Applications to understand risk factors underlying maternal morbidity.
We are very interested in improving maternal outcomes through data-driven analyses. Through ongoing collaborations with clinicians, we have developed intelligible ML models to predict adverse outcomes in pregnancy. These tools have set new standards for state-of-the-art accuracy in predicting bad outcomes, while simultaneously revealing the underlying risk factors and suggesting better targeted interventions.
References
2024
Interpretable Machine Learning Predicts Postpartum Hemorrhage with Severe Maternal Morbidity in a Lower Risk Laboring Obstetric Population
Background Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk factors rather than predictive models. Objective To develop, validate (internally and externally), and compare a machine learning model for predicting PPH associated with SMM against a standard hemorrhage risk assessment tool in a lower-risk laboring obstetric population. Study Design This retrospective cross-sectional study included clinical data from singleton, term births (>=37 weeks’ gestation) at 19 US hospitals (2016-2021) using data from 44,509 births at 11 hospitals to train a generalized additive model (GAM) and 21,183 births at 8 held-out hospitals to externally validate the model. The outcome of interest was PPH with severe maternal morbidity (blood transfusion, hysterectomy, vascular embolization, intrauterine balloon tamponade, uterine artery ligation suture, uterine compression suture, or admission to intensive care). Cesarean birth without a trial of vaginal birth and patients with a history of cesarean were excluded. We compared the model performance to that of the California Maternal Quality Care Collaborative (CMQCC) Obstetric Hemorrhage Risk Factor Assessment Screen. Results The GAM predicted PPH with an area under the receiver-operating characteristic curve (AUROC) of 0.67 (95% CI 0.64-0.68) on external validation, significantly outperforming the CMQCC risk screen AUROC of 0.52 (95% CI 0.50-0.53). Additionally, the GAM had better sensitivity of 36.9% (95% CI 33.01, 41.02) than the CMQCC screen sensitivity of 20.30% (95% CI 17.40, 22.52) at the CMQCC screen positive rate of 16.8%. The GAM identified in-vitro fertilization as a risk factor (adjusted OR 1.5; 95% CI 1.2-1.8) and nulliparous births as the highest PPH risk factor (adjusted OR 1.5; 95% CI; 1.4-1.6). Conclusion Our model identified almost twice as many cases of PPH as the CMQCC rules-based approach for the same screen positive rate and identified in-vitro fertilization and first-time births as risk factors for PPH. Adopting predictive models over traditional screens can enhance PPH prediction.
2023
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
@article{bosschieter2023interpretable,title={Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes},author={Bosschieter, Tomas M. and Xu, Zifei and Lan, Hui and Lengerich, Benjamin J. and Nori, Harsha and Painter, Ian and Souter, Vivienne and Caruana, Rich},year={2023},journal={Journal of Healthcare Informatics Research},informal_venue={JHIR},keywords={Healthcare, Pregnancy},}
2022
Unique insights into risk factors for antepartum stillbirth using explainable AI
@article{bosschieter2022smfm2,title={Unique insights into risk factors for antepartum stillbirth using explainable AI},author={Bosschieter, Tomas and Xu, Zifei and Lan, Hui and Lengerich, Benjamin and Nori, Harsha and Sitcov, Kristin and Painter, Ian and Souter, Vivienne and Caruana, Rich},journal={American Journal of Obstetrics \& Gynecology},informal_venue={SMFM},volume={},number={},pages={},year={2022},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
Understanding risk factors for shoulder dystocia using interpretable machine learning
@article{lan2022smfm,title={Understanding risk factors for shoulder dystocia using interpretable machine learning},author={Lan, Hui and Xu, Zifei and Bosschieter, Tomas and Lengerich, Benjamin and Nori, Harsha and Sitcov, Kristin and Painter, Ian and Souter, Vivienne and Caruana, Rich},journal={American Journal of Obstetrics \& Gynecology},informal_venue={SMFM},volume={},number={},pages={},year={2022},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
Preterm preeclampsia prediction using intelligible machine learning
@article{bosschieter2022smfm,title={Preterm preeclampsia prediction using intelligible machine learning},author={Bosschieter, Tomas and Xu, Zifei and Lan, Hui and Lengerich, Benjamin and Nori, Harsha and Sitcov, Kristin and Painter, Ian and Souter, Vivienne and Caruana, Rich},journal={American Journal of Obstetrics \& Gynecology},informal_venue={SMFM},volume={},number={},pages={},year={2022},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
Predicting severe maternal morbidity at admission for delivery using intelligible machine learning
@article{xu2022smfm,title={Predicting severe maternal morbidity at admission for delivery using intelligible machine learning},author={Xu, Zifei and Bosschieter, Tomas and Lan, Hui and Lengerich, Benjamin and Nori, Harsha and Sitcov, Kristin and Painter, Ian and Souter, Vivienne and Caruana, Rich},journal={American Journal of Obstetrics \& Gynecology},informal_venue={SMFM},volume={},number={},pages={},year={2022},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
2021
Length of labor and severe maternal morbidity in the NTSV population
@article{lengerich2021length,title={Length of labor and severe maternal morbidity in the NTSV population},author={Lengerich, Benjamin J. and Caruana, Rich and Weeks, William B and Painter, Ian and Spencer, Sydney and Sitcov, Kristin and Daly, Colleen and Souter, Vivienne},journal={American Journal of Obstetrics \& Gynecology},volume={224},number={2},pages={S33},year={2021},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
Insights into severe maternal morbidity in the NTSV population
@article{lengerich2021insights,title={Insights into severe maternal morbidity in the NTSV population},author={Lengerich, Benjamin J. and Caruana, Rich and Weeks, William B and Painter, Ian and Spencer, Sydney and Sitcov, Kristin and Daly, Colleen and Souter, Vivienne},journal={American Journal of Obstetrics \& Gynecology},volume={224},number={2},pages={S629--S630},year={2021},publisher={Elsevier},keywords={Healthcare, Pregnancy},}
