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Multicenter Study
. 2014 Jan 21;129(3):313-20.
doi: 10.1161/CIRCULATIONAHA.113.001773. Epub 2013 Oct 25.

Derivation and validation of a risk standardization model for benchmarking hospital performance for health-related quality of life outcomes after acute myocardial infarction

Suzanne V Arnold  1 , Frederick A Masoudi, John S Rumsfeld, Yan Li, Philip G Jones, John A Spertus
Affiliations
Multicenter Study

Derivation and validation of a risk standardization model for benchmarking hospital performance for health-related quality of life outcomes after acute myocardial infarction

Suzanne V Arnold et al. Circulation. .

Abstract

Background: Before outcomes-based measures of quality can be used to compare and improve care, they must be risk-standardized to account for variations in patient characteristics. Despite the importance of health-related quality of life (HRQL) outcomes among patients with acute myocardial infarction (AMI), no risk-standardized models have been developed.

Methods and results: We assessed disease-specific HRQL using the Seattle Angina Questionnaire at baseline and 1 year later in 2693 unselected AMI patients from 24 hospitals enrolled in the Translational Research Investigating Underlying disparities in acute Myocardial infarction Patients' Health status (TRIUMPH) registry. Using 57 candidate sociodemographic, economic, and clinical variables present on admission, we developed a parsimonious, hierarchical linear regression model to predict HRQL. Eleven variables were independently associated with poor HRQL after AMI, including younger age, previous coronary artery bypass graft surgery, depressive symptoms, and financial difficulties (R(2)=20%). The model demonstrated excellent internal calibration and reasonable calibration in an independent sample of 1890 AMI patients in a separate registry, although the model slightly overpredicted HRQL scores in the higher deciles. Among the 24 TRIUMPH hospitals, 1-year unadjusted HRQL scores ranged from 67-89. After risk-standardization, HRQL score variability narrowed substantially (range=79-83), and the group of hospital performance (bottom 20%/middle 60%/top 20%) changed in 14 of the 24 hospitals (58% reclassification with risk-standardization).

Conclusions: In this predictive model for HRQL after AMI, we identified risk factors, including economic and psychological characteristics, associated with HRQL outcomes. Adjusting for these factors substantially altered the rankings of hospitals as compared with unadjusted comparisons. Using this model to compare risk-standardized HRQL outcomes across hospitals may identify processes of care that maximize this important patient-centered outcome.

Keywords: myocardial infarction; quality of life; risk factors.

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Conflict of interest statement

Conflict of Interest Disclosure: Dr. Spertus owns the copyright to the Seattle Angina Questionnaire. The other authors have no disclosures to report.

Figures

Figure 1
Figure 1
Predictors of Long-Term Quality of Life After an Acute Myocardial Infarction
Figure 2
Figure 2. Calibration Plot for the HRQL Prediction Model in TRIUMPH (a) and PREMIER (b)
TRIUMPH Internal Validation: intercept of 0.94 (SE 3.44), a slope of 0.99 (SE 0.04), and R2 of 98%. PREMIER External Validation: intercept of 14.1 (SE 2.38; p-value [for difference from 0] <0.001), a slope of 0.82 (SE 0.03).
Figure 3
Figure 3
Unadjusted (a) and Risk-Standardized (b) 1-Year HRQL Outcomes by Site.
See this image and copyright information in PMC

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