Background: Hypertensive disorders of pregnancy are one of the leading causes of maternal morbidity and mortality worldwide. Management of these conditions can pose many clinical dilemmas and can be particularly challenging during the immediate postpartum period. Models for predicting and managing postpartum hypertension are necessary to help address this clinical challenge.
Objective: This study aimed to evaluate predictive models of blood pressure spikes in the postpartum period and to investigate clinical management strategies to optimize care.
Study design: This was a retrospective cohort study of postpartum women who participated in remote blood pressure monitoring. A postpartum blood pressure spike was defined as a blood pressure measurement of ≥140/90 mm Hg while on an antihypertensive medication and a blood pressure measurement of ≥150/100 mm Hg if not on an antihypertensive medication. We identified 3 risk level patient clusters (low, medium, and high) when predicting patient risk for a blood pressure spike on postpartum days 3 to 7. The variables used in defining these clusters were peak systolic blood pressure before discharge, body mass index, patient systolic blood pressure per trimester, heart rate, gestational age, maternal age, chronic hypertension, and gestational hypertension. For each risk cluster, we focused on 2 treatments, namely (1) postpartum length of stay (<3 days or ≥3 days) and (2) discharge with or without blood pressure medications. We evaluated the effectiveness of the treatments in different subgroups of patients by estimating the conditional average treatment effect values in each cluster using a causal forest. Moreover, for all patients, we considered discharge with medication policies depending on different discharge blood pressure thresholds. We used a doubly robust policy evaluation method to compare the effectiveness of the policies.
Results: A total of 413 patients were included, and among those, 267 (64.6%) had a postpartum blood pressure spike. The treatments for patients at medium and high risk were considered beneficial. The 95% confidence intervals for constant marginal average treatment effect for antihypertensive use at discharge were -3.482 to 4.840 and - 5.539 to 4.315, respectively; and for a longer stay they were -5.544 to 3.866 and -7.200 to 4.302, respectively. For patients at low risk, the treatments were not critical in preventing a blood pressure spike with 95% confidence intervals for constant marginal average treatment effect of 1.074 to 15.784 and -2.913 to 9.021 for the different treatments. We considered the option to discharge patients with antihypertensive use at different blood pressure thresholds, namely (1) ≥130 mm Hg and/or ≥80 mm Hg, (2) ≥140 mm Hg and/or ≥90 mm Hg, (3) ≥150 mm Hg and/or ≥ 100 mm Hg, or (4) ≥160 mm Hg and/or ≥ 110 mm Hg. We found that policy (2) was the best option with P<.05.
Conclusion: We identified 3 possible strategies to prevent outpatient blood pressure spikes during the postpartum period, namely (1) medium- and high-risk patients should be considered for a longer postpartum hospital stay or should participate in daily home monitoring, (2) medium- and high-risk patients should be prescribed antihypertensives at discharge, and (3) antihypertensive treatment should be prescribed if patients are discharged with a blood pressure of ≥140/90 mm Hg.
Keywords: blood pressure spike; hypertension; machine learning; postpartum; random forest method.
Copyright © 2024. Published by Elsevier Inc.