Development and validation of a machine-learning model for prediction of shoulder dystocia.
Adult
Biometry
/ methods
Cesarean Section
Diabetes, Gestational
Female
Fetal Macrosomia
/ diagnosis
Fetal Weight
Gestational Age
Humans
Israel
Machine Learning
/ standards
Patient Selection
Predictive Value of Tests
Pregnancy
ROC Curve
Reproducibility of Results
Risk Factors
Shoulder Dystocia
/ diagnosis
Ultrasonography, Prenatal
/ statistics & numerical data
EHR
anthropometry
artificial intelligence
biometry
macrosomia
Journal
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
ISSN: 1469-0705
Titre abrégé: Ultrasound Obstet Gynecol
Pays: England
ID NLM: 9108340
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
10
04
2019
revised:
04
09
2019
accepted:
16
09
2019
pubmed:
7
10
2019
medline:
15
12
2021
entrez:
7
10
2019
Statut:
ppublish
Résumé
To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of suspected macrosomia. We used electronic health records (EHR) from the Sheba Medical Center in Israel to develop the model (derivation cohort) and EHR from the University of California San Francisco Medical Center to validate the model's accuracy and clinical efficacy (validation cohort). Subsequent to application of inclusion and exclusion criteria, the derivation cohort included 686 singleton vaginal deliveries, of which 131 were complicated by ShD, and the validation cohort included 2584 deliveries, of which 31 were complicated by ShD. For each of these deliveries, we collected maternal and neonatal delivery outcomes coupled with maternal demographics, obstetric clinical data and sonographic fetal biometry. Biometric measurements and their derived estimated fetal weight were adjusted (aEFW) according to gestational age at delivery. A ML pipeline was utilized to develop the model. In the derivation cohort, the ML model provided significantly better prediction than did the current clinical paradigm based on fetal weight and maternal diabetes: using nested cross-validation, the area under the receiver-operating-characteristics curve (AUC) of the model was 0.793 ± 0.041, outperforming aEFW combined with diabetes (AUC = 0.745 ± 0.044, P = 1e We developed a ML model for prediction of ShD and, in a different cohort, externally validated its performance. The model predicted ShD better than did estimated fetal weight either alone or combined with maternal diabetes, and was able to stratify the risk of ShD and neonatal injury in the context of suspected macrosomia. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
588-596Informations de copyright
Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.
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