Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?. (June 2017)

• Record Type: Journal Article
• Authors: Alberola-Rubio, Jose; Garcia-Casado, Javier; Prats-Boluda, Gema; Ye-Lin, Yiyao; Desantes, Domingo; Valero, Javier; Perales, Alfredo
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 127
• Page End: 133
• Publication Date: 2017-06
• Subjects: Electrohysterogram -- SVM -- Majority voting -- Labor management; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Machine learning methods for medical labor induction is proposed. A total of 33 predictive models using obstetrical data and electrophysiological parameters were developed and evaluated. Electrophysiological parameters must be individually contextualized with patient obstetrical data (maternal clinical variables). Abstract: Background and objective: Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG). Methods: EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (≤41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared. Results: The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC = 0.65) and GA at recording time (AUC = 0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC = 0.76. Multiple input SVM obtained AUC = 0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC = 0.93. Conclusions: Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique.