Abstract
Abstract Background/objectives Hypertensive disorders of pregnancy (HDP) are major contributors to maternal and perinatal morbidity and mortality. Abnormal blood pressure circadian rhythm (BPCR) has been linked to poor prognosis in hypertension, but its impact on pregnancy outcomes in HDP remains unclear. This study aimed to assess the influence of BPCR on adverse pregnancy outcomes in HDP and to develop a predictive nomogram. Methods Pregnant individuals aged ≥ 20 years diagnosed with HDP who received prenatal care and delivered at the Beijing Genertec Aerospace Hospital, between May 2018 and May 2025 were consecutively enrolled. Those with chronic kidney, liver, or heart disease, or psychiatric disorders were excluded. Multivariate logistic regression was performed to identify independent risk factors, which were incorporated into a nomogram using R software. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis with internal validation. Additional bootstrap internal validation was performed in the complete dataset to assess model stability. Subgroup analyses were conducted according to HDP subtype. Results Among 555 eligible participants, 189 (34.1%) experienced adverse pregnancy outcomes. Multivariate analysis identified systolic BPCR (P = 0.008, OR = 0.86, 95% CI = 0.78–0.96), diastolic BPCR (P = 0.040, OR = 0.91, 95% CI = 0.83–0.99), and mean arterial pressure (MAP) (P = 0.010, OR = 1.07, 95% CI = 1.02–1.13) as independent risk factors. The nomogram showed good discrimination, and bootstrap internal validation demonstrated stable performance with an AUC of 0.865, a calibration intercept of −0.034, and a calibration slope of 0.946. Subgroup analyses supported the association of the combined SBPCR–DBPCR–MAP score with adverse pregnancy outcomes. Conclusions SBPCR, DBPCR, and MAP were independent predictors of adverse pregnancy outcomes in individuals with HDP. The nomogram based on these ABPM-derived variables demonstrated good internally validated performance and may help individualized risk stratification.