Mortality after cardiac bypass surgery: prediction from administrative versus clinical data.

BACKGROUND

Risk-adjusted outcome rates frequently are used to make inferences about hospital quality of care. We calculated risk-adjusted mortality rates in veterans undergoing isolated coronary artery bypass surgery (CABS) from administrative data and from chart-based clinical data and compared the assessment of hospital high and low outlier status for mortality that results from these 2 data sources.

STUDY POPULATION

We studied veterans who underwent CABS in 43 VA hospitals between October 1, 1993, and March 30, 1996 (n=15,288).

METHODS

To evaluate administrative data, we entered 6 groups of International Classification of Diseases (ICD)-9-CM codes for comorbid diagnoses from the VA Patient Treatment File (PTF) into a logistic regression model predicting postoperative mortality. We also evaluated counts of comorbid ICD-9-CM codes within each group, along with 3 common principal diagnoses, weekend admission or surgery, major procedures associated with CABS, and demographic variables. Data from the VA Continuous Improvement in Cardiac Surgery Program (CICSP) were used to create a separate clinical model predicting postoperative mortality. For each hospital, an observed-to-expected (O/E) ratio of mortality was calculated from (1) the PTF model and (2) the CICSP model. We defined outlier status as an O/E ratio outside of 1.0 (based on the hospital's 90% confidence interval). To improve the statistical and predictive power of the PTF model, selected clinical variables from CICSP were added to it and outlier status reassessed.

RESULTS

Significant predictors of postoperative mortality in the PTF model included 1 group of comorbid ICD-9-CM codes, intraortic balloon pump insertion before CABS, angioplasty on the day of or before CABS, weekend surgery, and a principal diagnosis of other forms of ischemic heart disease. The model's c-index was 0.698. As expected, the CICSP model's predictive power was significantly greater than that of the administrative model (c=0.761). The addition of just 2 CICSP variables to the PTF model improved its predictive power (c=0.741). This model identified 5 of 6 high mortality outliers identified by the CICSP model. Additional CICSP variables were statistically significant predictors but did not improve the assessment of high outlier status.

CONCLUSIONS

Models using administrative data to predict postoperative mortality can be improved with the addition of a very small number of clinical variables. Limited clinical improvements of administrative data may make it suitable for use in quality improvement efforts.