Risk-Adjusting Key Outcome Measures in a Clinical Quality PCI Registry: Development of a Highly Predictive Model Without the Need to Exclude High-Risk Conditions.

OBJECTIVES

This study sought to determine the most risk-adjustment model for 30-day all-cause mortality in order to report risk-adjusted outcomes. The study also explored whether the exclusion of extreme high-risk conditions of cardiogenic shock, intubated out-of-hospital cardiac arrest (OHCA), or the need for mechanical ventricular support affected the model's predictive accuracy.

BACKGROUND

Robust risk-adjustment models are a critical component of clinical quality registries, allowing outcomes to be reported in a fair and meaningful way. The Victorian Cardiac Outcomes Registry encompasses all 30 hospitals in the state of Victoria, Australia, that undertake percutaneous coronary intervention.

METHODS

Data were collected on 27,544 consecutive percutaneous coronary intervention procedures from 2014 to 2016. Twenty-eight patient risk factors and procedural variables were considered in the modeling process. The multivariable logistic regression analysis considered derivation and validation datasets, along with a temporal validation period.

RESULTS

The model included risk-adjustment for cardiogenic shock, intubated OHCA, estimated glomerular filtration rate, left ventricular ejection fraction, angina type, mechanical ventricular support, ≥80 years of age, lesion complexity, percutaneous access site, and peripheral vascular disease. The C-statistic for the derivation dataset was 0.921 (95% confidence interval: 0.905 to 0.936), with C-statistics of 0.931 and 0.934 for 2 validation datasets reflecting the 2014 to 2016 and 2017 periods. Subgroup modeling excluding cardiogenic shock and intubated OHCA provided similar risk-adjusted outcomes (p = 0.32).

CONCLUSIONS

Our study has developed a highly predictive risk-adjustment model for 30-day mortality that included high-risk presentations. Therefore, we do not need to exclude high-risk cases in our model when determining risk-adjusted outcomes.