Development of a Clinical Registry-Based 30-Day Readmission Measure for Coronary Artery Bypass Grafting Surgery David M. Shahian, Xia He, Sean M. O'Brien, Frederick L. Grover, Jeffrey P. Jacobs, Fred H. Edwards, Karl F. Welke, Lisa G. Suter, Elizabeth Drye, Cynthia M. Shewan, Lein Han and Eric Peterson Circulation. 2014;130:399-409; originally published online June 10, 2014; doi: 10.1161/CIRCULATIONAHA.113.007541 Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2014 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/130/5/399
Data Supplement (unedited) at: http://circ.ahajournals.org/content/suppl/2014/06/10/CIRCULATIONAHA.113.007541.DC1.html
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
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Health Services and Outcomes Research Development of a Clinical Registry-Based 30-Day Readmission Measure for Coronary Artery Bypass Grafting Surgery David M. Shahian, MD; Xia He, MS; Sean M. O’Brien, PhD; Frederick L. Grover, MD; Jeffrey P. Jacobs, MD; Fred H. Edwards, MD; Karl F. Welke, MD; Lisa G. Suter, MD; Elizabeth Drye, MD, SM; Cynthia M. Shewan, PhD; Lein Han, PhD; Eric Peterson, MD, MPH Background—Reducing readmissions is a major healthcare reform goal, and reimbursement penalties are imposed for higherthan-expected readmission rates. Most readmission risk models and performance measures are based on administrative rather than clinical data. Methods and Results—We examined rates and predictors of 30-day all-cause readmission following coronary artery bypass grafting surgery by using nationally representative clinical data (2008–2010) from the Society of Thoracic Surgeons National Database linked to Medicare claims records. Among 265 434 eligible Medicare records, 226 960 (86%) were successfully linked to Society of Thoracic Surgeons records; 162 572 (61%) isolated coronary artery bypass grafting admissions constituted the study cohort. Logistic regression was used to identify readmission risk factors; hierarchical regression models were then estimated. Risk-standardized readmission rates ranged from 12.6% to 23.6% (median, 16.8%) among 846 US hospitals with ≥30 eligible cases and ≥90% of eligible Centers for Medicare and Medicaid Services records linked to the Society of Thoracic Surgeons database. Readmission predictors (odds ratios [95% confidence interval]) included dialysis (2.02 [1.87–2.19]), severe chronic lung disease (1.58 [1.49–1.68]), creatinine (2.5 versus 1.0 or lower:1.49 [1.41–1.57]; 2.0 versus 1.0 or lower: 1.37 [1.32–1.43]), insulin-dependent diabetes mellitus (1.45 [1.39–1.51]), obesity in women (body surface area 2.2 versus 1.8: 1.44 [1.35–1.53]), female sex (1.38 [1.33–1.43]), immunosuppression (1.38 [1.28–1.49]), preoperative atrial fibrillation (1.36 [1.30–1.42]), age per 10-year increase (1.36 [1.33–1.39]), recent myocardial infarction (1.24 [1.08–1.42]), and low body surface area in men (1.22 [1.14–1.30]). C-statistic was 0.648. Fifty-two hospitals (6.1%) had readmission rates statistically better or worse than expected. Conclusions—A coronary artery bypass grafting surgery readmission measure suitable for public reporting was developed by using the national Society of Thoracic Surgeons clinical data linked to Medicare readmission claims. (Circulation. 2014;130:399-409.) Key Words: coronary artery bypass ◼ patient readmission ◼ registries ◼ risk adjustment
T
he reduction of hospital readmissions is a major goal of healthcare reform and has been the focus of Medicare Payment Advisory Commission reports.1,2 The Patient Protection and Affordable Care Act incorporates a Hospital Readmissions Reduction Program, which includes public reporting of risk-standardized readmission rates and progressively increasing reimbursement penalties for hospitals with higher-than-expected unplanned readmission rates. Bundled payment initiatives may create additional incentives for improved discharge planning and postdischarge care
coordination by making the index hospital financially responsible for subsequent readmissions.3
Clinical Perspective on p 409 Adjustment for differences in case mix is necessary to fairly implement readmission reduction initiatives and avoid inappropriately penalizing providers or misleading consumers.4 Previous readmission risk models developed for use by the Centers for Medicare and Medicaid Services (CMS) have been based on Medicare administrative claims data,5–7 and a claims-based
Received November 15, 2013; accepted May 27, 2014. From the Massachusetts General Hospital and Harvard Medical School, Boston, MA (D.M.S.); Duke Clinical Research Institute, Durham, NC (X.H., S.M.O., E.P.); University of Colorado School of Medicine-Anschutz Medical Campus, Aurora, CO, and Denver Department of Veterans Affairs Medical Center, Denver, CO (F.L.G.); All Children’s Hospital, John Hopkins University, Saint Petersburg, FL (J.P.J.); University of Florida College of Medicine, Jacksonville, FL (F.H.E.); Children’s Hospital of Illinois and the University of Illinois College of Medicine, Peoria, IL (K.F.W.); Yale-New Haven Health Services Corporation Center for Outcomes Research and Evaluation (CORE) and Yale School of Medicine, New Haven, CT (L.G.S., E.D.); Society of Thoracic Surgeons, Chicago, IL (C.M.S.); and Centers for Medicare and Medicaid Services, Baltimore, MD (L.H.). The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA. 113.007541/-/DC1. Correspondence to David M. Shahian, MD, Department of Surgery and Center for Quality and Safety, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. E-mail [email protected] © 2014 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.113.007541
Downloaded from http://circ.ahajournals.org/ by guest on November 3, 2014 399
400 Circulation July 29, 2014 coronary artery bypass grafting surgery (CABG) readmission measure is under development. However, some previous CABG studies have raised concerns about the accuracy of profiling measures based on administrative data8,9 because of their inadequate risk adjustment, case misclassification, and other issues. Although claims-based risk adjustment, with its inherent limitations, may be the only measure development option for many conditions, the vast majority of CABG procedures performed in the United States are entered into the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database. This registry contains detailed clinical data needed for robust risk adjustment. With the use of previously described algorithms,10 it has also been successfully linked to various claims data sources that can be used to determine readmissions, longterm outcomes, and resource use. Accordingly, STS and the Duke Clinical Research Institute, in collaboration with the Yale Center for Outcomes Research and Evaluation group and CMS, have developed a 30-day all-cause readmission measure based on STS Adult Cardiac Surgery Database clinical data linked to Medicare readmission data.
Methods Data Sources The STS Adult Cardiac Surgery Database provides distinct advantages in comparison with administrative data. These include uniform cohort definitions, standardized risk factor specifications designed by surgical content experts, and rigorous quality control. Eight to ten percent of participating sites undergo extensive annual external audits, and data accuracy is high (97% overall). These advantages are shared by other robust clinical registries such as those maintained by the State of New York and used for public reporting.11 STS Adult Cardiac Surgery Database data elements include preoperative clinical characteristics, operative techniques, complications, and mortality. With the use of these data, risk-adjusted outcomes feedback reports are provided to >1050 cardiac surgery programs (90%–95% of all US programs). The Medicare Part A inpatient database contains fee-for-service claims for reimbursement of hospital inpatient facility costs. Medicare
data elements used for this project include dates of admission and discharge, patient demographics, International Classification of Diseases, Ninth Revision diagnosis and procedure codes, and hospital provider number. The Medicare Enrollment Database contains patient demographic information, vital status, and monthly indicators of fee-for-service eligibility (to implement measure inclusion/exclusion criteria).
Cohort Development, Linkage Methodology, and Exclusions Derivation of the isolated CABG study cohort and linkage of STS and Medicare data are summarized in Figure 1 and described in detail in online-only Data Supplement Appendix I. The STS algorithm for defining an isolated CABG in Database version 2.61 (used for these analyses) is available on the STS website.12 Comparison of CMS and STS records that did or did not link is provided in Tables I and II in the online-only Data Supplement. From 2008 to 2010, a total of 265 434 CMS admissions from 1172 hospitals met eligibility criteria to be considered for linkage to the STS database. Among 237 790 index CABG admissions from 1012 STS-participating hospitals, 226 960 admissions linked to an STS record and 10 830 did not link. Nonlinked patients were more often female (36.2% versus 31.9%), black (6.8% versus 4.6%), and more likely to be readmitted (20.2% versus 18.6%). We also performed a complementary analysis of 2008 to 2010 CMS admissions that could be linked to STS records of patients ≥65 years of age who met the definition of isolated CABG. Among 262 277 STS records at 1024 centers that submitted CMS claims during the study period, 223 594 linked to a CMS admission and 38 683 did not. Nonlinked records were more often male (70.6% versus 69.5%), black (6.6% versus 5.3%), and diabetic patients with smaller body surface areas. They less often had chronic lung disease, peripheral vascular disease, or cerebrovascular disease, and more often had creatinine levels of 2 wk)
No CVA
2690
9.8
9971
7.4
Recent CVA (≤ 2 wk)
57
0.2
225
0.2
No
26 220
95.8
131 795
97.5
Yes
1124
4.1
3337
2.5
25 904
94.7
128 854
95.3
1328
4.9
5924
4.4
Previous CV interventions Number of previous CV surgeries
No previous CV surgery 1 prior CV surgery ≥2 prior CV surgeries
Prior PCI
No PCI PCI - within 6 h
114
0.4
357
0.3
20 040
73.2
102 283
75.6
255
0.9
1003
0.7 (Continued )
Downloaded from http://circ.ahajournals.org/ by guest on November 3, 2014
402 Circulation July 29, 2014 Table 1. Continued Readmission = Yes Variable
Level
Readmission = No
n
%
n
%
Elective
10 858
39.7
61 206
45.3
Urgent
15 230
55.7
69 124
51.1
1233
4.5
4710
3.5
34
0.1
133
0.1
13 714
50.1
77 782
57.5 17.3
Preoperative cardiac status Acuity status
Emergent Emergent salvage Myocardial infarction
Angina Arrhythmia
No prior MI MI > 21 days
5067
18.5
23 429
MI 8–21 days
1379
5.0
4597
3.4
MI 1–7 days
6005
21.9
24 540
18.2
MI > 6 and < 24 h
681
2.5
2930
2.2
MI ≤ 6 h
414
1.5
1516
1.1
No
11 173
40.8
48 975
36.2
Yes
15 956
58.3
85 111
62.9
No arrhythmia
23 073
84.3
120 817
89.4
21
0.1
70
0.1
AFib/flutter Heart block Sustained VT/VF Multiple types Preop IABP
No Yes
Congestive heart failure
No CHF CHF NYHA-I
Left main disease > 50% Ejection fraction, %
Mitral insufficiency
6
0.0
2166
1.6
3716
13.6
12 038
8.9
25 049
91.5
126 119
93.3
2292
8.4
8987
6.6
20 862
76.2
113 634
84.0
362
1.3
1456
1.1
CHF NYHA-II
1555
5.7
6082
4.5
2701
9.9
8575
6.3
CHF NYHA-IV
1761
6.4
5078
3.8
96
0.4
303
0.2
None
70
0.3
319
0.2
One
934
3.4
4943
3.7
Two
5262
19.2
26 714
19.8
Three
21 076
77.0
103 098
76.3
No
17 402
63.6
87 159
64.5
Yes
9888
36.1
47 702
35.3
< 25
962
3.5
3449
2.6
25–34
2307
8.4
8224
6.1
35–44
3879
14.2
16 300
12.1
45–54 Aortic insufficiency
0.0 1.9
CHF NYHA-III CHF missing NYHA Number of diseased coronary vessels
6 515
5807
21.2
29 726
22.0
≥ 55
13 353
48.8
72 718
53.8
None
15 729
57.5
76 852
56.8
Trivial
1685
6.2
7366
5.4
Mild
1886
6.9
8148
6.0
Moderate
401
1.5
1562
1.2
Severe
32
0.1
144
0.1 29.9
N/A
7453
27.2
40 398
None
11 336
41.4
58 688
43.4
Trivial
3074
11.2
15 417
11.4
Mild
5077
18.6
21 563
15.9
Moderate
1682
6.1
5979
4.4
Severe
179
0.7
588
N/A
5907
21.6
32 535
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0.4 24.1 (Continued )
Shahian et al CABG Readmission Measure Based on Clinical Data 403 Table 1. Continued Readmission = Yes Variable
Level
Tricuspid insufficiency
n
Readmission = No
%
n
%
None
11 558
42.2
57 545
42.6
Trivial
3041
11.1
14 924
11.0
Mild
3672
13.4
15 399
11.4
Moderate
883
3.2
2925
2.2
Severe
105
0.4
370
0.3
N/A
7926
29.0
43 282
32.0
Although the differences in readmission rates were small between groups, because of the large sample sizes, all P values were 50% (P=0.007). AFib indicates atrial fibrillation; CHF, congestive heart failure; CV, cardiovascular; CVA, cerebrovascular accident; IABP, intra-aortic balloon pump; MI, myocardial infarction; N/A, not available; NYHA, New York Heart Association; PCI, percutaneous coronary intervention; and VT/VF, ventricular tachycardia/ ventricular fibrillation.
transfers from the index CABG hospital to another acute-care facility. Follow-up of transfer patients commenced on the day of discharge from the last hospital in the transfer chain. Readmissions were always attributed to the hospital that initially performed the CABG.
Risk Adjustment Candidate risk adjustment covariates were selected based on prior literature including STS 2008 CABG mortality and morbidity models13 and previous CMS readmission measures.5–7 Race and ethnicity were candidate covariates in previous STS models but were excluded from the current measure, in accordance with National Quality Forum measure criteria and CMS practice, to avoid potentially masking care disparities. Candidate and final model covariates, and their mathematical representation in the model, are summarized in Table III and Appendix II in the online-only Data Supplement.
Variable Selection The final list of covariates selected by the surgeon panel included all covariates that were either (1) selected at the 0.05 level in the original full sample for at least 1 calendar year; or (2) were selected in at least 50% of bootstrap replicates at the 0.05 level in at least 1 calendar year. Details of the variable selection process are provided in onlineonly Data Supplement Appendix III.
Assessment of Model Calibration Before estimating hospital performance, we assessed the adequacy of the proposed case mix adjustment model. Coefficients from the final marginal model were reestimated by using only 2008 data, and then tested in a separate sample of 2009 data. To assess calibration, we compared observed versus expected all-cause 30-day readmission rates within patient subgroups based on deciles of predicted risk in 2009 data. We also used 2009 data to estimate the C-index of the model estimated from 2008 data. Although a low C-index does not imply that the model is misspecified or that hospital comparisons will be biased, it may serve as a benchmark for comparing alternative models for the same end point in the same target population.
Hospital-Specific Risk-Standardized Readmission Rates To estimate hospital-specific performance, the selected covariates were entered into a hierarchical logistic regression model with hospital-specific random intercept parameters. This approach explicitly models hospital-level differences while adjusting for case mix. These hierarchical models were used to estimate hospital-specific risk-standardized readmission rates (RSRRs) using methodologies identical to the existing CMS readmission measures.5–7 RSRRs were calculated for each hospital as the ratio of their predicted (analogous to observed, but incorporating both case mix and an estimate of hospital-specific effect) to their expected number of readmission events (calculated by using the national average effect in place of the hospital-specific effect), multiplied by the national unadjusted readmission rate (see online-only Data Supplement Appendix IV). This hierarchical RSRR is analogous to the commonly used ratio of observed-toexpected outcomes. It reflects the performance of a specific hospital with its unique mix of patients in comparison with the performance of an average hospital having the same patient mix. Hospitals were classified as better than expected if the 95% interval of their RSRR fell entirely below the aggregate readmission rate, as worse than expected if the 95% interval of the RSRR fell entirely above the aggregate readmission rate, and same as expected if their 95% interval overlapped the overall aggregate readmission rate.
Measure Reliability An important criterion for evaluating hospital performance measures is the extent to which between-hospital variation in the measure is explained by true differences versus chance variation (ie, signal versus noise).14 Measures with a high proportion of signal variance are more reliable and useful because of their higher power to discriminate high and low performers. Measures dominated by chance variation may lead to unfair conclusions and misinform consumers. To assess the reliability of the CABG readmission measure, we estimated the percentage of overall variation explained by true signal rather than random noise, using a Bayesian version of the hierarchical logistic regression model and WinBUGS software (see online-only Data Supplement Appendix V).
Results
Missing Data Missing data for model covariates were rare,
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/130/5/399
Data Supplement (unedited) at: http://circ.ahajournals.org/content/suppl/2014/06/10/CIRCULATIONAHA.113.007541.DC1.html
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
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Health Services and Outcomes Research Development of a Clinical Registry-Based 30-Day Readmission Measure for Coronary Artery Bypass Grafting Surgery David M. Shahian, MD; Xia He, MS; Sean M. O’Brien, PhD; Frederick L. Grover, MD; Jeffrey P. Jacobs, MD; Fred H. Edwards, MD; Karl F. Welke, MD; Lisa G. Suter, MD; Elizabeth Drye, MD, SM; Cynthia M. Shewan, PhD; Lein Han, PhD; Eric Peterson, MD, MPH Background—Reducing readmissions is a major healthcare reform goal, and reimbursement penalties are imposed for higherthan-expected readmission rates. Most readmission risk models and performance measures are based on administrative rather than clinical data. Methods and Results—We examined rates and predictors of 30-day all-cause readmission following coronary artery bypass grafting surgery by using nationally representative clinical data (2008–2010) from the Society of Thoracic Surgeons National Database linked to Medicare claims records. Among 265 434 eligible Medicare records, 226 960 (86%) were successfully linked to Society of Thoracic Surgeons records; 162 572 (61%) isolated coronary artery bypass grafting admissions constituted the study cohort. Logistic regression was used to identify readmission risk factors; hierarchical regression models were then estimated. Risk-standardized readmission rates ranged from 12.6% to 23.6% (median, 16.8%) among 846 US hospitals with ≥30 eligible cases and ≥90% of eligible Centers for Medicare and Medicaid Services records linked to the Society of Thoracic Surgeons database. Readmission predictors (odds ratios [95% confidence interval]) included dialysis (2.02 [1.87–2.19]), severe chronic lung disease (1.58 [1.49–1.68]), creatinine (2.5 versus 1.0 or lower:1.49 [1.41–1.57]; 2.0 versus 1.0 or lower: 1.37 [1.32–1.43]), insulin-dependent diabetes mellitus (1.45 [1.39–1.51]), obesity in women (body surface area 2.2 versus 1.8: 1.44 [1.35–1.53]), female sex (1.38 [1.33–1.43]), immunosuppression (1.38 [1.28–1.49]), preoperative atrial fibrillation (1.36 [1.30–1.42]), age per 10-year increase (1.36 [1.33–1.39]), recent myocardial infarction (1.24 [1.08–1.42]), and low body surface area in men (1.22 [1.14–1.30]). C-statistic was 0.648. Fifty-two hospitals (6.1%) had readmission rates statistically better or worse than expected. Conclusions—A coronary artery bypass grafting surgery readmission measure suitable for public reporting was developed by using the national Society of Thoracic Surgeons clinical data linked to Medicare readmission claims. (Circulation. 2014;130:399-409.) Key Words: coronary artery bypass ◼ patient readmission ◼ registries ◼ risk adjustment
T
he reduction of hospital readmissions is a major goal of healthcare reform and has been the focus of Medicare Payment Advisory Commission reports.1,2 The Patient Protection and Affordable Care Act incorporates a Hospital Readmissions Reduction Program, which includes public reporting of risk-standardized readmission rates and progressively increasing reimbursement penalties for hospitals with higher-than-expected unplanned readmission rates. Bundled payment initiatives may create additional incentives for improved discharge planning and postdischarge care
coordination by making the index hospital financially responsible for subsequent readmissions.3
Clinical Perspective on p 409 Adjustment for differences in case mix is necessary to fairly implement readmission reduction initiatives and avoid inappropriately penalizing providers or misleading consumers.4 Previous readmission risk models developed for use by the Centers for Medicare and Medicaid Services (CMS) have been based on Medicare administrative claims data,5–7 and a claims-based
Received November 15, 2013; accepted May 27, 2014. From the Massachusetts General Hospital and Harvard Medical School, Boston, MA (D.M.S.); Duke Clinical Research Institute, Durham, NC (X.H., S.M.O., E.P.); University of Colorado School of Medicine-Anschutz Medical Campus, Aurora, CO, and Denver Department of Veterans Affairs Medical Center, Denver, CO (F.L.G.); All Children’s Hospital, John Hopkins University, Saint Petersburg, FL (J.P.J.); University of Florida College of Medicine, Jacksonville, FL (F.H.E.); Children’s Hospital of Illinois and the University of Illinois College of Medicine, Peoria, IL (K.F.W.); Yale-New Haven Health Services Corporation Center for Outcomes Research and Evaluation (CORE) and Yale School of Medicine, New Haven, CT (L.G.S., E.D.); Society of Thoracic Surgeons, Chicago, IL (C.M.S.); and Centers for Medicare and Medicaid Services, Baltimore, MD (L.H.). The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA. 113.007541/-/DC1. Correspondence to David M. Shahian, MD, Department of Surgery and Center for Quality and Safety, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114. E-mail [email protected] © 2014 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.113.007541
Downloaded from http://circ.ahajournals.org/ by guest on November 3, 2014 399
400 Circulation July 29, 2014 coronary artery bypass grafting surgery (CABG) readmission measure is under development. However, some previous CABG studies have raised concerns about the accuracy of profiling measures based on administrative data8,9 because of their inadequate risk adjustment, case misclassification, and other issues. Although claims-based risk adjustment, with its inherent limitations, may be the only measure development option for many conditions, the vast majority of CABG procedures performed in the United States are entered into the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database. This registry contains detailed clinical data needed for robust risk adjustment. With the use of previously described algorithms,10 it has also been successfully linked to various claims data sources that can be used to determine readmissions, longterm outcomes, and resource use. Accordingly, STS and the Duke Clinical Research Institute, in collaboration with the Yale Center for Outcomes Research and Evaluation group and CMS, have developed a 30-day all-cause readmission measure based on STS Adult Cardiac Surgery Database clinical data linked to Medicare readmission data.
Methods Data Sources The STS Adult Cardiac Surgery Database provides distinct advantages in comparison with administrative data. These include uniform cohort definitions, standardized risk factor specifications designed by surgical content experts, and rigorous quality control. Eight to ten percent of participating sites undergo extensive annual external audits, and data accuracy is high (97% overall). These advantages are shared by other robust clinical registries such as those maintained by the State of New York and used for public reporting.11 STS Adult Cardiac Surgery Database data elements include preoperative clinical characteristics, operative techniques, complications, and mortality. With the use of these data, risk-adjusted outcomes feedback reports are provided to >1050 cardiac surgery programs (90%–95% of all US programs). The Medicare Part A inpatient database contains fee-for-service claims for reimbursement of hospital inpatient facility costs. Medicare
data elements used for this project include dates of admission and discharge, patient demographics, International Classification of Diseases, Ninth Revision diagnosis and procedure codes, and hospital provider number. The Medicare Enrollment Database contains patient demographic information, vital status, and monthly indicators of fee-for-service eligibility (to implement measure inclusion/exclusion criteria).
Cohort Development, Linkage Methodology, and Exclusions Derivation of the isolated CABG study cohort and linkage of STS and Medicare data are summarized in Figure 1 and described in detail in online-only Data Supplement Appendix I. The STS algorithm for defining an isolated CABG in Database version 2.61 (used for these analyses) is available on the STS website.12 Comparison of CMS and STS records that did or did not link is provided in Tables I and II in the online-only Data Supplement. From 2008 to 2010, a total of 265 434 CMS admissions from 1172 hospitals met eligibility criteria to be considered for linkage to the STS database. Among 237 790 index CABG admissions from 1012 STS-participating hospitals, 226 960 admissions linked to an STS record and 10 830 did not link. Nonlinked patients were more often female (36.2% versus 31.9%), black (6.8% versus 4.6%), and more likely to be readmitted (20.2% versus 18.6%). We also performed a complementary analysis of 2008 to 2010 CMS admissions that could be linked to STS records of patients ≥65 years of age who met the definition of isolated CABG. Among 262 277 STS records at 1024 centers that submitted CMS claims during the study period, 223 594 linked to a CMS admission and 38 683 did not. Nonlinked records were more often male (70.6% versus 69.5%), black (6.6% versus 5.3%), and diabetic patients with smaller body surface areas. They less often had chronic lung disease, peripheral vascular disease, or cerebrovascular disease, and more often had creatinine levels of 2 wk)
No CVA
2690
9.8
9971
7.4
Recent CVA (≤ 2 wk)
57
0.2
225
0.2
No
26 220
95.8
131 795
97.5
Yes
1124
4.1
3337
2.5
25 904
94.7
128 854
95.3
1328
4.9
5924
4.4
Previous CV interventions Number of previous CV surgeries
No previous CV surgery 1 prior CV surgery ≥2 prior CV surgeries
Prior PCI
No PCI PCI - within 6 h
114
0.4
357
0.3
20 040
73.2
102 283
75.6
255
0.9
1003
0.7 (Continued )
Downloaded from http://circ.ahajournals.org/ by guest on November 3, 2014
402 Circulation July 29, 2014 Table 1. Continued Readmission = Yes Variable
Level
Readmission = No
n
%
n
%
Elective
10 858
39.7
61 206
45.3
Urgent
15 230
55.7
69 124
51.1
1233
4.5
4710
3.5
34
0.1
133
0.1
13 714
50.1
77 782
57.5 17.3
Preoperative cardiac status Acuity status
Emergent Emergent salvage Myocardial infarction
Angina Arrhythmia
No prior MI MI > 21 days
5067
18.5
23 429
MI 8–21 days
1379
5.0
4597
3.4
MI 1–7 days
6005
21.9
24 540
18.2
MI > 6 and < 24 h
681
2.5
2930
2.2
MI ≤ 6 h
414
1.5
1516
1.1
No
11 173
40.8
48 975
36.2
Yes
15 956
58.3
85 111
62.9
No arrhythmia
23 073
84.3
120 817
89.4
21
0.1
70
0.1
AFib/flutter Heart block Sustained VT/VF Multiple types Preop IABP
No Yes
Congestive heart failure
No CHF CHF NYHA-I
Left main disease > 50% Ejection fraction, %
Mitral insufficiency
6
0.0
2166
1.6
3716
13.6
12 038
8.9
25 049
91.5
126 119
93.3
2292
8.4
8987
6.6
20 862
76.2
113 634
84.0
362
1.3
1456
1.1
CHF NYHA-II
1555
5.7
6082
4.5
2701
9.9
8575
6.3
CHF NYHA-IV
1761
6.4
5078
3.8
96
0.4
303
0.2
None
70
0.3
319
0.2
One
934
3.4
4943
3.7
Two
5262
19.2
26 714
19.8
Three
21 076
77.0
103 098
76.3
No
17 402
63.6
87 159
64.5
Yes
9888
36.1
47 702
35.3
< 25
962
3.5
3449
2.6
25–34
2307
8.4
8224
6.1
35–44
3879
14.2
16 300
12.1
45–54 Aortic insufficiency
0.0 1.9
CHF NYHA-III CHF missing NYHA Number of diseased coronary vessels
6 515
5807
21.2
29 726
22.0
≥ 55
13 353
48.8
72 718
53.8
None
15 729
57.5
76 852
56.8
Trivial
1685
6.2
7366
5.4
Mild
1886
6.9
8148
6.0
Moderate
401
1.5
1562
1.2
Severe
32
0.1
144
0.1 29.9
N/A
7453
27.2
40 398
None
11 336
41.4
58 688
43.4
Trivial
3074
11.2
15 417
11.4
Mild
5077
18.6
21 563
15.9
Moderate
1682
6.1
5979
4.4
Severe
179
0.7
588
N/A
5907
21.6
32 535
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0.4 24.1 (Continued )
Shahian et al CABG Readmission Measure Based on Clinical Data 403 Table 1. Continued Readmission = Yes Variable
Level
Tricuspid insufficiency
n
Readmission = No
%
n
%
None
11 558
42.2
57 545
42.6
Trivial
3041
11.1
14 924
11.0
Mild
3672
13.4
15 399
11.4
Moderate
883
3.2
2925
2.2
Severe
105
0.4
370
0.3
N/A
7926
29.0
43 282
32.0
Although the differences in readmission rates were small between groups, because of the large sample sizes, all P values were 50% (P=0.007). AFib indicates atrial fibrillation; CHF, congestive heart failure; CV, cardiovascular; CVA, cerebrovascular accident; IABP, intra-aortic balloon pump; MI, myocardial infarction; N/A, not available; NYHA, New York Heart Association; PCI, percutaneous coronary intervention; and VT/VF, ventricular tachycardia/ ventricular fibrillation.
transfers from the index CABG hospital to another acute-care facility. Follow-up of transfer patients commenced on the day of discharge from the last hospital in the transfer chain. Readmissions were always attributed to the hospital that initially performed the CABG.
Risk Adjustment Candidate risk adjustment covariates were selected based on prior literature including STS 2008 CABG mortality and morbidity models13 and previous CMS readmission measures.5–7 Race and ethnicity were candidate covariates in previous STS models but were excluded from the current measure, in accordance with National Quality Forum measure criteria and CMS practice, to avoid potentially masking care disparities. Candidate and final model covariates, and their mathematical representation in the model, are summarized in Table III and Appendix II in the online-only Data Supplement.
Variable Selection The final list of covariates selected by the surgeon panel included all covariates that were either (1) selected at the 0.05 level in the original full sample for at least 1 calendar year; or (2) were selected in at least 50% of bootstrap replicates at the 0.05 level in at least 1 calendar year. Details of the variable selection process are provided in onlineonly Data Supplement Appendix III.
Assessment of Model Calibration Before estimating hospital performance, we assessed the adequacy of the proposed case mix adjustment model. Coefficients from the final marginal model were reestimated by using only 2008 data, and then tested in a separate sample of 2009 data. To assess calibration, we compared observed versus expected all-cause 30-day readmission rates within patient subgroups based on deciles of predicted risk in 2009 data. We also used 2009 data to estimate the C-index of the model estimated from 2008 data. Although a low C-index does not imply that the model is misspecified or that hospital comparisons will be biased, it may serve as a benchmark for comparing alternative models for the same end point in the same target population.
Hospital-Specific Risk-Standardized Readmission Rates To estimate hospital-specific performance, the selected covariates were entered into a hierarchical logistic regression model with hospital-specific random intercept parameters. This approach explicitly models hospital-level differences while adjusting for case mix. These hierarchical models were used to estimate hospital-specific risk-standardized readmission rates (RSRRs) using methodologies identical to the existing CMS readmission measures.5–7 RSRRs were calculated for each hospital as the ratio of their predicted (analogous to observed, but incorporating both case mix and an estimate of hospital-specific effect) to their expected number of readmission events (calculated by using the national average effect in place of the hospital-specific effect), multiplied by the national unadjusted readmission rate (see online-only Data Supplement Appendix IV). This hierarchical RSRR is analogous to the commonly used ratio of observed-toexpected outcomes. It reflects the performance of a specific hospital with its unique mix of patients in comparison with the performance of an average hospital having the same patient mix. Hospitals were classified as better than expected if the 95% interval of their RSRR fell entirely below the aggregate readmission rate, as worse than expected if the 95% interval of the RSRR fell entirely above the aggregate readmission rate, and same as expected if their 95% interval overlapped the overall aggregate readmission rate.
Measure Reliability An important criterion for evaluating hospital performance measures is the extent to which between-hospital variation in the measure is explained by true differences versus chance variation (ie, signal versus noise).14 Measures with a high proportion of signal variance are more reliable and useful because of their higher power to discriminate high and low performers. Measures dominated by chance variation may lead to unfair conclusions and misinform consumers. To assess the reliability of the CABG readmission measure, we estimated the percentage of overall variation explained by true signal rather than random noise, using a Bayesian version of the hierarchical logistic regression model and WinBUGS software (see online-only Data Supplement Appendix V).
Results
Missing Data Missing data for model covariates were rare,
