Journal of cardiothoracic and vascular anesthesia · 2025
Nomograms Based on Myocardial Strain and Myocardial Work to Predict Low Cardiac Output Syndrome in Children Undergoing Surgery for Congenital Heart Disease.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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.
Qin Xiaoxue, Dan Yingzhi, Zhang Kan, Tong Chaoyang, Wang Hong, Sun Liping, Mamtili Ilyar, Huang Yue, Zheng Jijian
Аннотация
OBJECTIVES: To develop a nomogram model based on myocardial strain and myocardial work to predict the risk of low cardiac output syndrome (LCOS) in children undergoing congenital heart disease (CHD) surgery under cardiopulmonary bypass (CPB).
DESIGN: A retrospective observational study.
SETTING: At a tertiary care center.
PARTICIPANTS: One hundred fifty-five pediatric patients who underwent CHD surgery.
INTERVENTIONS: We retrospectively collected data from pediatric patients who underwent elective CHD surgery under CPB between January 2023 and December 2024. These patients underwent comprehensive transthoracic echocardiography and quantification of left ventricular myocardial work.
MEASUREMENTS AND MAIN RESULTS: A total of 155 pediatric patients were included, among whom 31 (20.0%) developed LCOS within 24 hours postoperatively. Least absolute shrinkage and selection operator regression identified four key predictors: aortic cross-clamp time, global longitudinal strain (GLS), global work index (GWI), and global work efficiency (GWE). Multivariate logistic regression showed that aortic cross-clamp time, GLS, GWI, and GWE were independent predictors of LCOS. A nomogram was constructed based on the regression model, with a total score range of 51 to 199, corresponding to a predicted LCOS probability ranging from 0.1% to 90%. The nomogram demonstrated excellent discriminative ability (area under the curve = 0.872, 95% CI: 0.793-0.951, p < 0.001), with a sensitivity of 74.2% and specificity of 88.7%. The calibration curve showed good agreement between predicted and observed outcomes (mean absolute error = 0.021). Decision curve analysis indicated that the model provided clinical net benefit across a range of decision thresholds.
CONCLUSION: We developed a nomogram model incorporating GLS, GWI, and GWE to predict postoperative LCOS in children undergoing CHD surgery. The model demonstrated favorable discrimination, calibration, and clinical applicability, offering a valuable tool for perioperative risk assessment and management.
DESIGN: A retrospective observational study.
SETTING: At a tertiary care center.
PARTICIPANTS: One hundred fifty-five pediatric patients who underwent CHD surgery.
INTERVENTIONS: We retrospectively collected data from pediatric patients who underwent elective CHD surgery under CPB between January 2023 and December 2024. These patients underwent comprehensive transthoracic echocardiography and quantification of left ventricular myocardial work.
MEASUREMENTS AND MAIN RESULTS: A total of 155 pediatric patients were included, among whom 31 (20.0%) developed LCOS within 24 hours postoperatively. Least absolute shrinkage and selection operator regression identified four key predictors: aortic cross-clamp time, global longitudinal strain (GLS), global work index (GWI), and global work efficiency (GWE). Multivariate logistic regression showed that aortic cross-clamp time, GLS, GWI, and GWE were independent predictors of LCOS. A nomogram was constructed based on the regression model, with a total score range of 51 to 199, corresponding to a predicted LCOS probability ranging from 0.1% to 90%. The nomogram demonstrated excellent discriminative ability (area under the curve = 0.872, 95% CI: 0.793-0.951, p < 0.001), with a sensitivity of 74.2% and specificity of 88.7%. The calibration curve showed good agreement between predicted and observed outcomes (mean absolute error = 0.021). Decision curve analysis indicated that the model provided clinical net benefit across a range of decision thresholds.
CONCLUSION: We developed a nomogram model incorporating GLS, GWI, and GWE to predict postoperative LCOS in children undergoing CHD surgery. The model demonstrated favorable discrimination, calibration, and clinical applicability, offering a valuable tool for perioperative risk assessment and management.