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Published on: January 28, 2020
Prediction of Cardiac and Noncardiac Mortality After Percutaneous Coronary Intervention
Daniel B Spoon1, Ryan J Lennon1, Peter J Psaltis1
1From the Division of Cardiovascular Diseases (D.B.S., A.P., D.R.H., A.L., C.S.R., B.J.G., H.H.T., M.S., R.G.) and Division of Biomedical Statistics and Informatics (R.J.L.), Mayo Clinic, Rochester, MN; and Department of Medicine, University of Adelaide, Adelaide, Australia (P.J.P.).
Insights
New risk models predict cardiac and noncardiac mortality after percutaneous coronary intervention (PCI). These models improve long-term outcome prediction beyond all-cause mortality for PCI patients.
Area of Science:
- Cardiology
- Outcomes Research
- Biostatistics
Background:
- Current risk models for percutaneous coronary intervention (PCI) primarily focus on all-cause mortality.
- There is a need for specialized models to predict specific causes of long-term mortality post-PCI.
Purpose of the Study:
- To develop and validate novel risk models for predicting cardiac and noncardiac mortality after PCI.
- To provide more granular risk stratification for patients undergoing PCI.
Main Methods:
- Retrospective analysis of 6636 patients undergoing PCI from 2003-2008.
- Utilized Fine and Gray extension of Cox proportional hazards models for cause-specific cumulative incidence.
- Developed integer-based risk scores for cardiac and noncardiac mortality.
Main Results:
- Identified key predictors for cardiac death (e.g., age, BMI, ejection fraction, heart failure history).
- Identified key predictors for noncardiac death (e.g., age, medication index, BMI, smoking, Charlson indices).
- Models demonstrated good predictive accuracy with corrected c-statistics of 0.82 for cardiac and 0.77 for noncardiac mortality at 5 years.
Conclusions:
- Novel risk models effectively predict long-term cardiac and noncardiac mortality post-PCI.
- These models offer improved risk stratification compared to existing all-cause mortality models.
Background:
Current risk models for predicting long-term mortality after percutaneous coronary intervention are restricted to all-cause mortality. We sought to develop novel risk models for the prediction of cardiac and noncardiac mortality after percutaneous coronary intervention.
Methods And Results:
We retrospectively evaluated patients who underwent index percutaneous coronary intervention at Mayo Clinic from 2003 to 2008. Long-term deaths were ascertained through scheduled prospective surveillance. Cause of death was determined via telephone interviews, medical records, and autopsy reports. Fine and Gray extension of Cox proportional hazards models was used to model cause-specific cumulative incidence. Candidate variables and interactions were chosen a priori, without variable selection methods. Resulting models were mapped to an integer-based risk score. The study comprised 6636 patients followed up over a median of 62 months (25th, 75th percentiles: 45, 77 months). There were 1488 deaths, 518 (35%) cardiac, 938 (63%) noncardiac, and 32 (2%) unknown. The 5-year predicted cardiac mortality ranged from 0.6% to 97%, with a corrected c-statistic of 0.82. Risk factors for cardiac death included age, body mass index, ejection fraction, and history of congestive heart failure. The integer score for noncardiac death included age, medicine index, body mass index, current smoker, noncardiac Charlson index and cardiac Charlson index, and accommodated significant age-based interactions for smoking and the 2 Charlson indices. Predicted noncardiac mortality at 5 years ranged from 0.2% to 81%, with a corrected c-statistic of 0.77.
Conclusions:
We report novel risk models to predict cardiac and noncardiac long-term mortality after percutaneous coronary intervention.
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