European heart journal. Cardiovascular Imaging · 2026
Reappraising cardiac function with myocardial contraction fraction: normal values, disease detection, and prognostication.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Kurdi Hibba, Thornton George, Shiwani Hunain, Artico Jessica, Abiodun Aderonke, Castelletti Silvia, Rosmini Stefania, Nordin Sabrina, Augusto Joao, Kozor Rebecca, Maestrini Viviana, Al Saikhan Lamia, Gul Uzma, Joy George, Hughes Rebecca, Bhuva Anish, Meredith Benjamin, Captur Gabriella, Fontanna Marianna, Hughes Derralynn, Kellman Peter, Hughes Alun D, Schelbert Erik, Manisty Charlotte H, Treibel Thomas A, Moon James C, Davies Rhodri H
Аннотация
AIMS: Assessing cardiac function is critical for managing cardiovascular disease, guiding treatment, monitoring progression, and risk stratification. While left ventricular (LV) ejection fraction (LVEF) is firmly established, it has limitations. Myocardial contraction fraction (MCF)-the ratio of stroke volume to myocardial volume, is simple to compute without additional analysis and offers a promising alternative to LVEF.
METHODS AND RESULTS: MCF was assessed across four datasets spanning healthy controls and chronic structural cardiac disease, with direct comparison to LVEF. Association between age, sex, and MCF were investigated in 3541 healthy subjects from the UK Biobank and sex-specific reference ranges derived. Several cohorts were recruited to investigate the discriminative power of MCF and LVEF between health and physiological adaption (n = 278 veteran athletes), pathological hypertrophy [hypertrophic cardiomyopathy, amyloid, Fabry, severe aortic stenosis (AS), and hypertension (HTN); n = 633], and dilatation [n = 103 dilated cardiomyopathy (DCM)]. Ability to track disease severity was assessed by looking at 41 558 subjects from the UK Biobank. Finally, prognostication was assessed on 1277 consecutive patients from an independent external dataset. All images were analysed using the same validated artificial intelligence algorithm. MCF varied with sex (mean MCF: 0.94 male; 1.1 female) but not age. Sex-specific reference ranges were established: [0.68-1.20] for male and [0.82-1.38] for female. MCF decreased in pathological disease (e.g. mean MCF: 0.72 HCM; 0.69 severe AS; 0.5 amyloid; 0.9 HTN) but there was no significant decrease in LVEF other than in amyloid (mean EF: 76% HCM; 64% severe AS; amyloid 56%; 65% HTN). Both MCF and ejection fraction (EF) decreased in DCM (EF 34%; MCF 0.58). MCF decreased with worsening HTN, whereas LVEF increased (P < 0.05). MCF had superior prognostic ability to LVEF (MCF vs. LVEF: HR = 0.772 vs. HR = 0.816; χ2 = 198 vs. χ2 = 151; P < 0.001).
CONCLUSION: We established MCF reference ranges, showing superior performance for detecting early disease and tracking progression compared with LVEF. MCF offers enhanced prognostic utility, complementing established metrics of LV function.
METHODS AND RESULTS: MCF was assessed across four datasets spanning healthy controls and chronic structural cardiac disease, with direct comparison to LVEF. Association between age, sex, and MCF were investigated in 3541 healthy subjects from the UK Biobank and sex-specific reference ranges derived. Several cohorts were recruited to investigate the discriminative power of MCF and LVEF between health and physiological adaption (n = 278 veteran athletes), pathological hypertrophy [hypertrophic cardiomyopathy, amyloid, Fabry, severe aortic stenosis (AS), and hypertension (HTN); n = 633], and dilatation [n = 103 dilated cardiomyopathy (DCM)]. Ability to track disease severity was assessed by looking at 41 558 subjects from the UK Biobank. Finally, prognostication was assessed on 1277 consecutive patients from an independent external dataset. All images were analysed using the same validated artificial intelligence algorithm. MCF varied with sex (mean MCF: 0.94 male; 1.1 female) but not age. Sex-specific reference ranges were established: [0.68-1.20] for male and [0.82-1.38] for female. MCF decreased in pathological disease (e.g. mean MCF: 0.72 HCM; 0.69 severe AS; 0.5 amyloid; 0.9 HTN) but there was no significant decrease in LVEF other than in amyloid (mean EF: 76% HCM; 64% severe AS; amyloid 56%; 65% HTN). Both MCF and ejection fraction (EF) decreased in DCM (EF 34%; MCF 0.58). MCF decreased with worsening HTN, whereas LVEF increased (P < 0.05). MCF had superior prognostic ability to LVEF (MCF vs. LVEF: HR = 0.772 vs. HR = 0.816; χ2 = 198 vs. χ2 = 151; P < 0.001).
CONCLUSION: We established MCF reference ranges, showing superior performance for detecting early disease and tracking progression compared with LVEF. MCF offers enhanced prognostic utility, complementing established metrics of LV function.