OBJECTIVES: Severity-adjusted @entity204 rates for coronary artery bypass graft (CABG) surgery by provider are published throughout the country. Whether five severity measures rated severity differently for identical @entity1 was examined in this study. METHODS: Two severity measures rate @entity1 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, @entity1 Management Categories, all @entity1 refined diagnosis related groups). The database contained 7,764 coronary artery bypass graft @entity1 from 38 hospitals with 3.2% in-hospital @entity204 . Logistic regression was performed to predict @entity204 from age, age squared, sex, and severity scores, and c statistics from these regressions were used to indicate model discrimination. Odds ratios of @entity204 predicted by different severity measures were compared. RESULTS: Code-based measures had better c statistics than clinical measures: all @entity1 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 @entity1 . Diagnosis codes indicting postoperative, life-threatening conditions may contribute to the superior predictive power of code-based measures. CONCLUSIONS: Clinical and code-based severity measures predicted different odds of dying for many coronary artery bypass graft @entity1 . 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 @entity204 rates.

Predicting in-hospital XXXX from coronary artery bypass graft surgery. Do different severity measures give different predictions?

@entity1 :: ('9606', 'Species') :: ['patients', 'patient', 'Patient'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death', 'deaths']
@entity204 :: (MESH:D003643,Disease) :: ['deaths']
