Cardiovascular Severe Maternal Morbidity in Pregnant and Postpartum Women: Development and Internal Validation of Risk Prediction Models

OBJECTIVES To develop and internally validate risk prediction models identifying women at risk for cardiovascular severe maternal morbidity (CSMM). DESIGN A retrospective cohort study. SETTING An obstetric teaching hospital between 2007 and 2017. POPULATION 89,681 delivery hospitalizations. METHODS We created and evaluated two models, one predicting CSMM at delivery (delivery model) and the other predicting CSMM postpartum following discharge from delivery hospitalization (postpartum CSMM). We assessed model discrimination and calibration and used bootstrapping for internal validation. MAIN OUTCOME MEASURES CSMM comprised the following confirmed conditions: pulmonary edema/acute heart failure, myocardial infarction, aneurysm, cardiac arrest/ventricular fibrillation, heart failure/arrest during surgery or procedure, cerebrovascular disorders, cardiogenic shock, conversion of cardiac rhythm, and difficult-to-control severe hypertension. RESULTS The delivery model contained 11 variables and 3 interaction terms. The strongest predictors were gestational hypertension, chronic hypertension, multiple gestation, cardiac lesions or valvular heart disease, maternal age ≥ 40 years, and history of poor pregnancy outcome. The postpartum model comprised 8 variables. The strongest predictors were severe preeclampsia, Non-Hispanic Black race/ethnicity, chronic hypertension, gestational hypertension, non-severe preeclampsia, and maternal age ≥ 40 years at delivery. The delivery and postpartum models had an area under the receiver operating characteristic curve of 0.87 (95% CI [0.85, 0.89]) and 0.85 (95% CI [0.80, 0.90]), respectively. Both models were adequately calibrated and performed well on internal validation. CONCLUSIONS These tools may help providers identify women at highest risk of CSMM and enable future prevention measures.