ABSTRACT
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: A total of 89 681 delivery hospitalisations. METHODS: We created and evaluated two models, one predicting CSMM at delivery (delivery model) and the other predicting CSMM postpartum following discharge from delivery hospitalisation (postpartum CSMM). We assessed model discrimination and calibration and used bootstrapping for internal validation. MAIN OUTCOME MEASURES: Cardiovascular severe maternal morbidity comprised the following confirmed conditions: pulmonary oedema/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 eight variables. The strongest predictors were severe pre-eclampsia, non-Hispanic Black race/ethnicity, chronic hypertension, gestational hypertension, non-severe pre-eclampsia 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 to identify women at highest risk of CSMM and enable future prevention measures. TWEETABLE ABSTRACT: Risk assessment tools for cardiovascular severe maternal morbidity were developed and internally validated.
