Abstract
No machine learning (ML) models for predicting delivery mode after labor induction (IOL) have been externally validated. We aimed to develop and validate one using medical records. Portuguese tertiary center data (n = 2434) were used for development and internal validation, and Consortium on Safe Labor data (n = 10,591) for external validation. Outcomes are vaginal delivery (VD) or cesarean section (CS). Internal validation employed different ML approaches, aiming for model simplification. Logistic regression performed best on internal validation: AUROC:0.793; F1-score:0.748; PPV:0.752, with good calibration and decision curve analysis (DCA), being selected for simplification. Simplified top-13 features model was selected for external validation: AUROC:0.808; F1-score:0.781; PPV:0.822, tending for VD (99.6%) while avoiding false-positives (0.5%). Calibration curves underestimated CS risk by 10–75%; DCA showed good net benefit. The model’s good AUROC and DCA suggest clinical utility. Calibration curve underestimation of CS risk may result from outcome imbalance between datasets.
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Acknowledgements
The data included in this paper were obtained from the Consortium on Safe Labor, supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, through Contract No. HHSN267200603425C. Institutions involved in the Consortium include, in alphabetical order: Baystate Medical Center, Springfield, MA; Cedars-Sinai Medical Center Burnes Allen Research Center, Los Angeles, CA; Christiana Care Health System, Newark, DE; Georgetown University Hospital, MedStar Health, Washington, DC; Indiana University Clarian Health, Indianapolis, IN; Intermountain Healthcare and the University of Utah, Salt Lake City, Utah; Maimonides Medical Center, Brooklyn, NY; MetroHealth Medical Center, Cleveland, OH.; Summa Health System, Akron City Hospital, Akron, OH; The EMMES Corporation, Rockville MD (Data Coordinating Center); University of Illinois at Chicago, Chicago, IL; University of Miami, Miami, FL; and University of Texas Health Science Center at Houston, Houston, Texas. The named authors alone are responsible for the views expressed in this manuscript, which does not necessarily represent the decisions or the stated policy of the NICHD.We acknowledge NICHD DASH for providing the Consortium on Safe Labor data that was used for this research.This work has received funding from the Research Grant from the Coimbra Hospital and University Centre.
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Ferreira, I., Simões, J., Correia, J. et al. External validation of a machine learning model for delivery mode prediction after induction. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02384-0
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DOI: https://doi.org/10.1038/s41746-026-02384-0
