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Risk Stratification and Outcome Prediction in Heart Failure Patients With Cardiac Implantable Electronic Devices
Keijiro Nakamura1, Kazutaka Aonuma2, Torsten Kayser3
1Division of Cardiovascular Medicine, Toho University Ohashi Medical Center Tokyo Japan.
Insights
Machine learning models accurately predict adverse outcomes in Japanese heart failure patients with cardiac devices. This approach enhances risk stratification and supports personalized treatment strategies for better patient management.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Heart failure (HF) prevalence is rising in Japan's aging population.
- Implantable cardioverter defibrillator and cardiac resynchronization therapy use is lower in Japan compared to Western countries.
- The HINODE study prospectively collected data on Japanese patients with cardiac devices.
Purpose of the Study:
- To develop interpretable machine learning (ML) models for improved risk stratification in Japanese HF patients.
- To identify key predictors of adverse outcomes, including HF hospitalization and all-cause mortality.
- To support personalized management strategies for HF patients with cardiac devices.
Main Methods:
- Utilized data from 332 HINODE participants with adequate data.
- Developed predictive models using XGBoost with 5-fold cross-validation.
- Employed Shapley Additive Explanations (SHAP) for feature importance and K-means clustering for risk stratification.
Main Results:
- Models demonstrated strong discrimination for HF events (AUC 0.83) and mortality (AUC 0.85).
- Key predictors identified included QRS duration, QT interval, left ventricular volumes, and medications.
- Two distinct risk clusters were identified: low-risk (n=236) and high-risk (n=86), with significantly different event rates.
Conclusions:
- Interpretable ML models accurately predict risk and enable phenotype-based stratification in Japanese HF patients.
- Findings support the use of ML for personalized management of HF patients with cardiac devices.
- This approach can help optimize treatment strategies in this growing patient population.
Background:
Heart failure (HF) is increasing in Japan's rapidly aging population, yet use of implantable cardioverter defibrillators and cardiac resynchronization therapy remains lower than in Western countries. Using data from HINODE, which prospectively evaluated Japanese patients with cardiac devices, we developed interpretable machine learning (ML) models to improve risk stratification and identify key predictors of adverse outcomes.
Methods And Results:
Among 354 HINODE participants, 332 with adequate data were analyzed. Predictive models (XGBoost; 5-fold cross-validation) targeted HF hospitalization and all-cause mortality. Missingness was handled with multiple imputation; calibration was assessed by calibration plots and Hosmer-Lemeshow tests. Model discrimination was strong (area under the curve 0.83 and 0.85 for HF events and mortality). Shapley additive explanations (SHAP) highlighted QRS duration, QT interval, left ventricular (LV) volumes, and selected medications as major contributors. Using top SHAP features, K-means (k=2) identified low-risk (n=236) and high-risk (n=86) clusters. The high-risk cluster had larger LV volumes, wider QRS, and higher event rates. Kaplan-Meier curves showed significant differences between clusters for HF events (15.7% vs. 47.7%, log-rank P<0.001) and mortality (8.1% vs. 20.9%; hazard ratio 2.58, 95% confidence interval 1.45-4.60). Performance was temporally stable across enrollment periods.
Conclusions:
Interpretable ML provided accurate risk prediction and phenotype-based stratification in Japanese HF patients with cardiac devices, supporting personalized management.
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