BMC anesthesiology · 2025
Development and validation of a nomogram for predicting perioperative transfusion in children undergoing cardiac surgery with CPB.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Wang Wenting, Wang He, Liu Jia, Jin Yu, Ji Bingyang, Liu Jinping
Аннотация
BACKGROUND: Timely recognition of perioperative red blood cell transfusion (PRT) risk is crucial for developing personalized blood management strategies in pediatric patients. In this study, we sought to construct a prediction model for PRT risk in pediatric patients undergoing cardiac surgery with cardiopulmonary bypass (CPB).
METHODS: From September 2014 to December 2021, 23,884 pediatric patients under the age of 14 were randomly divided into training and testing cohorts at a 7:3 ratio. Variable selection was performed using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then used to identify predictors, and a nomogram was developed to predict PRT risk. The model's performance was evaluated based on discrimination, calibration, and clinical utility in both cohorts.
RESULTS: After multiple rounds of variable selection, eight predictors of PRT risk were identified: age, weight, preoperative hemoglobin levels, presence of cyanotic congenital heart disease, CPB duration, minimum rectal temperature during CPB, CPB priming volume, and the use of a small incision. The predictive model incorporating these variables demonstrated strong performance, with an area under the curve (AUC) of 0.886 (95% CI: 0.880-0.891) in the training cohort and 0.883 (95% CI: 0.875-0.892) in the testing cohort. The calibration plot closely aligned with the ideal diagonal line, and decision curve analysis indicated that the model provided a net clinical benefit.
CONCLUSIONS: Our predictive model exhibits good performance in assessing PRT risk in pediatric patients undergoing cardiac surgery with CPB, providing clinicians a practical tool to optimize individualized perioperative blood management strategies for this vulnerable population.
METHODS: From September 2014 to December 2021, 23,884 pediatric patients under the age of 14 were randomly divided into training and testing cohorts at a 7:3 ratio. Variable selection was performed using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then used to identify predictors, and a nomogram was developed to predict PRT risk. The model's performance was evaluated based on discrimination, calibration, and clinical utility in both cohorts.
RESULTS: After multiple rounds of variable selection, eight predictors of PRT risk were identified: age, weight, preoperative hemoglobin levels, presence of cyanotic congenital heart disease, CPB duration, minimum rectal temperature during CPB, CPB priming volume, and the use of a small incision. The predictive model incorporating these variables demonstrated strong performance, with an area under the curve (AUC) of 0.886 (95% CI: 0.880-0.891) in the training cohort and 0.883 (95% CI: 0.875-0.892) in the testing cohort. The calibration plot closely aligned with the ideal diagonal line, and decision curve analysis indicated that the model provided a net clinical benefit.
CONCLUSIONS: Our predictive model exhibits good performance in assessing PRT risk in pediatric patients undergoing cardiac surgery with CPB, providing clinicians a practical tool to optimize individualized perioperative blood management strategies for this vulnerable population.