Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation

Area of Science:

• Cardiovascular Surgery
• Pediatric Cardiology
• Medical Informatics

Background:

• High rates of postoperative adverse events in coarctation of aorta (CoA) patients necessitate improved outcome prediction.
• Current strategies lack a universally accepted method for individualizing risk assessment in pediatric CoA surgical candidates.

Purpose of the Study:

• To develop and validate a novel risk prediction model for postoperative adverse events in pediatric patients undergoing surgery for coarctation of aorta.
• To compare the performance of the new model against existing risk stratification tools.

Main Methods:

• A cohort of 514 pediatric patients with CoA from two centers was analyzed.
• A multivariable logistic regression model was developed using the least absolute shrinkage and selection operator (LASSO) for variable selection.
• Model performance was evaluated using ROC curves, Hosmer-Lemeshow tests, calibration plots, and compared with Aristotle Basic Complexity (ABC) and Risk Adjustment for Congenital Heart Surgery (RACHS-1) scores using NRI and IDI.

Main Results:

• Postoperative adverse events were observed in 37.9% of patients.
• Nine key predictors were identified: thoracotomy incision, preoperative ventilation, VSD, cardiac dysfunction, pulmonary hypertension, height, BMI z-score, LVEF, and LVPW thickness.
• The developed model demonstrated strong performance (AUC = 0.8195) and significantly outperformed ABC and RACHS-1 scores in predicting adverse events.

Conclusions:

• An easily applicable postoperative risk model for pediatric CoA patients was successfully developed and validated using readily available clinical data.
• This new model offers superior predictive accuracy compared to the established ABC and RACHS-1 scoring systems, enhancing clinical decision-making.