Evidence map›Paper›PMID 41845266›Full record

ArticleBMC pregnancy and childbirth2026

Development and internal validation of a predictive model for intrapartum hypertension: a retrospective case-control study.

Yihan Lu, Ruofan Kong, Ruoyun Wei, Lizhou Sun, Xiaotong Tang, Yuanyuan Zhang

Abstract readValidation Study
In one paragraph

Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yihan Lu *Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
Ruofan Kong *Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
Ruoyun WeiDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
Lizhou SunDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. Lizhou_sun@163.com.
Xiaotong TangDepartment of Obstetrics and Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital, Nanjing, 210004, Jiangsu, China. 506434188@qq.com.
Yuanyuan ZhangDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. yuanyuanzhang@njmu.edu.cn.

Funding

National Natural Science Foundation of China 82502044National Natural Science Foundation of China 82571940the Science and Technology Development Fund of Nanjing Medical University NMUB20240082
6 · The paper itself

Abstract

backgroundIntrapartum hypertension is associated with adverse outcomes, yet tools for its prediction are limited. This study aimed to develop a predictive model using routine clinical data.

methodsThis retrospective case-control study included 200 women, comprising 100 with intrapartum hypertension and 100 matched controls. We developed a predictive model by first screening candidate variables through univariate analysis. Variables that showed significant differences were then entered into a multivariate logistic regression model to identify independent predictors. The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC). Internal validation was conducted via the bootstrap method with 500 resamples to correct for optimism and to estimate a more generalizable AUC. Intrapartum hypertension was defined as a systolic blood pressure ≥ 140 mmHg or a diastolic blood pressure ≥ 90 mmHg during labor, confirmed on two separate occasions. Risk factors were identified using univariate and multivariate logistic regression, and predictive performance was assessed by ROC analysis.

resultsThe case group exhibited higher rates of cesarean delivery (14% vs. 4%), higher peak blood pressure, and greater neonatal birth weight (all P < 0.05). Independent risk factors identified were elevated lactate dehydrogenase (LDH), higher admission weight, gestational diabetes mellitus, proteinuria in the second trimester or antenatal period (all P < 0.05). The final multivariate model achieved an AUC of 0.832. After bootstrap internal validation, the optimism-corrected AUC was 0.815.

conclusionA model incorporating five routinely available clinical parameters (including lactate dehydrogenase level, admission weight, gestational diabetes mellitus, and second-trimester or antenatal proteinuria) effectively identifies parturients at high risk for developing intrapartum hypertension. This tool can facilitate early, targeted monitoring in clinical practice.

Indexed as

Hypertension, Pregnancy-InducedAdultCase-Control StudiesCesarean SectionFemaleHumansLogistic ModelsPrediction AlgorithmsPregnancyRetrospective StudiesRisk FactorsROC CurveCase-control studyIntrapartum hypertensionMaternal and neonatal outcomesPpredictive modelRisk factors

Identifiers

PMID41845266
PMCPMC13107844

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