Enhanced prediction of mortality after percutaneous coronary intervention by consideration of general and neurological indicators.

OBJECTIVES

This study sought to improve methodology for predicting post-percutaneous coronary intervention (PCI) mortality.

BACKGROUND

Recently, an increased proportion of post-PCI deaths caused by noncardiac causes has been suggested, often in rapidly triaged patients resuscitated from sudden cardiac death or presenting with cardiogenic shock. Older risk adjustment algorithms may not adequately reflect these issues.

METHODS

Consecutive patients undergoing PCI from 2000 to 2009 were randomly divided into training (n = 8,966) and validation (n = 8,891) cohorts. The 2010 ACC-NCDR (American College of Cardiology-National Cardiovascular Data Registry) mortality algorithm was applied to the training cohort and its highest risk decile, separately. Variables describing general and neurological status at admission were then tested for their additional predictive capability and new algorithms developed. These were tested in the validation cohort, using receiver-operator characteristic curve, Hosmer-Lemeshow, and reclassification measures as principal outcome measures.

RESULTS

In-hospital mortality was 1.0%, of which 52.2% had noncardiac causes or major contributions. Baseline model C-statistics for the total and upper decile training cohorts were 0.904 and 0.830. The Aldrete score (addressing consciousness, respiration, skin color, muscle function, and circulation) and neurology scores added incremental information, resulting in improved validation cohort C-statistics (entire group: 0.883 to 0.914, p < 0.001; high-risk decile: 0.829 to 0.874, p < 0.001). Reclassification of the ACC-NCDR <90th and ≥90th risk percentiles by the new score yielded improved mortality prediction (p < 0.001 and p = 0.033, respectively).

CONCLUSIONS

Half of in-hospital deaths in this series were of noncardiac causation. Prediction of in-hospital mortality after PCI can be considerably improved over conventional models by the inclusion of variables describing general and neurological status.