Comparing risk-prediction methods using administrative or clinical data in assessing excess in-hospital mortality in patients with acute myocardial infarction.
OBJECTIVES
To compare results of statistical process-control analyses of in-hospital deaths of patients with acute myocardial infarction by using either administrative or clinical data sources and prediction models, and to assess variation in results according to selected patient characteristics.


DESIGN
Retrospective, cross-sectional study comparing variable life-adjusted display (VLAD) curves derived by using administrative or clinical prediction models applied to a single patient sample.


PARTICIPANTS AND SETTING
Data from 467 consecutive patients admitted to a tertiary hospital in Queensland, between 1 July 2003 and 31 March 2006, with a coded discharge diagnosis of acute myocardial infarction.


MAIN OUTCOME MEASURE
Statistical estimates of cumulative lives gained or lost in excess of those predicted at the end of the study period.


RESULTS
The two prediction models, when applied to all patients, generated almost identical VLAD curves, showing a steadily increasing excess mortality over the study period, culminating in an estimated 11 excess deaths. Risk estimates for individual patients from each model were significantly correlated (r = 0.46, P < 0.001). After exclusion of misclassified cases, out-of-hospital cardiac arrests and deaths within 30 minutes of presentation, replotting the curves reversed the mortality trend and yielded, depending on the model, a net gain of three or seven lives. After further exclusion of transfers in from other hospitals and patients whose care had a palliative or conservative intent, the net gain increased to seven or 10 lives.


CONCLUSION
Appropriate patient selection is more important than choice of dataset or risk-prediction model when statistical process-control methods are used to flag unfavourable mortality trends suggestive of suboptimal hospital care.