Identifying the Impact of Systolic Dysfunction on CKD Risk in HFpEF Patients

Chronic Kidney Disease (CKD), common in HFpEF, is tied to congestion and elevated filling pressures, with subtle systolic dysfunction unexplored. This study used machine learning to assess left ventricular systolic function as a CKD predictor in HFpEF.

Franchini K, presented a session held at the American Heart Association (AHA) from 16^th -18^th November 2024, in Chicago, Illinois that discussed the predictive power of left ventricular systolic function for CKD in patients with HFpEF, using advanced machine learning (ML) techniques.

497 patients referred to a specialized HFpEF clinic were analyzed. Using machine learning models (Random Forest, XGBoost, Logistic Regression, Decision Tree), we assessed traditional risk factors (sex, age, hypertension, obesity, diabetes, and smoking) and renal function markers (eGFR and UACR) to estimate the risk of HFpEF based on the HFA-PEFF scoring system. Echocardiography-derived variables were used to predict CKD, defined as an eGFR <60 ml/min/1.73 m2. SHapley Additive exPlanations (SHAP) provided insights into model predictions.

The study population consisted primarily of women (67%) with an average age of 62 ± 12 years, increased BMI (33 ± 7 kg/m2), and prevalent hypertension (79%), dyslipidemia (71%), and type 2 diabetes (38%). The logistic regression model that incorporated traditional risk factors and renal markers achieved an AUROC of 0.80 ± 0.07, indicating significant impacts of lower eGFR and higher UACR on the risk of HFpEF (Panel A). The Random Forest model, which included variables derived from echocardiography, achieved the highest accuracy (AUROC 0.96 ± 0.02). Key systolic predictors of lower eGFR included reductions in tissue Doppler S wave and stroke volume index, as well as impaired left ventricular diastolic function (increases in E/e’ ratio) and elevated estimated pulmonary artery pressure (TRV – tricuspid regurgitation peak velocity) (Panel B).

This study identifies subtle systolic dysfunction, impaired diastolic function, and elevated pulmonary arterial pressure as key predictors of CKD in HFpEF. Advanced ML techniques offer insights into disease mechanisms. Early detection of these impairments can improve risk stratification and guide targeted interventions in the management of HFpEF.