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Prediction model for obstetric anal sphincter injury using machine learning

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Abstract

Introduction and hypothesis

Obstetric anal sphincter injury (OASI) is a complication with substantial maternal morbidity. The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for OASI, based on maternal and fetal variables collected at admission to labor.

Materials and methods

We performed a retrospective cohort study at a tertiary university hospital. Included were term deliveries (live, singleton, vertex). A comparison was made between women diagnosed with OASI and those without such injury. For formation of a machine learning-based model, a gradient boosting machine learning algorithm was implemented. Evaluation of the performance model was achieved using the area under the receiver-operating characteristic curve (AUC).

Results

Our cohort comprised 98,463 deliveries, of which 323 (0.3%) were diagnosed with OASI. Applying a machine learning model to data recorded during admission to labor allowed for individualized risk assessment with an AUC of 0.756 (95% CI 0.732–0.780). According to this model, a lower number of previous births, fewer pregnancies, decreased maternal weight and advanced gestational week elevated the risk for OASI. With regard to parity, women with one previous delivery had approximately 1/3 of the risk for OASI compared to nulliparous women (OR = 0.3 (0.23–0.39), p < 0.001), and women with two previous deliveries had 1/3 of the risk compared to women with one previous delivery (OR = 0.35 (0.21–0.60), p < 0.001).

Conclusion

Our machine learning-based model stratified births to high or low risk for OASI, making it an applicable tool for personalized decision-making upon admission to labor.

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Author notes

  1. Henry Hillel Chill and Joshua Guedalia contributed equally to this work.

Authors and Affiliations

  1. Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Obstetrics and Gynecology, Faculty of Medicine, Hadassah-Hebrew University Medical Center, PO Box 12000, Jerusalem, Ein Kerem, Israel

    Henry Hillel Chill & David Shveiky

  2. Department of Obstetrics and Gynecology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

    Henry Hillel Chill, Michal Lipschuetz, Tzvika Shimonovitz, David Shveiky & Gilad Karavani

  3. The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel

    Joshua Guedalia, Michal Lipschuetz & Ron Unger

Authors
  1. Henry Hillel Chill
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  2. Joshua Guedalia
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  3. Michal Lipschuetz
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  4. Tzvika Shimonovitz
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  5. Ron Unger
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  6. David Shveiky
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  7. Gilad Karavani
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Contributions

Chill: Project development, data collection, data analysis, manuscript writing and editing.

Guedalia: Project development, data collection, data analysis, manuscript writing and editing.

Lipschuetz: Data collection, data analysis, manuscript editing.

Shimonovitz: Data analysis, manuscript editing.

Unger: Data analysis, manuscript editing.

Shveiky: Manuscript editing, data analysis.

Karavani: Project development, data collection, data analysis, manuscript writing and editing.

Corresponding author

Correspondence to Henry Hillel Chill.

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Chill, H.H., Guedalia, J., Lipschuetz, M. et al. Prediction model for obstetric anal sphincter injury using machine learning. Int Urogynecol J 32, 2393–2399 (2021). https://doi.org/10.1007/s00192-021-04752-8

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  • DOI: https://doi.org/10.1007/s00192-021-04752-8

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