International urogynecology journal · 2021
Prediction model for obstetric anal sphincter injury using machine learning.
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Chill Henry Hillel, Guedalia Joshua, Lipschuetz Michal, Shimonovitz Tzvika, Unger Ron, Shveiky David, Karavani Gilad
Аннотация
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.
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.