(297) Prediction of Shoulder Dystocia Utilizing Machine Learning

Objective: To determine whether a machine learning model can predict patients at risk for shoulder dystocia (SD) better than estimations of birthweight (BW) alone.

Study Design: This was a retrospective analysis of 17,731 pregnant individuals from 7/2013 to 10/2018. Utilizing a machine learning model, a total of 122 binary and continuous variables were included. Baseline models were built with different sets of variables during 3 time periods: 57 in antepartum period, 96 in stage 1 and 119 in stage 2 of labor. BW was used as a proxy for estimated fetal weight (EFW) because documented assessment of EFW was not available in all cases. Two decision tree-based models, Random Forest and XGBoost, were used as predictive models and performance was evaluated with 5-fold cross validation. Area under the receiver operating characteristic curve (AUROC) and area under precision/recall curve (AUPR) were used as metrics for evaluating model performance. Mean and standard error of performance metrics were calculated.

Results: The cohort included 21,232 vaginal deliveries. There were 415 (1.95%) vaginal deliveries complicated by SD that occurred in 406 patients. An AUROC of 0.73 ± 0.01 (mean ± standard error) was achieved for RandomForest model and 0.78 ± 0.01 for XGBoost model for BW. BW was then added as a variable to variable sets from the antepartum period and each labor stage to assess performance change. The RandomForest model predicted patients at risk for SD better than BW alone, but only the AUROC was statistically significant at all stages (p < 0.05). (Figure 1) This finding was not demonstrated in the XGBoost model.

Conclusion: Our machine learning model performed better in predicting SD than EFW alone (using BW as a proxy) at each time period evaluated – the antepartum period, 1st stage and 2nd stage of labor.  While our results indicate that our model may enhance the prediction of SD, these findings should be validated using a more robust data set that includes documented EFW to account for the margin of error between BW and EFW.