High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data.
Machine learning
Pediatric surgical risk
Personalized care
Prediction model
Journal
American journal of surgery
ISSN: 1879-1883
Titre abrégé: Am J Surg
Pays: United States
ID NLM: 0370473
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
received:
23
08
2022
revised:
09
02
2023
accepted:
10
03
2023
medline:
21
6
2023
pubmed:
23
3
2023
entrez:
22
3
2023
Statut:
ppublish
Résumé
New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP). All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation. 431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications). We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.
Sections du résumé
BACKGROUNDS
New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP).
METHODS
All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation.
RESULTS
431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).
CONCLUSIONS
We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.
Identifiants
pubmed: 36948897
pii: S0002-9610(23)00106-X
doi: 10.1016/j.amjsurg.2023.03.009
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
115-121Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest No coauthors have any conflicts of interest to disclose.