Journal of cardiothoracic and vascular anesthesia · 2026
Levosimendan in Patients With Low Cardiac Ejection Fraction Undergoing Coronary Artery Bypass Grafting: Machine Learning Double Robust Causal Effect Analysis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bis Jarosław, Mendrala Konrad, Deja Marek, Darocha Tomasz, Morkisz Łukasz, Jodłowski Mikołaj, Gocoł Radosław
Аннотация
OBJECTIVES: The effectiveness of levosimendan in patients with severely impaired left ventricular function undergoing coronary artery bypass grafting (CABG) remains debated. While some early publications suggested a survival benefit, recent studies show conflicting results. This study used causal analysis to determine if preoperative levosimendan improves 30-day survival in this selected population.
DESIGN: Retrospective cohort study with machine learning double-robust causal effect analysis.
SETTING: Institutional cardiac surgery database.
PARTICIPANTS: A total of 973 patients with reduced left ventricular ejection fraction (LVEF ≤30%) undergoing CABG with or without concomitant valvular surgery.
INTERVENTIONS: Preoperative levosimendan administration versus no levosimendan.
MEASUREMENTS AND MAIN RESULTS: The primary outcome was 30-day all-cause mortality. Mortality prediction was modeled using extreme gradient boosting with Shapley additive explanations to quantify predictor contributions. Levosimendan contributed minimally to mortality prediction; age, renal function, and surgical complexity were the dominant predictors. The estimated average treatment effect of levosimendan on 30-day mortality was negligible (0.12 percentage points; 95% confidence interval [CI], -0.84 to 1.56). Null effects were consistent across double-robust targeted maximum likelihood estimation (difference, 1.08 percentage points; 95% CI, -6.66 to 10.71) and overlap weighting (0.12 percentage points; 95% CI, -0.80 to 1.58).
CONCLUSIONS: In this study, levosimendan does not significantly impact 30-day mortality in patients undergoing CABG with severely reduced LVEF (≤30%). Machine learning double-robust causal inference demonstrated a lack of significant therapeutic effect in this population.
DESIGN: Retrospective cohort study with machine learning double-robust causal effect analysis.
SETTING: Institutional cardiac surgery database.
PARTICIPANTS: A total of 973 patients with reduced left ventricular ejection fraction (LVEF ≤30%) undergoing CABG with or without concomitant valvular surgery.
INTERVENTIONS: Preoperative levosimendan administration versus no levosimendan.
MEASUREMENTS AND MAIN RESULTS: The primary outcome was 30-day all-cause mortality. Mortality prediction was modeled using extreme gradient boosting with Shapley additive explanations to quantify predictor contributions. Levosimendan contributed minimally to mortality prediction; age, renal function, and surgical complexity were the dominant predictors. The estimated average treatment effect of levosimendan on 30-day mortality was negligible (0.12 percentage points; 95% confidence interval [CI], -0.84 to 1.56). Null effects were consistent across double-robust targeted maximum likelihood estimation (difference, 1.08 percentage points; 95% CI, -6.66 to 10.71) and overlap weighting (0.12 percentage points; 95% CI, -0.80 to 1.58).
CONCLUSIONS: In this study, levosimendan does not significantly impact 30-day mortality in patients undergoing CABG with severely reduced LVEF (≤30%). Machine learning double-robust causal inference demonstrated a lack of significant therapeutic effect in this population.