Abstract
Background
Thirty-day risk-standardized mortality rates after acute myocardial infarction are commonly used to evaluate and compare hospital performance. However, it is not known whether differences between hospitals in early patient survival are associated with differences in long-term survival.
Methods
We analyzed data from the Cooperative Cardiovascular Project, a study of Medicare beneficiaries hospitalized for acute myocardial infarction between 1994-96 with 17 years of follow-up. We grouped hospitals into five strata based on case-mix severity. Within each case-mix stratum, we compared life expectancy in patients admitted to high and low-performing hospitals, as defined by quintiles of thirty-day risk-standardized mortality rates. Cox proportional hazards models were used to calculate life expectancy.
Results
The study sample included 119,735 patients with acute myocardial infarction admitted to 1,824 hospitals. Within each case-mix stratum, survival curves for patients admitted to hospitals in each risk-standardized mortality rate quintile separated within the first 30 days and then remained parallel over 17 years of follow-up. Estimated life expectancy declined as hospital risk-standardized mortality rate quintile increased. On average, patients treated at high-performing hospitals lived between 1.14 and 0.84 years longer than patients treated at low-performing hospitals, depending on hospital case-mix. When 30-day survivors were examined separately, there was no difference in unadjusted or adjusted life expectancy across hospital risk-standardized mortality rate quintiles.
Conclusion
Patients admitted to high-performing hospitals after acute myocardial infarction had longer life expectancies than patients treated in low-performing hospitals. This survival benefit arose in the first 30 days and persisted over the long term.
Public reporting has become a mainstay of national efforts to improve the quality of care delivered in U.S. hospitals.1 Increasingly, risk-standardized mortality rates are used to benchmark quality and gauge hospital performance because they reflect meaningful and widely interpretable results of hospital care.2,3 Since 2007, the Centers for Medicare and Medicaid Services (CMS) have reported hospital-specific 30-day risk-standardized mortality rates for several common conditions, and more recently, risk-standardized mortality rates have been incorporated into payment policies.4-6
Although several studies have evaluated the association of condition-specific risk-standardized mortality rates with other short-term quality metrics,7-16 it is not known whether patients admitted to hospitals with better short-term outcomes have improved long-term patient survival. The short-term survival benefit conferred at high-performing hospitals may dissipate over time, lending support to the theory that these hospitals discharge more patients alive but with higher subsequent mortality. Alternatively, patients treated at high-performing hospitals may have a survival benefit that persists over time, suggesting that differences in the quality of care delivered produce an early benefit that endures over time.
Accordingly, we used data from the Cooperative Cardiovascular Project (CCP), a large nationally representative cohort study of Medicare beneficiaries hospitalized with acute myocardial infarction with over 17 years of follow-up, to evaluate the association between hospital 30-day risk-standardized mortality rates and life expectancy after acute myocardial infarction. We selected acute myocardial infarction because it was one of the first conditions for which 30-day risk-standardized mortality rates were developed and because there is significant heterogeneity in risk-standardized mortality rates across hospitals. We used life expectancy to measure long-term survival because it is an easily interpretable metric that is meaningful to patients and can be used to calculate the years of life saved by treatment at high- versus low-performing hospitals.
METHODS
Study Design and Conduct
The study was designed by all authors and approved by the Institutional Review Board at Yale University. Funding was provided by the National Heart, Lung, and Blood Institute and the NIGMS Medical Scientist Training Program. The first author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analyses.
Study Population
We analyzed data from the CCP, a Health Care Financing Administration quality-improvement initiative for patients with acute myocardial infarction, which has been described extensively elsewhere.17,18 The CCP included fee-for-service Medicare beneficiaries hospitalized with a principal discharge diagnosis of acute myocardial infarction (International Classification of Diseases, Ninth Revision, Clinical Modification, code 410), except acute myocardial infarction readmissions (code 410.x2). Each hospital was sampled for an 8-month period during the interval between February 1994 and July 1995. Trained personnel at centralized data abstraction centers abstracted patient records for information on demographics, medical history, clinical presentation, laboratory and electrocardiographic data, and treatment.
In our analyses, we used similar inclusion criteria to those used in established risk-standardized mortality rate measures.19,20 Specifically, we limited our sample to patients 65 years of age or older with clinically confirmed acute myocardial infarction. When patients were hospitalized more than once during the study period, we used only the first admission. We excluded patients admitted directly from ambulatory surgery, patients transferred from other acute care hospitals, and patients who left the hospital against medical advice. Finally, we limited the analysis to only hospitals with at least 30 patients with a discharge diagnosis of acute myocardial infarction during the 8-month sample period.
Outcome Variable
We used data from the 1994 to 2012 Medicare Denominator files to ascertain survival over 17 years of follow-up. The Denominator files contain demographic and enrollment information on all Medicare fee-for-service and Medicare Advantage beneficiaries in a given year, including dates of death. Time to death was defined as days from admission to the date of death and was censored at 17 years of follow-up.
Calculation of Risk-Standardized Mortality Rates
Calculations of risk-standardized mortality rates were performed using a medical record model described by Krumholz et al, which is detailed in the Supplementary Appendix Methods.19,20 Briefly, we used a hierarchical logistic model that linked the log-odds of mortality within 30 days of admission as a function of patient demographic and clinical variables, and a random hospital-specific effect. Risk-standardized mortality rates are calculated as the ratio of “predicted” to “expected” mortality at a given hospital, multiplied by the national observed mortality rate.20 For each hospital, the denominator (“expected” mortality) is the number of deaths expected within 30 days based on national mortality data for that hospital's case mix. It is calculated using a common intercept for all hospitals and thus, can be viewed as a measure of hospital case mix. The numerator (“predicted” mortality) is the number of predicted deaths based on that specific hospital's performance. It is calculated using a hospital-specific intercept and is analogous to “observed” mortality. Conceptually, the ratio of “predicted” to “expected” mortality compares a particular hospital's performance to that of an “average” hospital with the same case mix.
Sample Stratification
To ensure that we were comparing life expectancy estimates between patients admitted to hospitals with similar case mix, we stratified hospitals into quintiles based on their “expected” mortality (i.e. the denominator of the 30-day risk-standardized mortality rate model), which is a marker of case mix or patient risk (Figure 1). Life expectancy analyses were conducted separately for each case-mix stratum to permit comparisons of patients admitted to hospitals with similar case mix. Within each case-mix stratum, we ranked hospitals by risk-standardized mortality rate and grouped them into quintiles (Figure 1). These quintiles reflect hospital performance on 30-day mortality among hospitals with similar case mix and were the primary unit of analysis for all life expectancy calculations. We used the terms “high-performing” and “low-performing” to refer to those hospitals in the lowest and highest 30-day mortality quintile, respectively, within each case-mix stratum.
Figure 1. Flow chart describing study exclusions and process of creating case-mix strata and hospital performance quintiles.
The total cohort included 119,735 patients with acute myocardial infarction (AMI) distributed across 1,824 hospitals. Hospitals were rank ordered by expected mortality, a measure of case mix, and stratified into quintiles of hospitals admitting more healthy to less healthy patients. Within each case-mix stratum, hospitals were then ranked by risk-standardized mortality rate (RSMR), a measure of hospital performance, and stratified into quintiles, representing higher-quality to lower-quality hospitals. Models evaluating the association between RSMR quintile and life expectancy after myocardial infarction were estimated separately for each case-mix stratum.
Life Expectancy Calculations
Life expectancy is defined as mean survival and can be calculated as the area under the survival curve. We used a 3-step process to calculate life expectancy (Supplementary Appendix Figure S1), which is detailed in the Supplementary Appendix Methods. First, we fit a separate marginal Cox proportional hazards model within each case-mix stratum. The model included dummy variables for risk-standardized mortality rate quintile and patient age. Second, we plotted the expected survival curves separately for patients within each risk-standardized mortality rate quintile and extrapolated the curves to age 100 using exponential models. We selected exponential models because we lacked information on the shape of the survival curves and thus chose a model with a constant hazard that does not make assumptions about changes to the hazard function over time. Finally, mean life expectancy estimates were calculated by summing the areas under the individual Cox and exponential survival functions. We then calculated the years of life saved as the difference in life expectancy between patients treated at hospitals in the lowest and highest risk-standardized mortality rate quintiles within each case-mix stratum.
To determine whether differences in life expectancy across risk-standardized mortality rate quintiles could be explained by differences in patient characteristics and treatment between hospitals, we repeated the life expectancy calculations above, adjusting first for clinical characteristics and then for treatment characteristics. The clinical model included 35 sociodemographic, medical history, frailty, and clinical presentation characteristics. The treatment model adjusted for the same variables in the clinical model in addition to reperfusion therapies, aspirin, and beta-blockers on admission. Details of these models are described in the Supplementary Appendix Methods.
Finally, to determine whether the survival benefit of being admitted to a higher-performing hospital occurred exclusively in the first 30 days or continued to increase after 30 days, we repeated unadjusted and adjusted life expectancy calculations among 30-day survivors only. The same methods and covariates were applied to calculate life expectancy and years of life saved in all patients and in 30-day survivors. Life expectancy was calculated from the time of admission for the overall cohort and from 30 days for 30-day survivors. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC).
RESULTS
Patient Characteristics
The final study sample included 119,735 patients with acute myocardial infarction admitted to 1,824 hospitals (Figure 1). Chart-abstracted baseline characteristics for patients in each case-mix stratum are presented in Table 1, and clinical presentation and treatment characteristics for each case-mix stratum are presented in Supplementary Appendix Table S1. Compared with patients admitted to hospitals in the lowest case-mix stratum (i.e. lowest expected mortality), those admitted to hospitals in the highest case-mix stratum (i.e. highest expected mortality) were older on average, had a higher prevalence of diabetes, and were less likely to have undergone prior coronary revascularization procedures. Patients admitted to hospitals in the highest case-mix stratum had higher rates of shock and heart failure on presentation than patients in the lowest case-mix stratum. In addition they were less likely to receive reperfusion therapy or aspirin on admission.
Table 1.
Patient characteristics by hospital case mix (i.e. expected mortality) strata (n=119,735)
| Case-Mix Strata | |||||||
|---|---|---|---|---|---|---|---|
| Overall N=119,735 | Stratum 1 (i.e. hospitals that admitted the healthiest patients) N=21,054 | Stratum 2 N=24,891 | Stratum 3 N=26,228 | Stratum 4 N=24,618 | Stratum 5 (i.e. hospitals that admitted the sickest patients) N=22,944 | p-value for trend* | |
| Demographics | |||||||
| Age, mean (SD) | 76.5 (7.4) | 75.4 (7.1) | 75.9 (7.2) | 76.4 (7.3) | 76.9 (7.4) | 77.6 (7.6) | <0.001 |
| Female, N(%) | 58,453 (48.8) | 9681 (46.0) | 11,855 (47.6) | 12,817 (48.9) | 12,268 (49.8) | 11,832 (51.6) | <0.001 |
| Nonwhite race, N(%) | 10502 (8.8) | 2140 (10.2) | 2025 (8.1) | 2170 (8.3) | 2109 (8.6) | 2058 (9.0) | <0.001 |
| Median household income percentile, mean (SD) | 49.5 (28.9) | 44.7 (28.6) | 47.6 (28.8) | 47.7 (28.3) | 52.5 (28.5) | 54.6 (29.1) | <0.001 |
| Missing | 4879 (4.1) | 1118 (5.3) | 1263 (5.1) | 1002 (3.8) | 876 (3.6) | 620 (2.7) | |
| Medical History | |||||||
| Diabetes | 36,690 (30.6) | 6162 (29.3) | 7568 (30.4) | 8157 (31.1) | 7588 (30.8) | 7215 (31.5) | <0.001 |
| Hypertension | 74,531 (62.3) | 13,131 (62.4) | 15,490 (62.2) | 16,452 (62.7) | 15,231 (61.9) | 14,227 (62.0) | 0.31 |
| History of AMI | 35,668 (29.8) | 6160 (29.3) | 7510 (30.2) | 7978 (30.4) | 7256 (29.5) | 6764 (29.5) | 0.02 |
| History of CABG | 15,484 (12.9) | 3007 (14.3) | 3334 (13.4) | 3427 (13.1) | 2982 (12.1) | 2734 (11.9) | <0.001 |
| History of PCI | 8294 (6.9) | 1753 (8.3) | 1858 (7.5) | 1822 (7.0) | 1559 (6.3) | 1302 (5.7) | <0.001 |
| History of cerebrovascular accident | 16,701 (14.0) | 2745 (13.0) | 3301 (13.3) | 3763 (14.4) | 3433 (14.0) | 3459 (15.1) | <0.001 |
| COPD | 24,167 (20.2) | 4173 (19.8) | 5064 (20.3) | 5417 (20.7) | 4921 (20.0) | 4592 (20.0) | 0.15 |
| Current smoker | 17371 (14.5) | 3310 (15.7) | 3789 (15.2) | 3860 (14.7) | 3441 (14.0) | 2971 (13.0) | <0.001 |
| Obesity | 18824 (18.7) | 3438 (19.1) | 4115 (19.3) | 4268 (19.4) | 3758 (18.2) | 3245 (17.2) | <0.001 |
| Missing | 18878 (15.8) | 3024 (14.4) | 3601 (14.5) | 4204 (16.0) | 3926 (16.0) | 4123 (18.0) | |
| CHF | 25610 (21.4) | 3753 (17.8) | 4901 (19.7) | 5788 (22.1) | 5630 (22.9) | 5538 (24.1) | <0.001 |
| Chronic kidney disease | 5817 (4.9) | 836 (4.0) | 1155 (4.6) | 1182 (4.5) | 1301 (5.3) | 1343 (5.9) | <0.001 |
| Cancer | 2770 (2.3) | 446 (2.1) | 573 (2.3) | 592 (2.3) | 598 (2.4) | 561 (2.5) | 0.13 |
| Dementia | 7116 (7.3) | 1069 (5.1) | 1268 (5.1) | 1544 (5.9) | 1556 (6.3) | 1679 (7.3) | <0.001 |
| Anemia | 8394 (7.0) | 1367 (6.5) | 1644 (6.6) | 1825 (7.0) | 1801 (7.3) | 1757 (7.7) | <0.001 |
| Frailty Indices | |||||||
| Admission from skilled nursing facility | 7661 (8.8) | 947 (4.5) | 1368 (5.5) | 1624 (6.2) | 1693 (6.9) | 2029 (8.8) | <0.001 |
| Immobile on admission | 22028 (18.9) | 3256 (15.8) | 4310 (17.7) | 4842 (18.9) | 4724 (19.7) | 4896 (22.1) | <0.001 |
| Missing | 3081 (2.6) | 488 (2.3) | 574 (2.3) | 618 (2.4) | 655 (2.7) | 746 (3.3) | |
| Incontinent on admission | 8505 (7.3) | 1131 (5.5) | 1586 (6.5) | 1891 (7.4) | 1847 (7.7) | 2050 (9.2) | <0.001 |
| Missing | 2630 (2.2) | 351 (1.7) | 488 (2.0) | 577 (2.2) | 559 (2.3) | 655 (2.9) | |
P-values are for trend across all five case-mix strata.
Life Expectancy
Unadjusted survival curves for patients in each risk-standardized mortality rate quintile were plotted for each of the five case-mix strata (Supplementary Appendix Figures S2 through S6). Within each case-mix stratum, patients in the lowest risk-standardized mortality rate quintile (i.e. admitted to high-performing hospitals) had the highest survival, whereas patients in the highest risk-standardized mortality rate quintile (i.e. admitted to low-performing hospitals) had the lowest survival over all 17 years.
Unadjusted life expectancy estimates after acute myocardial infarction by risk-standardized mortality rate quintile and case-mix stratum showed similar patterns (Supplementary Appendix Figure S7 and Table S2). As case-mix severity increased (moving from case-mix stratum 1 to stratum 5), life expectancy decreased. Similarly, within each case-mix stratum, life expectancy decreased as risk-standardized mortality rate quintile increased, resulting in statistically significant differences in life expectancy between the high-performing hospitals and low-performing hospitals (Table 2). For example, in the lowest case-mix stratum, patients treated at high-performing hospitals lived on average 1.07 (95% confidence interval [CI] 0.75-1.39) years longer than patients treated at low-performing hospitals. Similarly, in the highest case-mix stratum, the difference in life expectancy for patients treated at high- versus low-performing hospitals was 0.83 (95% CI 0.63-1.13) years. These values varied slightly by case mix but were statistically significant in all 5 case-mix strata (all p<0.001).
Table 2.
Mean (95% confidence interval) years of life saved by patients treated at hospitals in high-performing hospitals (i.e. lowest RSMR quintile) relative to low-performing hospitals (i.e. highest RSMR quintile), stratified by hospital case-mix strata from the time of admission in all patients, and from 30-days among 30-day survivors only.
| Hospital Case-mix Strata | Case-Mix Severity (Expected Mortality Rate)* Mean (SE) | Unadjusted | Adjusted for Clinical Characteristics† | Adjusted for Treatment‡ |
|---|---|---|---|---|
| Years of Life Saved Mean (95% CI) | Years of Life Saved Mean (95% CI) | Years of Life Saved Mean (95% CI) | ||
| ALL PATIENTS§ | ||||
| 1 (“Healthiest” patients) | 13.2 (1.0) | 1.07 (0.75, 1.39) | 1.20 (0.89, 1.51) | 1.14 (0.84, 1.44) |
| 2 | 15.1 (0.4) | 0.98 (0.82, 1.25) | 0.89 (0.62, 1.15) | 0.74 (0.48, 1.01) |
| 3 | 16.3 (0.4) | 0.94 (0.67, 1.23) | 0.94 (0.71, 1.18) | 0.80 (0.57, 1.03) |
| 4 | 17.8 (0.5) | 0.95 (0.64, 1.26) | 0.85 (0.60, 1.11) | 0.77 (0.52, 1.01) |
| 5 (“Sickest” patients) | 20.3 (1.6) | 0.83 (0.63, 1.13) | 0.93 (0.68, 1.18) | 0.84 (0.60, 1.09) |
| 30-DAY SURVIVORS ONLY∥ | ||||
| 1 (“Healthiest” patients) | 13.2 (1.0) | 0.21 (−0.15, 0.57) | 0.41 (0.07, 0.74)# | 0.40 (0.07, 0.72)# |
| 2 | 15.1 (0.4) | 0.15 (−0.18, 0.48) | 0.11 (−0.18, 0.41) | 0.06 (−0.24, 0.35) |
| 3 | 16.3 (0.4) | 0.09 (−0.22, 0.40) | 0.24 (−0.03, 0.51) | 0.20 (−0.06, 0.46) |
| 4 | 17.8 (0.5) | 0.06 (−0.31, 0.43) | 0.03 (−0.25, 0.31) | 0.02 (−0.26, 0.29) |
| 5 (“Sickest” patients) | 20.3 (1.6) | −0.08 (−0.39, 0.23) | 0.08 (−0.22, 0.37) | 0.06 (−0.22, 0.35) |
Hospitals were grouped into case-mix strata using the expected mortality estimates generated from the risk-standardized mortality rate models as a marker of case-mix severity. Mean expected mortality estimates for hospitals in each stratum are provided here to demonstrate the range of case-mix severity across strata.
Clinical model is adjusted for patient sociodemographic (age, gender, race, socioeconomic status), medical history (diabetes, hypertension, history of myocardial infarction, history of coronary artery bypass grafting, history of percutaneous coronary intervention, history of cerebrovascular accident or transient ischemic attack chronic obstructive pulmonary disease, congestive heart failure, chronic kidney disease, cancer, dementia or Alzheimer's disease, anemia, current smoking, obesity), frailty (admission from a skilled nursing facility, immobility or incontinence on admission), and clinical presentation (heart rate on presentation, initial systolic blood pressure, shock within the first 48 hours of admission, duration of chest pain before presentation, blood urea nitrogen, creatinine, white blood cell count, ST-elevation myocardial infarction, left or right bundle branch block, heart block, anterior or lateral acute myocardial infarction).
Treatment model is adjusted for those variables in the clinical model in addition to revascularization (percutaneous coronary intervention or coronary artery bypass grafting) within 30 days of admission, fibrinolytic therapy during hospitalization, aspirin, and beta-blockers on admission.
All p-values for comparing years of life saved by all patients admitted to high-versus low-performing hospitals are <0.001.
P-values for comparing years of life saved by 30-day survivors admitted to high- versus low-performing hospitals are >0.05, unless otherwise noted.
P-value 0.001.
Differences in patient life expectancy between high- and low-performing hospitals persisted after adjustment for patient sociodemographic and clinical characteristics in the clinical model (Table 2, Supplementary Appendix Figure S8, and Supplementary Appendix Table S2) and after further adjustment for treatment in the full model (Table 2, Figure 2, and Supplementary Appendix Table S2). After full adjustment, patients treated at high-performing hospitals lived an average of 1.14 (95% CI 0.84-1.44) years longer than patients treated at low-performing hospitals in the lowest case-mix stratum and 0.84 (95% CI 0.60-1.09) years longer in the highest case-mix stratum. Differences in life expectancy across risk-standardized mortality rate quintiles remained statistically significant in all 5 case-mix strata (all p<0.001).
Figure 2. Mean life expectancy (in years) after acute myocardial infarction, by risk-standardized mortality rate quintile calculated from admission, adjusted for patient characteristics and treatment.
Models for the calculation of life expectancy after acute myocardial infarction (AMI) were estimated separately for each case-mix stratum. Higher case-mix stratum signifies hospitals that admitted “sicker” patients with higher expected mortality. Within each case-mix stratum, higher risk-standardized mortality rate (RSMR) quintile indicates lower-performing hospitals (with a higher than expected mortality).
Life Expectancy Among 30-Day Survivors
When 30-day survivors were examined separately, survival curves for patients in the 5 risk-standardized mortality rate quintiles were nearly identical in each of the five case-mix strata (Supplementary Appendix Figures S9 through S13). As in the analyses including all patients, life expectancy estimates declined as case-mix severity increased, reflecting the fact that hospitals in these case-mix strata were caring for patients at higher risk. However, life expectancy estimates were similar for 30-day survivors in all risk-standardized mortality rate quintiles within a given case-mix stratum (Supplementary Appendix Figure S14 and Supplementary Appendix Table S3). Unadjusted differences in life expectancy between the highest- and lowest-performing hospitals were not significant for any case-mix stratum (all p>0.10). Life expectancy estimates were similar for 30-day survivors treated at high- and low-performing hospitals even after adjustment for differences in clinical characteristics and treatment between hospitals (Table 2, Figure 3, Supplementary Appendix Table S3 and Supplementary Appendix Figure S15).
Figure 3. Mean life expectancy (in years) after acute myocardial infarction, by risk-standardized mortality rate quintile for 30-day survivors, adjusted for patient characteristics and treatment.
Models for the calculation of life expectancy after acute myocardial infarction (AMI) were estimated separately for each case-mix stratum. Higher case-mix stratum signifies hospitals that admitted “sicker” patients with higher expected mortality. Within each case-mix stratum, higher risk-standardized mortality rate (RSMR) quintile indicates lower-performing hospitals (with a higher than expected mortality).
DISCUSSION
Using data from a large medical record study of patients with acute myocardial infarction with long-term follow-up, we demonstrated statistically significant differences in life expectancy between patients admitted to hospitals with high and low performance on 30-day mortality quality measures. After grouping hospitals with similar case mix, patients treated at high-performing hospitals (i.e. low 30-day risk-standardized mortality rates) lived, on average, between 0.74 and 1.14 years longer after acute myocardial infarction than those treated at low-performing hospitals (i.e. high 30-day risk-standardized mortality rates). These findings were consistent across case-mix strata, indicating that the relationship between hospital performance and long-term patient outcomes is independent of hospital case mix. The survival advantage for patients treated at high-performing hospitals arose from differences in survival during the first 30 days after hospitalization and then persisted over the remainder of follow-up.
Prior studies have similarly shown that the survival benefits associated with individual therapies occur largely in the first 30 days and then persist over time. In the ISIS-2 and GISSI-1 trials, aspirin and reperfusion therapy were associated with significant survival gains in the first 30 days to 1 year, which then persisted over 10 years.21,22 Our study extends these findings to short-term hospital outcome measures, showing that the early survival advantage achieved by high-performing hospitals is durable. If hospitals with low 30-day risk-standardized mortality rates achieved lower-than-expected mortality by forestalling death for the first 30 days, we might expect higher long-term mortality rates and thus lower life expectancy in 30-day survivors. Instead, our findings show that patients treated at high-performing hospitals who survive the acute period do not lose that advantage. Alternatively, if high-performing hospitals admitted patients with lower risk than what was captured by the risk model, we would expect the survival curves to continue to diverge. The fact that the survival curves remain parallel after the first 30 days suggests that the association of early hospital performance with outcomes is the result of quality differences and not residual confounding.
Our results suggest that investing in initiatives to improve short-term hospital performance may also improve patient outcomes over the long term. Many quality improvement efforts have focused on improving process-of-care measures for acute myocardial infarction,23,24 but these efforts, while important, have failed to resolve the variation in risk-standardized mortality among hospitals. 11-14,25,26 Like past studies, we found an inconsistent relationship between risk-standardized mortality rate quintile and acute myocardial infarction process measures (Supplementary Appendix Table S4). This observation is likely because many patients are not eligible for the process measures, these measures represent only a narrow assay of quality, or the follow-up time is too short to capture the effect of discharge treatments.12 Other factors such as hospital culture, organizational structure, and collaboration across providers may explain more of the variation.27-31
This study has several limitations. First, we applied several patient and hospital exclusion criteria in this study to calculate risk-standardized mortality rates. As a result, our findings may not be generalizable to all patients or hospitals, but do reflect current methods of estimating short-term outcomes. Second, approximately 7% of patients in the CCP were still alive after 17 years of follow-up, which required extrapolation of the expected survival curves to calculate life expectancy. Third, we lacked information on patients who lost Medicare eligibility, however, we estimate this number to be relatively small. Fourth, our study is based on observational data, and as such, unmeasured factors related to hospital quality and to hospital selection could confound our analyses. Fifth, patients surviving to 30 days in the current era may have different features than those in the mid-1990's, with potentially greater risks of heart failure, reinfarction, and even mortality over the long term.32,33
In conclusion, we found that patients treated at hospitals with lower 30-day risk-standardized mortality rates had significantly longer life expectancies after acute myocardial infarction than patients treated at hospitals with higher risk-standardized mortality rates. These differences in life expectancy were attributable to more patients surviving the first 30 days at high-performing hospitals than at low-performing hospitals and the persistence of that benefit over time.
Supplementary Material
Acknowledgements
The authors acknowledge the assistance of Qualidigm and the Centers for Medicare & Medicaid Services (CMS) in providing data, which made this research possible. The content of this publication does not reflect the views of Qualidigm or CMS, nor does mention of organizations imply endorsement by the U.S. Government. The authors assume full responsibility for the accuracy and completeness of the ideas presented.
Support:
Funding for this manuscript came from the National Heart, Lung, and Blood Institute and the NIGMS Medical Scientist Training Program.
Dr. Bucholz is supported by an F30 Training grant F30HL120498-01A1 from the National Heart, Lung, and Blood Institute and by NIGMS Medical Scientist Training Program grant T32GM07205. Dr. Krumholz is supported by grant U01 HL105270-04 (Center for Cardiovascular Outcomes Research at Yale University) from the National Heart, Lung, and Blood Institute.
Footnotes
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