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

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 pos...

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Autores principales: Gu, Yan, Li, Qianqian, Lin, Rui, Jiang, Wenxi, Wang, Xue, Zhou, Gengxu, Su, Junwu, Fan, Xiangming, Gao, Pei, Jin, Mei, Wang, Yuan, Du, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175771/
https://www.ncbi.nlm.nih.gov/pubmed/34095260
http://dx.doi.org/10.3389/fcvm.2021.672627
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author Gu, Yan
Li, Qianqian
Lin, Rui
Jiang, Wenxi
Wang, Xue
Zhou, Gengxu
Su, Junwu
Fan, Xiangming
Gao, Pei
Jin, Mei
Wang, Yuan
Du, Jie
author_facet Gu, Yan
Li, Qianqian
Lin, Rui
Jiang, Wenxi
Wang, Xue
Zhou, Gengxu
Su, Junwu
Fan, Xiangming
Gao, Pei
Jin, Mei
Wang, Yuan
Du, Jie
author_sort Gu, Yan
collection PubMed
description 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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spelling pubmed-81757712021-06-05 Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation Gu, Yan Li, Qianqian Lin, Rui Jiang, Wenxi Wang, Xue Zhou, Gengxu Su, Junwu Fan, Xiangming Gao, Pei Jin, Mei Wang, Yuan Du, Jie Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2021-05-21 /pmc/articles/PMC8175771/ /pubmed/34095260 http://dx.doi.org/10.3389/fcvm.2021.672627 Text en Copyright © 2021 Gu, Li, Lin, Jiang, Wang, Zhou, Su, Fan, Gao, Jin, Wang and Du. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Gu, Yan
Li, Qianqian
Lin, Rui
Jiang, Wenxi
Wang, Xue
Zhou, Gengxu
Su, Junwu
Fan, Xiangming
Gao, Pei
Jin, Mei
Wang, Yuan
Du, Jie
Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title_full Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title_fullStr Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title_full_unstemmed Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title_short Prognostic Model to Predict Postoperative Adverse Events in Pediatric Patients With Aortic Coarctation
title_sort prognostic model to predict postoperative adverse events in pediatric patients with aortic coarctation
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175771/
https://www.ncbi.nlm.nih.gov/pubmed/34095260
http://dx.doi.org/10.3389/fcvm.2021.672627
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