Related Experiment Videos
Risk stratification after successful coronary revascularization
Masashi Goto1, Shun Kohsaka, Noriaki Aoki
1Kyoto University Health Service, Yoshida-Honmachi, Kyoto 606-8501, Japan. goto@msa.biglobe.ne.jp
Insights
Predicting long-term survival after coronary revascularization is crucial. A simple method using congestive heart failure history, age, and renal insufficiency effectively stratifies patients into risk groups.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
Background:
- Identifying patients needing intensive therapy post-coronary revascularization is essential.
- Existing survival predictors (hazard ratios) lack clinical practicality.
- A user-friendly method for predicting long-term survival is needed.
Purpose of the Study:
- To develop a practical method for predicting long-term survival in coronary revascularization patients.
- To create a user-friendly tool for risk stratification.
Main Methods:
- Retrospective analysis of 3331 patients undergoing first revascularization (1995-1999).
- Utilized a decision-tree induction algorithm for mortality prediction.
- Examined all-cause mortality during 3-year follow-up.
Main Results:
- Congestive heart failure history, age >65, and renal insufficiency were key predictors.
- Patients stratified into low, intermediate, and high-risk groups (2.0%–18.8% 3-year mortality).
- Decision tree model performance comparable to logistic regression (AUC 0.72 vs. 0.76).
Conclusions:
- Long-term mortality risk in revascularization patients is predictable.
- Three easily obtainable clinical predictors enable effective risk stratification.
- This method offers a practical approach for clinicians.
Background:
Clinicians treating coronary revascularization patients need to be able to identify those who require more intensive medical therapy or follow-up. However, predictors of survival after coronary revascularization are often reported in terms of hazard ratios, which are accurate but difficult to convert to concrete values. We sought to develop a more practical and user-friendly method of predicting long-term survival in revascularization patients.
Methods:
We used a decision-tree induction algorithm to retrospectively examine all-cause mortality during 3-year follow-up in 3331 consecutive patients with multivessel or single proximal left anterior descending coronary artery disease who underwent an isolated first revascularization by either coronary stenting or coronary artery bypass graft between 1995 and 1999.
Results:
Recursive partitioning of the derivation cohort by the algorithm indicated that the best single predictor of long-term mortality was history of congestive heart failure, followed by age greater than 65 years and the presence of renal insufficiency. With these three variables, patients were readily stratified into low-, intermediate-, and high-risk groups whose 3-year mortality risks ranged from 2.0% to 18.8%. Logistic regression revealed nine significant predictors of 3-year mortality, including two interaction terms. Areas under the receiver operation characteristic curve for prediction of 3-year mortality were not significantly different between the decision tree and the logistic regression models [0.72 (95% confidence interval, 0.69 to 0.75) vs. 0.76 (95% confidence interval, 0.73 to 0.80)].
Conclusions:
Long-term mortality risk in coronary revascularization patients can be estimated from three predictors that are easily obtained in clinical settings.
Related Concept Videos
01:27Coronary Artery Disease V: Interprofessional Care
01:28Acute Coronary Syndrome IV: Interprofessional Care
01:30Acute Coronary Syndrome I: Introduction
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:26Coronary Artery Disease IV: Preventive Measures