Evidence map›Paper›PMID 42064853›Full record

ArticleFrontiers in cardiovascular medicine2026

Development and validation of a lasso-logistic regression-based risk prediction model for retinopathy in patients with hypertensive disorders of pregnancy.

Shuzhen Gan, Yuanyuan Liu, Caiping Lai, Qiong Li

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Article in Frontiers in cardiovascular medicine, 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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5 · Who and what money

Authors and funding

4 authors.

Shuzhen GanDepartment of Obstetrics, Ganzhou People's Hospital, Ganzhou, Jiangxi, China.
Yuanyuan LiuDepartment of Obstetrics, Ganzhou People's Hospital, Ganzhou, Jiangxi, China.
Caiping LaiDepartment of Obstetrics, Xunwu County People's Hospital, Ganzhou, Jiangxi, China.
Qiong LiDepartment of Obstetrics, Ganzhou People's Hospital, Ganzhou, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To identify risk factors for retinopathy in patients with hypertensive disorders of pregnancy (HDP) and develop a predictive nomogram model. Methods: A total of 667 patients with PIH treated at our hospital between December 2020 and December 2025 were retrospectively enrolled based on M. Kendall sample size estimation. Patients were randomly assigned to a development cohort ( Results: Among the 400 patients in the modeling group, 112 developed retinopathy, with an incidence of 28.0%. Eight potential predictors were identified by LASSO regression. Multivariate analysis revealed that HDP onset <28 weeks (OR = 7.027), disease duration >3 weeks (OR = 11.548), proteinuria (+++) (OR = 14.535), hematocrit >0.35 (OR = 16.733), and systolic blood pressure (OR = 1.143) were independent risk factors (all Conclusion: The nomogram based on clinical and laboratory indicators demonstrated good discrimination and calibration in internal and external validation of retinopathy risk in PIH patients, with good clinical applicability.

Indexed as

lasso-logistic regressionnomogrampregnancy induced hypertensionretinopathyrisk prediction model

Identifiers

PMID42064853
PMCPMC13125053

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