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

Abstract

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, E_max 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.

Keywords: hypertension; postpartum readmission; pre-eclampsia; predict; predictive model.