Maternal hypertensive disorders in pregnancy and early childhood cardiometabolic risk factors: The Generation R Study

Gootjes, Dionne V.; Posthumus, Anke G.; Jaddoe, Vincent W. V.; Rijn, Bas B. van; Steegers, Eric A.P.

doi:10.1371/journal.pone.0261351

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…For dynamic factors, the clinical epidemiological factors and biomarkers for this research were body mass index and PlGF. A large number of researches had confirmed that body mass index and PlGF were risk factors for HDP ( 31 , 32 ), so we analyzed the hemodynamic and basic biochemical factors. We divided the pregnant woman's gestational weeks into five stages: 0–13, 14–20, 21–28, 29–34, 35–40 weeks.…”

Section: Methodsmentioning

confidence: 99%

Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy

Zhang

Yang

et al. 2022

Front. Surg.

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BackgroundHypertensive disorders in pregnancy (HDP) are diseases that coexist with pregnancy and hypertension. The pathogenesis of this disease is complex, and different physiological and pathological states can develop different subtypes of HDP.ObjectiveTo investigate the predictive effects of different variable selection and modeling methods on four HDP subtypes: gestational hypertension, early-onset preeclampsia, late-onset preeclampsia, and chronic hypertension complicated with preeclampsia.MethodsThis research was a retrospective study of pregnant women who attended antenatal care and labored at Beijing Maternity Hospital, Beijing Haidian District Maternal and Child Health Hospital, and Peking University People's Hospital. We extracted maternal demographic data and clinical characteristics for risk factor analysis and included gestational week as a parameter in this study. Finally, we developed a dynamic prediction model for HDP subtypes by nonlinear regression, support vector machine, stepwise regression, and Lasso regression methods.ResultsThe AUCs of the Lasso regression dynamic prediction model for each subtype were 0.910, 0.962, 0.859, and 0.955, respectively. The AUC of the Lasso regression dynamic prediction model was higher than those of the other three prediction models. The accuracy of the Lasso regression dynamic prediction model was above 85%, and the highest was close to 92%. For the four subgroups, the Lasso regression dynamic prediction model had the best comprehensive performance in clinical application. The placental growth factor was tested significant (P < 0.05) only in the stepwise regression dynamic prediction model for early-onset preeclampsia.ConclusionThe Lasso regression dynamic prediction model could accurately predict the risk of four HDP subtypes, which provided the appropriate guidance and basis for targeted prevention of adverse outcomes and improved clinical care.

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Section: Methodsmentioning

confidence: 99%