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<p>Left-ventricular cardiac power output (CPO), stroke-work index (LVSWI), and ventriculo-arterial coupling (VAC) capture cardiac energetics and ventricular–vascular interaction, however their bedside prognostic value in acute decompensated heart failure (ADHF) remains uncertain. To determine whether machine-learning (ML) phenotyping based on these Doppler-derived indices refines risk stratification beyond conventional echocardiography. This prospective study enrolled 500 ADHF patients with LVEF &lt; 40%. LVSWI, CPO, and VAC were calculated from admission echocardiography. Standardized values underwent K-means clustering, and phenotype outcomes were compared using Kaplan–Meier curves and Cox regression. A traditional logistic model (EF, TAPSE, LVEDP, RVSP, RAP) was contrasted with an augmented model adding the three indices. A random forest classifier using LVSWI, CPO, and VAC predicted 6-month mortality and was internally validated. Clustering yielded two phenotypes: low-output/uncoupled (<i>n</i> = 262) and preserved-output/coupled (<i>n</i> = 238). The low-output group had higher LVEDP, RVSP, RAP, and systemic vascular resistance (all <i>p</i> &lt; 0.05) despite a similar EF. Six-month mortality was 21.4% versus 11.8% (log-rank <i>p</i> = 0.03; HR 2.15, 95% CI 1.08–4.30). The augmented logistic model outperformed the traditional model (AUC 0.76 vs. 0.67; Brier 0.128 vs. 0.233; NRI + 11.3%; IDI + 5.6%). The random-forest model achieved AUC 0.81 (test 0.73) and ranked LVSWI as the strongest predictor. The mortality-predictive thresholds were 0.52&#xa0;W for CPO and 14.4&#xa0;g·min/m² for LVSWI. ML phenotyping based on Doppler-derived energetics identifies physiologically distinct heart failure phenotypes. Integrating CPO, LVSWI, and VAC into routine echocardiography may provide complementary physiology-based risk stratification beyond conventional echocardiographic assessment and may help guide personalized hemodynamic-targeted therapy.</p>

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Cardiac energetics and ventriculo-arterial interaction–based phenotyping in heart failure: a machine learning analysis

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Kapil Rajendran,
  • Aju Ajay,
  • Arun Jude Alphonse,
  • Arun Prathap,
  • Mayur Vasantrao Ahire,
  • Vinayakumar Desabandhu

摘要

Left-ventricular cardiac power output (CPO), stroke-work index (LVSWI), and ventriculo-arterial coupling (VAC) capture cardiac energetics and ventricular–vascular interaction, however their bedside prognostic value in acute decompensated heart failure (ADHF) remains uncertain. To determine whether machine-learning (ML) phenotyping based on these Doppler-derived indices refines risk stratification beyond conventional echocardiography. This prospective study enrolled 500 ADHF patients with LVEF < 40%. LVSWI, CPO, and VAC were calculated from admission echocardiography. Standardized values underwent K-means clustering, and phenotype outcomes were compared using Kaplan–Meier curves and Cox regression. A traditional logistic model (EF, TAPSE, LVEDP, RVSP, RAP) was contrasted with an augmented model adding the three indices. A random forest classifier using LVSWI, CPO, and VAC predicted 6-month mortality and was internally validated. Clustering yielded two phenotypes: low-output/uncoupled (n = 262) and preserved-output/coupled (n = 238). The low-output group had higher LVEDP, RVSP, RAP, and systemic vascular resistance (all p < 0.05) despite a similar EF. Six-month mortality was 21.4% versus 11.8% (log-rank p = 0.03; HR 2.15, 95% CI 1.08–4.30). The augmented logistic model outperformed the traditional model (AUC 0.76 vs. 0.67; Brier 0.128 vs. 0.233; NRI + 11.3%; IDI + 5.6%). The random-forest model achieved AUC 0.81 (test 0.73) and ranked LVSWI as the strongest predictor. The mortality-predictive thresholds were 0.52 W for CPO and 14.4 g·min/m² for LVSWI. ML phenotyping based on Doppler-derived energetics identifies physiologically distinct heart failure phenotypes. Integrating CPO, LVSWI, and VAC into routine echocardiography may provide complementary physiology-based risk stratification beyond conventional echocardiographic assessment and may help guide personalized hemodynamic-targeted therapy.