Risk stratification for long-term mortality after percutaneous coronary intervention
Chuntao Wu1, Fabian T Camacho, Spencer B King
1From the Penn State Hershey College of Medicine, Hershey, PA (C.W., F.T.C.); St. Joseph's Health System, Atlanta, GA (S.B.K.); Johns Hopkins Medical Center, Baltimore, MD (G.W.); Mayo Clinic, Rochester, MN (D.R.H.); United Health Services, Binghamton, NY (N.J.S.); Geisinger Health System, Danville, PA (P.B.B.); Mt. Sinai Medical Center, New York, NY (S.S.); Yale University School of Medicine, New Haven, CT (J.P.C.); Albany Medical College, Albany, NY (F.J.V.); Boston Medical Center, Boston, MA (A.K.J.); and University at Albany, State University of New York, Albany, NY (E.L.H.).
Insights
A new risk score accurately predicts long-term mortality after percutaneous coronary intervention (PCI). This simple tool, derived from a complex Cox model, uses preprocedural factors to assess patient risk.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- A validated, simple risk score for predicting long-term mortality after percutaneous coronary intervention (PCI) using preprocedural factors is lacking.
- Existing risk prediction models are often complex and not easily applicable in clinical practice.
Purpose of the Study:
- To develop and validate a simplified risk score for predicting long-term mortality after PCI.
- To identify key preprocedural risk factors associated with mortality post-PCI.
Main Methods:
- A Cox proportional hazards model was developed using a derivation sample of 11,897 patients who underwent PCI.
- The model was simplified to create a point-based risk score.
- The risk score was validated on a separate sample, and its predictive accuracy for 1, 3, and 5-year mortality was assessed using C statistics.
Main Results:
- The Cox model identified 12 significant risk factors for mortality, including age, BMI, multivessel disease, ejection fraction, hemodynamic state, and comorbidities.
- The simplified risk score demonstrated good agreement between observed and predicted risks of death.
- The C statistics for the model in the validation sample were 0.787 (1-year), 0.785 (3-year), and 0.773 (5-year), indicating good predictive accuracy.
Conclusions:
- A simple, point-based risk score can effectively predict long-term mortality after PCI.
- This score, derived from a Cox model, offers a practical tool for risk stratification in patients undergoing PCI.
Background:
A simple risk score to predict long-term mortality after percutaneous coronary intervention (PCI) using preprocedural risk factors is currently not available. In this study, we created one by simplifying the results of a Cox proportional hazards model.
Methods And Results:
A total of 11,897 patients who underwent PCI from October through December 2003 in New York State were randomly divided into derivation and validation samples. Patients' vital statuses were tracked using the National Death Index through the end of 2008. A Cox proportional hazards model was fit to predict death after PCI using the derivation sample, and a simplified risk score was created. The Cox model identified 12 separate risk factors for mortality including older age, extreme body mass indexes, multivessel disease, a lower ejection fraction, unstable hemodynamic state or shock, several comorbidities (cerebrovascular disease, peripheral vascular disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes mellitus, and renal failure), and a history of coronary artery bypass graft surgery. The C statistics of this model when applied to the validation sample were 0.787, 0.785, and 0.773 for risks of death within 1, 3, and 5 years after PCI, respectively. In addition, the point-based risk score demonstrated good agreement between patients' observed and predicted risks of death.
Conclusions:
A simple risk score created from a more complicated Cox proportional hazards model can be used to accurately predict a patient's risk of long-term mortality after PCI.
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