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Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
Yan Gu1,2,3,4,5,6, Qianqian Li7, Rui Lin1,2,3,4
1Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
A new risk model accurately predicts postoperative adverse events in pediatric coarctation of aorta (CoA) patients. This model, using clinical data, outperforms existing strategies like ABC score and RACHS-1 for better surgical risk assessment.
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.
Abstract:
Background: Postoperative adverse events remain excessively high in surgical patients with coarctation of aorta (CoA). Currently, there is no generally accepted strategy to predict these patients' individual outcomes. Objective: This study aimed to develop a risk model for the prediction of postoperative risk in pediatric patients with CoA. Methods: In total, 514 patients with CoA at two centers were enrolled. Using daily clinical practice data, we developed a model to predict 30-day or in-hospital adverse events after the operation. The least absolute shrinkage and selection operator approach was applied to select predictor variables and logistic regression was used to develop the model. Model performance was estimated using the receiver-operating characteristic curve, the Hosmer-Lemeshow test and the calibration plot. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) compared with existing risk strategies were assessed. Results: Postoperative adverse events occurred in 195 (37.9%) patients in the overall population. Nine predictive variables were identified, including incision of left thoracotomy, preoperative ventilation, concomitant ventricular septal defect, preoperative cardiac dysfunction, severe pulmonary hypertension, height, weight-for-age z-score, left ventricular ejection fraction and left ventricular posterior wall thickness. A multivariable logistic model [area under the curve = 0.8195 (95% CI: 0.7514-0.8876)] with adequate calibration was developed. Model performance was significantly improved compared with the existing Aristotle Basic Complexity (ABC) score (NRI = 47.3%, IDI = 11.5%) and the Risk Adjustment for Congenital Heart Surgery (RACHS-1) (NRI = 75.0%, IDI = 14.9%) in the validation set. Conclusion: Using daily clinical variables, we generated and validated an easy-to-apply postoperative risk model for patients with CoA. This model exhibited a remarkable improvement over the ABC score and the RACHS-1 method.
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