgical patients. These data have the potential to improve our understanding of the relationship between intensity of care and surgical outcomes, with the possibility to better inform new payment structures for surgical episodes of care.
Methods Patient Population and Data Source We used data from the Medicare Provider Analysis and Review files from 2010. The Centers for Medicare and Medicaid Services maintains this database using claims submitted by hospitals where Medicare beneficiaries receive care. Patient-level data included age, sex, race/ethnicity, comorbidities (principal and secondary diagnosis codes), procedural codes, 30-day morbidity and mortality, and information on length of hospital stay. We selected patients who underwent 7 common major surgical operations using International Classification of Diseases, Ninth Revision, Clinical Modification codes. For this analysis, we included the following procedures: colectomy, lower extremity revascularization, lower extremity amputation, abdominal aortic aneurysm repair, coronary artery bypass graft, aortic valve repair, and hip fracture repair. We excluded patients younger than 65 years or those with incomplete clinical data in the registry. This study was approved by the University of Michigan institutional review board; patient consent was waived.
Hospital Care Intensity and Resource Data The Dartmouth Atlas of Healthcare maintains an extensive data repository focusing on defining and understanding the wide variation of health care resource use in the United States. Using Medicare data from patients with 1 of 9 chronic conditions (malignant cancer/leukemia, dementia, diabetes mellitus with endorgan damage, congestive heart failure, chronic pulmonary disease, peripheral vascular disease, severe chronic liver disease, coronary artery disease, and chronic renal failure), the Dartmouth Atlas generates metrics of health care intensity for beneficiaries in their last 2 years of life. For this analysis, we used the Hospital Care Intensity (HCI) Index as our primary exposure variable.3 The HCI Index is an age-, sex-, race/ethnicity–, and illness-standardized ratio of inpatient days and physician encounters. Adjustment is accomplished using ordinary least squares regression. This ratio is then normalized to the national average to provide a relative comparison of each hospital’s intensity of care. Additional metrics of care intensity were obtained using the hospital-specific data contained within the Dartmouth Atlas’ Care of Chronically Ill Patients During the Last Two Years of Life data registry for 2010. Data on hospital structure and resources were derived from the American Hospital Association Annual Survey Database. These data in-
Original Investigation Research
cluded hospital bed size and occupancy, annual surgical volumes, and staffing (full-time–equivalent nursing and technician support).
Outcomes Our primary outcomes for this analysis were 30-day mortality, major complications, and failure to rescue. Major complications were identified by International Classification of Diseases, Ninth Revision, Clinical Modification codes for the following occurrence categories: pulmonary failure (518.81, 518.4, 518.5, and 518.8), pneumonia (481, 482.0-482.9, 483, 484, 485, and 507.0), myocardial infarction (410.00-410.91), deep venous thrombosis/pulmonary embolism (415.1, 451.11, 451.19, 451.2, 451.81, and 453.8), renal failure (584), surgical site infection (958.3, 998.3, 998.5, 998.59, and 998.51), gastrointestinal bleeding (530.82, 531.00-531.21, 531.40, 531.41, 531.60, 531.61, 532.00-532.21, 532.40, 532.41, 532.60, 532.61, 533.00533.21, 533.40, 533.41, 533.60, 533.61, 534.00-534.21, 534.40, 534.41, 534.60, 534.61, 535.01, 535.11, 535.21, 535.31, 535.41, 535.51, 535.61, and 578.9), and hemorrhage (998.1). Overall complication rates were consistent with previously published work using similar patient populations and data sets. We defined failure to rescue as mortality in patients with at least 1 major complication (ie, the case fatality rate for patients sustaining a major complication), as has been previously described.10
Statistical Analysis We compared demographic, comorbidity, and operative differences between hospitals with the t test, χ2 test, and Fisher exact test, as appropriate. We compared differences between HCI as a continuous variable and postoperative outcomes in bivariate analysis using Pearson correlation coefficient. Hospitals were compiled into 3 groups based on HCI: low, average, and high intensity. We stratified reporting of all adjusted outcomes by these categories to provide a generalizable comparison of hospitals based on their relative care intensity. We constructed 3 separate logistic regression models using patient demographics, comorbidities, urgency of operation, and procedural factors to generate risk-adjusted rates of mortality, major complications, and failure to rescue for each hospital. Next, we used hierarchical logistic regression modeling to adjust all outcome rates for reliability to account for hospitallevel random effects.11 Reliability adjustment reduces statistical noise that can result from hospitals with lower surgical case volumes. The c statistic for all models ranged between 0.73 and 0.88, with good discriminatory power on the basis of the Hosmer-Lemeshow test. Outcome rates for low–, average–, and high–care intensity hospitals were calculated using the individual hospital’s risk- and reliability-adjusted rates.11 We then calculated risk ratios (RRs) and 95% CIs for mortality, major complications, and failure to rescue using low care intensity hospitals as a reference category. For each outcome, we conducted model testing to determine the relative contribution of patient-level covariates, hospital structural factors, and hospital care intensity to the variation observed among hospitals. We first quantified the variance ascribed to hospital-level random effects using an empty mixed-effects logistic regression model (xtmelogit in Stata ver-
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Research Original Investigation
Hospital Care Intensity and Surgical Outcomes
Table 1. Patient and Hospital Characteristics by Hospital Care Intensity Hospital Care Intensity Characteristic
Low (n = 239 449)
Average (n = 232 173)
High (n = 234 898) 75
Patient characteristics Age, median, y
75
74
Male, %
51.0
51.4
50.2
Nonwhite race/ ethnicity, %
10.8
15.0
20.7
5.2
4.8
4.3
Coexisting conditions, % Obesity Hypertension
57.8
59.6
59.2
Congestive heart failure
8.3
8.2
8.3
Pulmonary circulatory disease
1.5
1.5
1.5
Peripheral vascular disease
19.0
20.3
20.4
Diabetes mellitus
24.4
25.9
25.7
Liver disease
5.7
6.3
6.7
Metastatic cancer
3.0
2.9
3.0
Coagulopathy
6.5
7.0
7.2
Preoperative weight loss
6.2
6.1
5.9
Hospital characteristics Average daily census, No. Nurse to patient ratio, mean
191
190
231
3.3
4.1
3
18.5
30.6
36.3
Inpatient
2708.4
3937.9
4442.6
Outpatient
4832.2
6065.5
6586.7
318
473
537
Average total ICU beds, No. No. of surgical procedures/y
Full-time equivalent Registered nurses Radiologic technicians
24.9
36.2
38.9
Laboratory technicians
20.7
33.9
34.5
Respiratory technicians
13.5
21.1
33.6
62.1
62.3
59.5
Electronic medical record, %
Abbreviation: ICU, intensive care unit.
Results Hospital Care Intensity, Hospital Structure, and Patient Demographics Hospital care intensity varied widely across hospitals from 0.35 to 3.41. Consistent with its characteristic as a normalized ratio, the mean (1.04) and median (0.98) HCI for the entire cohort of 2544 hospitals contributing to this analysis was near 1.0. As expected, low–care intensity hospitals had a significantly lower HCI than high–care intensity hospitals (0.74 vs 1.41; P < .001). We identified 706 520 patients who underwent 1 of 7 designated procedures. Patient characteristics did not differ significantly across low–, average–, and high–care intensity hospitals with these 7 operations (Table 1). The single exception to this was nonwhite race, which was nearly twice as high at high- compared with low-intensity hospitals (P < .001). In contrast, hospital structural factors were significantly different across categories of care intensity. For example, high– care intensity hospitals had significantly greater average daily censuses, inpatient surgical case volumes, and full-time– equivalent technical support (P < .001 for all). Procedural volumes for the entire cohort were as follows: colectomy (n = 121 560), lower extremity revascularization (n = 169 930), lower extremity amputation (n = 48 658), abdominal aortic aneurysm repair (n = 41 327), coronary artery bypass graft (n = 128 510), aortic valve repair (n = 46 388), and hip fracture repair (n = 150 147). We found no differences in the overall procedural mix across low–, average–, and high–care intensity hospitals. The median length of stay for all patients was 6 days (interquartile range, 7 days).
Hospital Care Intensity and Surgical Outcomes
sion 12.1). We subsequently generated linear predictors of the outcome in question using patient-level covariates first. We sequentially added hospital structural factors and HCI, each time generating a new linear predictor. The relative decrease in variance attributed to hospital-level random effects was then calculated to determine each parameter’s influence on observed variation. Finally, we compared alternative metrics for HCI across low-, average-, and high-intensity centers, as identified by the HCI Index. We used the t test and Mann-Whitney U test to compare differences between high– and low–care intensity hospitals. We also conducted a sensitivity analysis using 2 alternative proxies for intensity of care (average inpatient Medicare spending and percentage of deaths occurring in the hospital). These metrics are compiled for Medicare ben1256
eficiaries with chronic illness, as just defined, in the last year of life. Hospitals were similarly stratified into categories of low, average, and high care intensity and outcomes were compared in an identical fashion, as just described using our primary exposure, HCI. A significance level of α = .05 was used. All statistical analyses were performed using Stata statistical software version 12.1.
The unadjusted 30-day mortality rate for the entire patient cohort was 6.4%. We observed the lowest unadjusted mortality after coronary artery bypass grafting (3.8%) and the highest after lower extremity amputation (9.8%). The unadjusted major complication rate for all patients was 31.2%. The lowest morbidity rates were observed after coronary artery bypass grafting (18.8%), whereas the highest rates were observed after hip fracture repair (44.8%). We first assessed the relationship between HCI and postoperative outcomes in bivariate analysis. With HCI treated as a continuous variable, we assessed the linear correlation between HCI and mortality (r = 0.024; P = .22), major complications (r = 0.163; P < .001), and failure to rescue (r = −0.052; P = .008). These results are graphically displayed in scatterplot format (eFigure in the Supplement). Hospital care intensity was significantly associated with major complications and failure to rescue. However, the strength of this relationship was
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Hospital Care Intensity and Surgical Outcomes
Original Investigation Research
Table 2. Surgical Outcomes by Hospital Care Intensity Hospital Care Intensity Low
Average RR (95% CI)
Rate, %
High RR (95% CI)
Rate, %
RR (95% CI)
Outcome
Rate, %
Morbidity
31.0
1 [Reference]
31.3
1.01 (1.00-1.02)
32.1
1.04 (1.03-1.05)
Mortality
6.5
1 [Reference]
6.5
1.00 (0.99-1.01)
6.4
0.99 (0.98-1.00)
14.2
1 [Reference]
14.2
1.01 (1.00-1.02)
13.5
0.95 (0.94-0.97)
Failure to rescue Abbreviation: RR, risk ratio.
Table 3. Model Testing for Postoperative Outcomes Model Parametersa Positive Outcome
Empty Model
Predicted Risk
Hospital Resources
Hospital Care Intensity
Morbidity Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.349 NA
0.299 14.3
0.291 16.6
0.288 17.5
Mortality Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.324 NA
0.252 22.2
0.225 30.6
0.225 30.6
Failure to rescue Covariance parameter, SD of intercepts Reduction in hospital variation, %
0.33 NA
0.307 7.0
weakly positive for major complications and very weakly negative for failure to rescue. Next, we assessed the relationship between HCI and outcomes in multivariate analysis. Risk- and reliabilityadjusted mortality rates for low–, average–, and high–care intensity hospitals are reported in Table 2. There were no differences in postoperative mortality across low–, average–, or high–care intensity hospitals. We observed a small, but statistically significant, increase in major complication rates for patients who underwent surgery at high– vs low–care intensity hospitals (RR, 1.04; 95% CI, 1.03-1.05). In contrast, patients who underwent operations at high–care intensity centers were 5% less likely to die in the setting of a major complication (failure to rescue) (RR, 0.95; 95% CI, 0.940.97). We repeated all analyses within each of the 7 distinct procedural categories and obtained similar outcomes (eTable in the Supplement). Furthermore, we conducted a sensitivity analysis using average inpatient Medicare spending and percentage of deaths occurring in the hospital as alternative proxies for HCI. When stratifying centers by these variables, we obtained nearly identical results, indicating a high level of correlation between the HCI summary measure and other putative surrogates for intensive treatment styles. The results of model testing indicated that the addition of HCI to the multivariate model minimally reduced the magnitude of between-hospital variation in outcomes (Table 3). For example, the addition of HCI to the multivariate model reduced the magnitude of variation in failure-to-rescue rates between hospitals by 18.2% (patient and hospital structural factors) to 20.9% (including HCI).
0.270 18.2
0.261 20.9
Abbreviation: NA, not applicable. a
Model parameters are added cumulatively.
Factors Associated With Hospital Care Intensity Finally, we evaluated the relationship between HCI and other measures of care intensity and resource use. We compared overall differences in several metrics across low–, average–, and high–intensity hospitals as designated by HCI (Table 4). All outcomes were calculated for Medicare beneficiaries in the last 2 years of life. In general, we observed significantly higher overall and inpatient Medicare spending. Patients treated at high– care intensity hospitals had more physician contact and, on average, spent more days in the hospital and intensive care unit. In contrast, patients treated at high–care intensity hospitals were less likely to be enrolled in hospice and spent fewer days in hospice when compared with low–care intensity hospitals.
Discussion While previous studies have shown wide variation in the intensity of medical care provided by hospitals, to our knowledge, few have explicitly addressed the relationship between aggressive treatment style and patient outcomes with surgery. We investigated the relationship between HCI and outcomes in Medicare beneficiaries after 7 common operations. We observed a small, but statistically significant, increase in the rates of major complications for patients treated at high– compared with low–care intensity centers. In contrast, failureto-rescue rates were lower at high–care intensity hospitals, potentially indicating differences in complication management compared with low–care intensity centers. Despite this, HCI explained a small proportion of the overall variation in failure-
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Research Original Investigation
Hospital Care Intensity and Surgical Outcomes
Table 4. Alternative Metrics for Hospital Care Intensity Hospital Care Intensity Characteristica
Low
Average
High
P Value for Low vs High
Total
65 008.00
75 024.00
94 789.00
