000126548 001__ 126548
000126548 005__ 20241125101147.0
000126548 0247_ $$2doi$$a10.3390/app13137478
000126548 0248_ $$2sideral$$a133973
000126548 037__ $$aART-2023-133973
000126548 041__ $$aeng
000126548 100__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis Mariano
000126548 245__ $$aMachine learning algorithms combining slope deceleration and fetal heart rate features to predict acidemia
000126548 260__ $$c2023
000126548 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126548 5203_ $$aElectronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections.
000126548 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T69-23D$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000126548 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126548 590__ $$a2.5$$b2023
000126548 592__ $$a0.508$$b2023
000126548 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b44 / 181 = 0.243$$c2023$$dQ1$$eT1
000126548 591__ $$aPHYSICS, APPLIED$$b87 / 179 = 0.486$$c2023$$dQ2$$eT2
000126548 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b115 / 231 = 0.498$$c2023$$dQ2$$eT2
000126548 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b258 / 439 = 0.588$$c2023$$dQ3$$eT2
000126548 593__ $$aEngineering (miscellaneous)$$c2023$$dQ2
000126548 593__ $$aFluid Flow and Transfer Processes$$c2023$$dQ2
000126548 593__ $$aMaterials Science (miscellaneous)$$c2023$$dQ2
000126548 593__ $$aInstrumentation$$c2023$$dQ2
000126548 593__ $$aProcess Chemistry and Technology$$c2023$$dQ3
000126548 593__ $$aComputer Science Applications$$c2023$$dQ3
000126548 594__ $$a5.3$$b2023
000126548 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126548 700__ $$aCastán, Berta
000126548 700__ $$0(orcid)0000-0001-7995-6969$$aEsteban-Escaño, Javier
000126548 700__ $$0(orcid)0009-0001-6297-2767$$aSanz-Enguita, Gerardo
000126548 700__ $$0(orcid)0000-0002-9496-9714$$aLaliena, Antonio R.
000126548 700__ $$0(orcid)0000-0001-9058-1037$$aLou-Mercadé, Ana Cristina$$uUniversidad de Zaragoza
000126548 700__ $$aChóliz-Ezquerro, Marta
000126548 700__ $$0(orcid)0000-0002-9048-121X$$aCastán, Sergio$$uUniversidad de Zaragoza
000126548 700__ $$aSavirón-Cornudella, Ricardo
000126548 7102_ $$11013$$2645$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Obstetricia y Ginecología
000126548 773__ $$g13 (2023), [22 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000126548 8564_ $$s10181444$$uhttps://zaguan.unizar.es/record/126548/files/texto_completo.pdf$$yVersión publicada
000126548 8564_ $$s2686789$$uhttps://zaguan.unizar.es/record/126548/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126548 909CO $$ooai:zaguan.unizar.es:126548$$particulos$$pdriver
000126548 951__ $$a2024-11-22-12:04:48
000126548 980__ $$aARTICLE