Development and validation of a machine-learning model for prediction of shoulder dystocia.

OBJECTIVES: To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of suspected macrosomia. METHODS: We used electronic health records (EHR) from the Sheba Medical Center in Israel to develop the model (derivation cohort) and EHR from the University of California San Francisco Medical Center to validate the model's accuracy and clinical efficacy (validation cohort). Subsequent to application of inclusion and exclusion criteria, the derivation cohort included 686 singleton vaginal deliveries, of which 131 were complicated by ShD, and the validation cohort included 2584 deliveries, of which 31 were complicated by ShD. For each of these deliveries, we collected maternal and neonatal delivery outcomes coupled with maternal demographics, obstetric clinical data and sonographic fetal biometry. Biometric measurements and their derived estimated fetal weight were adjusted (aEFW) according to gestational age at delivery. A ML pipeline was utilized to develop the model. RESULTS: In the derivation cohort, the ML model provided significantly better prediction than did the current clinical paradigm based on fetal weight and maternal diabetes: using nested cross-validation, the area under the receiver-operating-characteristics curve (AUC) of the model was 0.793 ± 0.041, outperforming aEFW combined with diabetes (AUC = 0.745 ± 0.044, P = 1e-16 ). The following risk modifiers had a positive beta that was > 0.02, i.e. they increased the risk of ShD: aEFW (beta = 0.164), pregestational diabetes (beta = 0.047), prior ShD (beta = 0.04), female fetal sex (beta = 0.04) and adjusted abdominal circumference (beta = 0.03). The following risk modifiers had a negative beta that was < -0.02, i.e. they were protective of ShD: adjusted biparietal diameter (beta = -0.08) and maternal height (beta = -0.03). In the validation cohort, the model outperformed aEFW combined with diabetes (AUC = 0.866 vs 0.784, P = 0.00007). Additionally, in the validation cohort, among the subgroup of 273 women carrying a fetus with aEFW ≥ 4000 g, the aEFW had no predictive power (AUC = 0.548), and the model performed significantly better (0.775, P = 0.0002). A risk-score threshold of 0.5 stratified 42.9% of deliveries to the high-risk group, which included 90.9% of ShD cases and all cases accompanied by maternal or newborn complications. A more specific threshold of 0.7 stratified only 27.5% of the deliveries to the high-risk group, which included 63.6% of ShD cases and all those accompanied by newborn complications. CONCLUSION: We developed a ML model for prediction of ShD and, in a different cohort, externally validated its performance. The model predicted ShD better than did estimated fetal weight either alone or combined with maternal diabetes, and was able to stratify the risk of ShD and neonatal injury in the context of suspected macrosomia. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Second-trimester abortion attitudes and practices among maternal-fetal medicine and family planning subspecialists.

BACKGROUND: Patients deciding to undergo dilation and evacuation (D&E) or induction abortion for fetal anomalies or complications may be greatly influenced by the counseling they receive. We sought to compare maternal-fetal medicine (MFM) and family planning (FP) physicians' attitudes and practice patterns around second-trimester abortion for abnormal pregnancies. METHODS: We surveyed members of the Society for Maternal-Fetal Medicine and Family Planning subspecialists in 2010-2011 regarding provider recommendations between D&E or induction termination for various case scenarios. We assessed provider beliefs about patient preferences and method safety regarding D&E or induction for various indications. We compared responses by specialty using descriptive statistics and conducted unadjusted and adjusted analyses of factors associated with recommending a D&E. RESULTS: Seven hundred ninety-four (35%) physicians completed the survey (689 MFMs, 105 FPs). We found that FPs had 3.9 to 5.5 times higher odds of recommending D&E for all case scenarios (e.g. 80% of FPs and 41% of MFMs recommended D&E for trisomy 21). MFMs with exposure to family planning had greater odds of recommending D&E for all case scenarios (p < 0.01 for all). MFMs were less likely than FPs to believe that patients prefer D&E and less likely to feel that D&E was a safer method for different indications. CONCLUSION: Recommendations for D&E or induction vary significantly depending on the type of physician providing the counseling. The decision to undergo D&E or induction is one of clinical equipoise, and physicians should provide unbiased counseling. Further work is needed to understand optimal approaches to shared decision making for this clinical decision.