Key result
Forcecardiography strongly correlates with key echocardiographic metrics including GWI, LVOT VTI, and SV.
Why the study?
Identifying correlations between forcecardiography-derived features and echocardiographic or electrocardiographic parameters may enable non-invasive assessment of cardiac performance in patients with heart failure.
Does Forcecardiography (FCG) accurately predict echocardiographic parameters in HFrEF patients with CRT-D devices?
Observational (n=11)
Does Forcecardiography (FCG) accurately predict echocardiographic parameters in HFrEF patients with CRT-D devices?
Forcecardiography shows promise as a novel, non-invasive tool for estimating key echocardiographic metrics such as global work index, LVOT VTI, and stroke volume in heart failure patients.
Introduction Forcecardiography (FCG) is a non-invasive technique that measures chest wall vibrations generated by cardiac mechanical activity, providing valuable insights into cardiac function. By detecting mechanical cardiac events such as valve opening and closure, FCG can complement ECG data to estimate key hemodynamic parameters. Identifying correlations between FCG-derived features and echocardiographic or electrocardiographic parameters may enable non-invasive assessment of cardiac performance in patients with heart failure. Purpose This study aimed to evaluate the relationship between FCG-derived features and ECG/ECHO parameters in patients with heart failure and CRT-D devices, under different pacing modalities. Methods FCG and ECG signals were recorded in 11 HFrEF patients (ischemic and non-ischemic etiologies) with CRT-D devices during intrinsic rhythm, biventricular (BiV), and right ventricular (RV) pacing. FCG features (energy, skewness, kurtosis) were compared with ECG parameters (e.g., QRS duration) and echocardiographic indices including stroke volume (SV), global longitudinal strain (GLS), LVOT velocity-time integral (VTI), and global work index (GWI). Regression analyses were used to assess predictive correlations, evaluated with R² and RMSE (Root Mean Squared Error). Results Significant correlations were found between FCG-derived features and ECG/ECHO parameters (coefficients: -0.53 to 0.85). Regression models demonstrated strong predictive performance for GWI (R² = 0.93, RMSE = 174.44 mmHg%), LVOT VTI (R² = 0.90, RMSE = 3.51 cm), and SV (R² = 0.91, RMSE = 12.09 mL). Conclusions FCG shows promise as a non-invasive tool for estimating echocardiographic metrics such as GWI, LVOT VTI, and SV. Among extracted features, kurtosis exhibited strong predictive value across both intrinsic rhythm and pacing conditions. Larger studies are needed to confirm these preliminary findings and support broader clinical application.
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Salucci et al. (2026) conducted an observational in Heart failure with reduced ejection fraction (HFrEF) (n=11). Forcecardiography (FCG) was evaluated on Predictive correlations between FCG features and echocardiographic indices (GWI, LVOT VTI, SV) (R² 0.90 to 0.93). Forcecardiography-derived features demonstrated strong predictive performance for echocardiographic metrics including global work index (R²=0.93), LVOT VTI (R²=0.90), and stroke volume (R²=0.91).
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