Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia.

Abstract

Background: Myocardial injury is common during liver transplantation and is associated with poor outcomes. The development of a reliable prediction system for this type of injury is crucial for reducing the incidence of cardiac complications in children receiving living donor liver transplantation (LDLT). However, establishing a practical myocardial injury prediction system for children with biliary atresia remains a considerable challenge.

Aim: To create and validate a nomogram model for predicting myocardial injury in children with biliary atresia who received LDLT.

Methods: Clinical data from pediatric patients who received LDLT for biliary atresia between November, 2019 and January, 2022 were retrospectively analyzed. The complete dataset was randomly partitioned into a training set and a validation set at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used to preliminarily screen out the predictors of myocardial injury. The prediction model was established via multivariable logistic regression and presented in the form of a nomogram.

Results: This study included 321 patients, 150 (46.7%) of whom had myocardial injury. The participants were randomly allocated into two groups: A training group consisting of 225 patients and a validation group comprising 96 patients. The predictors in this nomogram included the preoperative neutrophil-to-lymphocyte ratio, high sensitivity C-reactive protein level, pediatric end-stage liver disease score and postreperfusion syndrome. The area under the curve for predicting myocardial injury was 0.865 in the training set and 0.856 in the validation set. The calibration curve revealed that the predicted values were very close to the actual values in the two sets. Decision curve analysis revealed that the prediction model offered a favorable net benefit.

Conclusion: The nomogram developed in this study effectively predicts myocardial injury in pediatric LDLT patients, showing good accuracy and potential for clinical application.