Computational medicine, present and the future: obstetrics and gynecology perspective

Postpartum readmission for hypertension and pre-eclampsia: development and validation of a predictive model

OBJECTIVE

To develop a model for predicting postpartum readmission for hypertension and pre-eclampsia at delivery discharge and assess external validation or model transportability across clinical sites.

DESIGN

Prediction model using data available in the electronic health record from two clinical sites.

SETTING

Two tertiary care health systems from the Southern (2014-2015) and Northeastern USA (2017-2019).

POPULATION

A total of 28 201 postpartum individuals: 10 100 in the South and 18 101 in the Northeast.

METHODS

An internal-external cross validation (IECV) approach was used to assess external validation or model transportability across the two sites. In IECV, data from each health system were first used to develop and internally validate a prediction model; each model was then externally validated using the other health system. Models were fit using penalised logistic regression, and accuracy was estimated using discrimination (concordance index), calibration curves and decision curves. Internal validation was performed using bootstrapping with bias-corrected performance measures. Decision curve analysis was used to display potential cut points where the model provided net benefit for clinical decision-making.

MAIN OUTCOME MEASURES

The outcome was postpartum readmission for either hypertension or pre-eclampsia <6 weeks after delivery.

RESULTS

The postpartum readmission rate for hypertension and pre-eclampsia overall was 0.9% (0.3% and 1.2% by site, respectively). The final model included six variables: age, parity, maximum postpartum diastolic blood pressure, birthweight, pre-eclampsia before discharge and delivery mode (and interaction between pre-eclampsia × delivery mode). Discrimination was adequate at both health systems on internal validation (c-statistic South: 0.88; 95% confidence interval [CI] 0.87-0.89; Northeast: 0.74; 95% CI 0.74-0.74). In IECV, discrimination was inconsistent across sites, with improved discrimination for the Northeastern model on the Southern cohort (c-statistic 0.61 and 0.86, respectively), but calibration was not adequate. Next, model updating was performed using the combined dataset to develop a new model. This final model had adequate discrimination (c-statistic: 0.80, 95% CI 0.80-0.80), moderate calibration (intercept -0.153, slope 0.960, Emax 0.042) and provided superior net benefit at clinical decision-making thresholds between 1% and 7% for interventions preventing readmission. An online calculator is provided here.

CONCLUSIONS

Postpartum readmission for hypertension and pre-eclampsia may be accurately predicted but further model validation is needed. Model updating using data from multiple sites will be needed before use across clinical settings.

Reducing primary cesarean delivery rate through implementation of a smart intrapartum surveillance system

The rapid changes in clinical maternity situations that occur in a labor and delivery unit can lead to unpredictable maternal and newborn morbidities. Cesarean section (CS) rate is a key indicator of the accessibility and quality of a labor and delivery unit. This retrospective cross-sectional study assesses the nulliparous, term, singleton, vertex (NTSV) cesarean delivery rates before and after the implementation of a smart intrapartum surveillance system. Research data were collected from the electronic medical records of a labor and delivery unit. The primary outcome was the CS rate of the NTSV population. The data of 3648 women admitted for delivery were analyzed. Of the studied deliveries, 1760 and 1888 occurred during the preimplementation and postimplementation periods, respectively. The CS rate for the NTSV population was 31.0% and 23.3% during the preimplementation and postimplementation periods, respectively, indicating a significant 24.7% (p = 0.014) reduction in CS rate after the implementation of the smart intrapartum surveillance system (relative risk, 0.75; 95% confidence interval, 0.71-0.80). In the NTSV population, the vaginal and CS birth groups, no significant difference in terms of newborn weight, neonatal Apgar scores, composite neonatal adverse outcome indicator, and the occurrence of the following: neonatal intensive care unit admission, neonatal meconium aspiration, chorioamnionitis, shoulder dystocia, perineal laceration, placental abruption, postpartum hemorrhage, maternal blood transfusion, and hysterectomy before and after the implementation of the smart intrapartum surveillance system. This study reveals that the use of the smart intrapartum surveillance system can effectively reduce the primary CS rate for low-risk NTSV pregnancies without significantly affecting perinatal outcomes.

Utilizing machine learning to predict unplanned cesarean delivery

OBJECTIVE

To develop a comprehensive machine learning (ML) model predicting unplanned cesarean delivery (uCD) among singleton pregnancies based on features available at admission to labor.

METHODS

A retrospective cohort study from a tertiary medical center. Women with singleton vertex pregnancy of 34 weeks or more admitted for vaginal delivery between March 2011 and May 2019 were included. The cohort was divided into training (80%) and validation (20%) data sets. A separate cohort between June 2019 and April 2021 served as a test data set. Features selection was performed using a Random Forest ML algorithm.

RESULTS

The study population included 73 667 women, of which 4125 (6.33%) underwent uCD. The final model consisted of 13 features, based on prediction importance. The XGBoost model performed best with areas under the curve for the training, validation, and test data sets of 0.874, 0.839, and 0.840, respectively. The model showed a 65% positive predictive value for uCD among women in the 100th centile group, and a 99% or more negative predictive value in the less than 50th centile group. Positive and negative predictive values remained high among subgroups with high pretest probability of uCD.

CONCLUSION

An ML model for the prediction of uCD provides clinically useful risk stratification that remains accurate across gestational weeks 34-42 and among clinical risk groups. The model may be clinically useful for physicians and women admitted for labor.

SYNOPSIS

A machine learning model predicts unplanned cesarean delivery and can inform women's individualized decision making.

Derivation and external validation of risk stratification models for severe maternal morbidity using prenatal encounter diagnosis codes

OBJECTIVE

We sought to develop a prediction model using prenatal diagnosis codes that could help clinicians objectively stratify a women's risk for delivery-related morbidity.

STUDY DESIGN

We performed a prospective cohort study of women delivering at a single academic medical center between 2016 and 2019. Diagnosis codes from outpatient encounters were extracted from the electronic health record. Standard and common machine-learning methods for variable selection were compared. The performance characteristics from the selected model in the training data set-a LASSO model with a lambda that minimized the Bayes information criteria-were compared in a testing and external validation set.

RESULTS

The model identified a group of women, those in the highest decile of predicted risk, who were at a two to threefold increased risk of maternal morbidity.

CONCLUSION

As EHR data becomes more ubiquitous, other data types generated from the prenatal period may improve the model's performance.