Severity-adjusted death rates for coronary artery bypass graft (CABG) surgery by provider are published throughout the country. Whether five severity measures rated severity differently for identical patients was examined in this study.
Two severity measures rate patients using clinical data taken from the first two hospital days (MedisGroups, physiology scores); three use diagnoses and other information coded on standard, computerized hospital discharge abstracts (Disease Staging, Patient Management Categories, all patient refined diagnosis related groups). The database contained 7,764 coronary artery bypass graft patients from 38 hospitals with 3.2% in-hospital deaths. Logistic regression was performed to predict deaths from age, age squared, sex, and severity scores, and c statistics from these regressions were used to indicate model discrimination. Odds ratios of death predicted by different severity measures were compared.
Code-based measures had better c statistics than clinical measures: all patient refined diagnosis related groups, c = 0.83 (95% C.I. 0.81, 0.86) versus MedisGroups, c = 0.73 (95% C.I. 0.70, 0.76). Code-based measures predicted very different odds of dying than clinical measures for more than 30% of patients. Diagnosis codes indicting postoperative, life-threatening conditions may contribute to the superior predictive power of code-basedmeasures.
Clinical and code-based severity measures predicted different odds of dying for many coronary artery bypass graft patients. Although code-based measures had better statistical performance, this may reflect their reliance on diagnosis codes for life-threatening conditions occurring late in the hospitalization, possibly as complications of care. This compromises their utility for drawing inferences about quality of care based on severity-adjusted coronary artery bypass graft death rates.
*From the Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, the Charles A. Dana, Research Institute, and the Harvard-Thorndike Laboratory, Boston, Massachusetts.
†From the Health Care Research Unit, Section of General Internal Medicine, Evans Memorial Department of Clinical Research and Medicine, Boston University Medical Center, Boston, Massachusetts.
‡From the Health Care Management Program and Operations, Management Department, School of Management, Boston University, Boston, Massachusetts.
§From the Division of General Internal Medicine, Section of Health Policy and Research, Department of Medicine, Brigham and Women's Hospital, Department of Healthcare Policy, Harvard Medical School, Boston, Massachusetts.
This research was supported by the Agency for Health Care Policy and Research, under grant no. ROI HS06742-03. The views expressed are solely those of the authors.
Address correspondence to: Dr. Lisa Iezzoni, Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center East Campus LY-326, 330 Brookline Avenue, Boston, MA 02215.