Abstract 4140208: Predicting 30-Day and 1-Year Mortality in Heart Failure with Preserved Ejection Fraction (HFpEF) using Electronic Health Record Data

INTRODUCTION: Heart failure with preserved ejection fraction (HFpEF) represents 50% of heart failure patients. Predictive models in HFpEF, specifically those derived from electronic health record (EHR) data, are less established. HYPOTHESIS: EHR models can predict 30-day and 1-year mortality in HFpEF patients. Machine learning (ML) models compared with traditional may show superior performance. AIM: Develop and compare prediction models for 30-day and 1-year mortality in HFpEF using EHR data, using traditional and ML techniques. Methods: Using MIMIC-IV data (EHR data from the Beth Israel Deaconess Medical Center in Boston) from 2008-2019, we identified patients aged ≥ 18 years admitted with a primary diagnosis of HFpEF using ICD-9 and 10 codes. Demographics, vital signs, prior diagnoses, and lab data were extracted. After preprocessing, data was partitioned into 70% training and 30% test. Prediction models from seven model classes were developed using a variety of imputation and oversampling techniques using 5-fold cross validation - Support Vector Classifier (SVC), Logistic Regression, Lasso Regression, Elastic Net, Random Forest, HistGradient Boosting Classifier, and XGBoost. Model performance was compared using several metrics. SHapley Additive exPlanations (SHAP) analysis was used to assess individual feature influence on model output. Results: Among 3910 individual hospitalizations for HFpEF the 30-day mortality was 6.3% and 1-year mortality was 29.2%. Performance metrics of the best model in each class is shown in Table 1. By AUCs, Lasso and Elastic Net Regression models provided superior prediction for 30-day mortality (AUCs of 0.79, each), whereas Random Forest and HistGradient Boosting Classifier for 1-year mortality (AUCs of 0.78 each). Overall, considering all metrics, Elastic Net and Lasso regression models gave a good combination of sensitivity, specificity and AUC enhancing their clinical applicability. SHAP analyses showed age, NT-proBNP and sodium levels, WBC, and platelet counts as key drivers of these predictions. Conclusion: Models derived from EHR data, which is important for implementability, predict mortality after HFpEF hospitalization with good performance comparable to models derived from registry or trial data.