Empirically derived composite measures of surgical performance.
BACKGROUND
Individual quality measures have significant limitations for assessing surgical performance. Despite growing interest in composite measures, empirically-based methods for combining multiple domains of surgical quality are not well established.


OBJECTIVE
To develop and validate a composite measure of surgical performance that best describes variation in hospital mortality rates and forecasts future performance.


METHODS
Using the national Medicare claims database, we identified all patients undergoing aortic valve replacement in 2000 to 2001 (n = 53,120). To serve as input variables, we identified hospital-level predictors of mortality with aortic valve replacement, including hospital volume, complication rates, and mortality with other procedures. Hospital-specific predicted mortality rates were then determined using Bayesian-derived modeling techniques and assessed against subsequent hospital mortality (2002-2003).


RESULTS
Our composite measure explained 78% of the variation in aortic valve replacement mortality rates (2000-2001). The most important input variables were hospital volume, mortality with aortic valve replacement, and mortality for other high-risk cardiac procedures. The composite measure forecasted 70% of future hospital-level variation in mortality rates (2002-2003), and was substantially better in this regard than individual measures. Hospitals scoring in the bottom quintile on the composite measure in 2000 to 2001 had 2-fold higher mortality rates in 2002 to 2003 than hospitals in the top quintile (adjusted odds ratio, 1.97; 95% CI, 1.73-2.23).


CONCLUSIONS
Compared with individual surgical quality indicators, empirically derived composite measures are superior in explaining variation in hospital mortality rates and in forecasting future performance. Such measures could be useful for public reporting, value-based purchasing, or benchmarking for quality improvement purposes.
Staiger
Douglas O
DO
Department of Economics and the Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Hanover, New Hampshire, USA.
Dimick
Justin B
JB
Baser
Onur
O
Fan
Zhaohui
Z
Birkmeyer
John D
JD
D000368:Aged; D000369:Aged, 80 and over; D001021:Aortic Valve / Q000601:surgery*; D001499:Bayes Theorem; D005260:Female; D006302:Health Services Research / Q000706:statistics & numerical data; D019918:Heart Valve Prosthesis Implantation / Q000401:mortality*; D017052:Hospital Mortality; D006761:Hospitals / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D006278:Medicare / Q000706:statistics & numerical data; D011183:Postoperative Complications / Q000401:mortality; D019984:Quality Indicators, Health Care / Q000592:standards*; D012015:Reference Standards; D014481:United States