Machine learning algorithm to predict acidemia using electronic fetal monitoring recording parameters

Esteban-Escaño, Javier ; Castán, Berta ; Castán, Sergio (Universidad de Zaragoza) ; Chóliz-Ezquerro, Marta ; Asensio, César ; Laliena, Antonio R. ; Sanz-Enguita, Gerardo ; Sanz, Gerardo (Universidad de Zaragoza) ; Esteban, Luis Mariano ; Savirón, Ricardo

Resumen: Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

Idioma: Inglés

DOI: 10.3390/e24010068

Año: 2022

Publicado en: ENTROPY 24, 1 (2022), 68 [16 pp.]
ISSN: 1099-4300

Factor impacto JCR: 2.7 (2022)

Categ. JCR: PHYSICS, MULTIDISCIPLINARY rank: 40 / 85 = 0.471 (2022) - Q2 - T2

Factor impacto CITESCORE: 4.7 - Physics and Astronomy (Q2)

Factor impacto SCIMAGO: 0.541 - Electrical and Electronic Engineering (Q2) - Physics and Astronomy (miscellaneous) (Q2) - Mathematical Physics (Q2) - Information Systems (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Obstetricia y Ginecología (Dpto. Cirugía)