Development and validation of a nomogram for predicting perioperative transfusion in children undergoing cardiac surgery with CPB.

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.