Abstract
Background: The role of cardiac magnetic resonance (CMR) in predictingoutcome in heart failure preserved ejection fraction (HFpEF) remains to be fully investigated. We sought to investigate CMR predictors for HF hospitalization in HFpEF patients using a machine learning risk model (ML).
Methods: In a retrospective study, we identified203 HFpEF patients (64±12 years of age, 48% women) who were referred for CMR. Left atrial (LA) and right ventricular (RV) strains were measured using CVI42® software. An explainable ML using XGBoost® was developed based on CMR and clinical data to predict future HF hospitalization as primary outcome. SHAP (SHapley Additive exPlanations) values were calculated to interpret contributions of different risk markers to outcome.
Results: During follow-up(50±39 months), 85 patients(42%) met the primary outcome. Demographics and ventricular functions were similar between groups with and without outcome (p>0.05). However, hospitalized patients had impaired LA (19.1±8.3% vs. 9.6±6.4%, p<0.001) and RV (-19.6±4.4% vs. -14.6±4.6%, p<0.001) strains. Figure 1A shows the performance of model with and without addition of strain data, demonstrating the incremental value of strain in predicting outcome. SHAP values demonstrated that LA and RV strains are the most prognostic predictors (Figure 1B&1C).
Conclusion: An explainable ML can identify HFpEF patients with high likelihood of hospitalization. SHAP analysis identifies LA and RV strains as major predictors of adverse outcome.