Key result
Cardiac MRI-based machine learning provides superior HF hospitalization prediction over basic clinical models.
Why the study?
Contrast agents are often contraindicated in HFpEF due to concomitant kidney disease, making the prognostic value of noncontrast cardiac MRI for predicting heart failure hospitalization clinically important.
Does a noncontrast cardiac MRI-based machine learning model improve prediction of heart failure hospitalization in patients with HFpEF compared to a basic clinical model?
Observational (n=203)
Does a noncontrast cardiac MRI-based machine learning model improve prediction of heart failure hospitalization in patients with HFpEF compared to a basic clinical model?
A machine learning model incorporating noncontrast cardiac MRI markers (specifically LA and RV strains) significantly improves the prediction of future heart failure hospitalization in HFpEF patients compared to clinical and echocardiographic data alone.
BACKGROUND: Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important. PURPOSE: To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization. STUDY TYPE: Retrospective. POPULATION: A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). FIELD STRENGTH: A 1.5 T, balanced steady-state free precession (bSSFP) sequence. ASSESSMENT: Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization. STATISTICAL TESTS: ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant. RESULTS: During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and -15%, respectively. DATA CONCLUSIONS: Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Küçükseymen et al. (2021) conducted an observational in Heart failure with preserved ejection fraction (HFpEF) (n=203). Cardiac MRI-based machine learning model vs. Basic clinical machine learning model was evaluated on Heart failure hospitalization. A cardiac MRI-based machine learning model provided significantly superior prediction of heart failure hospitalization compared to a basic clinical model (AUC 0.81 vs 0.64).
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