In HFpEF patients, a multimodal assessment combining LVMi and ST2 predicts hospitalization risk with a six-fold increased risk in high-risk groups (AUC 0.861).
Does a multimodal strategy combining echocardiographic and biomarker data improve prediction of heart failure hospitalizations and symptom severity in patients with HFpEF?
Integrating echocardiographic markers of cardiac remodeling and diastolic dysfunction with circulating biomarkers provides superior predictive accuracy for HF hospitalization and symptom severity in HFpEF compared to individual parameters.
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Abstract Background Heart failure with preserved ejection fraction (HFpEF) presents diagnostic and prognostic challenges due to its heterogeneous pathophysiology. A multimodal strategy combining echocardiographic and biomarker data may enhance clinical risk stratification. Purpose To assess the combined value of echocardiographic and circulating biomarker parameters in predicting symptom severity and HF hospitalizations in patients with HFpEF. Methods We prospectively evaluated 88 patients with chronic HFpEF (mean age 73.7 ± 8.2 years; 60.2% female). Baseline data included clinical and echocardiographic variables alongside serum levels of NT-proBNP, ST2, IGFBP-7, and CA-125. Logistic regression and ROC analysis were used to assess predictive performance. Results During a median follow-up of 15.9 months, 17 out of 88 patients were hospitalized for HF. Among echocardiographic parameters, the strongest predictors of hospitalization were left ventricular mass index (LVMi ≥108 g/m²; AUC 0.805, sensitivity 88.9%, specificity 73%) and septal E/e′ ratio ≥15.6 (AUC 0.793, sensitivity 88.9%, specificity 81.4%). From the biomarker panel, ST2 had the highest predictive performance (AUC 0.78; cut-off 18.3 ng/mL), followed by NT-proBNP (AUC 0.699; cut-off 967 pg/mL) and CA-125 (AUC 0.665; cut-off 5.9 U/mL). In multivariate analysis, ST2 (HR 1.085; p=0.007), NT-proBNP (HR 1.001; p=0.032) and LVMi (HR 1.021; p=0.041) remained independent predictors of hospitalization. Moreover, a high-risk subgroup defined by LVMi ≥108 g/m², NT-proBNP ≥967 pg/mL, and ST2 ≥18.3 ng/mL exhibited a six-fold increased risk of hospitalization. A combined logistic regression model incorporating LVMi, septal E/e′, NT-proBNP, ST2, and CA-125 achieved excellent discriminative performance for predicting HF hospitalization, with an AUC of 0.861 (95% CI: 0.770–0.953; p0.0001). This integrative approach demonstrated a superior predictive accuracy when compared to any individual parameter. Regarding HF severity, CA-125 was the strongest independent predictor of NYHA class III (HR 1.109; p=0.046), suggesting a key role for subclinical congestion. A mathematical model incorporating E-wave velocity, septal E/e′, NT-proBNP, IGFBP-7, and CA-125, achieved robust discrimination (AUC 0.826) for NYHA class ≥III, reinforcing the value of a multimodal approach in assessing functional status. Conclusions In patients with HFpEF, echocardiographic markers of diastolic dysfunction and cardiac remodelling (particularly increased septal E/e′ ratio and elevated LVMi) show strong associations with symptom burden and are key predictors of HF hospitalization. When combined with circulating biomarkers such as NT-proBNP, ST2 and CA-125, their predictive accuracy improves significantly. This multimodal assessment highlights the pivotal role of cardiac imaging in evaluating functional status and future decompensation risk, reinforcing the utility of integrated multiparametric risk stratification in HFpEF.Regression model - HF hospitalization Regression model - NYHA class III
Profire et al. (Thu,) reported a other. In HFpEF patients, a multimodal assessment combining LVMi and ST2 predicts hospitalization risk with a six-fold increased risk in high-risk groups (AUC 0.861).