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Ultrasound Obstet Gynecol. 2019 Oct 6. doi: 10.1002/uog.21878. [Epub ahead of print]

Development and validation of a machine learning model for prediction of shoulder dystocia.

Author information

1
Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Stanford University School of Medicine, Stanford, CA, USA.
2
Department of Obstetrics and Gynecology, The Sheba Medical Center, Tel Hashomer, Israel.
3
Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, University of California, San Francisco, San Francisco, CA, USA.
4
Department of Obstetrics and Gynecology, The Kaplan Medical Center, Rehovot, Israel.
5
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, 94158, USA.
6
Department of Pediatrics, Division of Neonatal and developmental medicine, Stanford University School of Medicine, Stanford, CA, USA.

Abstract

OBJECTIVE:

We sought to develop a machine learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model accuracy and potential clinical efficacy in optimizing the use of cesarean delivery (CD) in the context of suspected macrosomia.

STUDY DESIGN:

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 accuracy and clinical efficacy (validation cohort). Subsequent to inclusion and exclusion criteria, the derivation cohort consisted of 686 deliveries [131 complicated by ShD], and the validation cohort of 2,584 deliveries [31 complicated by ShD]. For each of these deliveries, we collected maternal and neonatal delivery outcomes coupled with maternal demographics, obstetric clinical data and sonographic biometric measurements of the fetus. Biometric measurements and their derived estimated fetal weight were adjusted (aEFW) to the date of the delivery. A ML pipeline was utilized to develop the model.

RESULTS:

In the derivation cohort, the ML model provided significantly better prediction than the current paradigm: using nested cross validation the area under the receiver operator characteristics curve (AUC) of the model was 0.793 ± 0.041, outperforming aEFW and diabetes (0.745 ± 0.044, p-value = 1e-16). The following risk modifiers had a positive beta > 0.02 increasing the risk of ShD: aEFW (0.164), pregestational diabetes (0.047), prior ShD (0.04), female fetal sex (0.04) and adjusted abdominal circumference (0.03). The following risk modifiers had a negative beta < -0.02 protective of ShD: adjusted biparietal diameter (-0.08) and maternal height (-0.03). In the validation cohort the model outperformed aEFW and diabetes (AUC = 0.866 vs. 0.784, p-value = 0.00007). Additionally, in the validation cohort, among the subgroup of 273 women carrying a fetus with aEFW above 4,000 g, the aEFW had no predictive power (AUC = 0.548), and the model performed significantly better (0.775, p-value = 0.0002). A risk-score threshold of 0.5 stratified 42.9% of deliveries to the high-risk group that included 90.9% of ShD cases and all cases accompanied by maternal or newborn complications. A more specific threshold of 0.7 stratified only 27.5% of the deliveries to the high-risk groups that included 72.7% of ShD cases, and all those accompanied by newborn complications.

CONCLUSION:

We developed a ML model for prediction of ShD. We externally validated the model performance in a different cohort. The model predicted ShD better than EFW+ maternal diabetes and was able to stratify the risk of ShD and neonatal injury in the context of suspected macrosomia. This article is protected by copyright. All rights reserved.

KEYWORDS:

EHR; anthropometry; artificial intelligence; biometry; macrosomia

PMID:
31587401
DOI:
10.1002/uog.21878

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