Machine learning algorithms combining slope deceleration and fetal heart rate features to predict acidemia

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

Resumen: Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections.

Idioma: Inglés

DOI: 10.3390/app13137478

Año: 2023

Publicado en: Applied Sciences (Switzerland) 13 (2023), [22 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.5 (2023)

Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 44 / 181 = 0.243 (2023) - Q1 - T1
Categ. JCR: PHYSICS, APPLIED rank: 87 / 179 = 0.486 (2023) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 115 / 231 = 0.498 (2023) - Q2 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 258 / 439 = 0.588 (2023) - Q3 - T2

Factor impacto CITESCORE: 5.3 - Engineering (all) (Q1) - Computer Science Applications (Q2) - Materials Science (all) (Q2) - Fluid Flow and Transfer Processes (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3)

Factor impacto SCIMAGO: 0.508 - Engineering (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2) - Materials Science (miscellaneous) (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3) - Computer Science Applications (Q3)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T69-23D
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Obstetricia y Ginecología (Dpto. Cirugía)