Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy
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 diff...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Surgery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2022.1005974/full |
| _version_ | 1811234475921637376 |
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| author | Xinyu Zhang Qi Xu Lin Yang Ge Sun Guoli Liu Cuiting Lian Ziwei Li Dongmei Hao Yimin Yang Xuwen Li |
| author_facet | Xinyu Zhang Qi Xu Lin Yang Ge Sun Guoli Liu Cuiting Lian Ziwei Li Dongmei Hao Yimin Yang Xuwen Li |
| author_sort | Xinyu Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T11:36:56Z |
| format | Article |
| id | doaj.art-daeb7e2dbfc94e7eaa698fbeb24f9782 |
| institution | Directory Open Access Journal |
| issn | 2296-875X |
| language | English |
| last_indexed | 2024-04-12T11:36:56Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj.art-daeb7e2dbfc94e7eaa698fbeb24f97822022-12-22T03:34:49ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2022-10-01910.3389/fsurg.2022.10059741005974Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancyXinyu Zhang0Qi Xu1Lin Yang2Ge Sun3Guoli Liu4Cuiting Lian5Ziwei Li6Dongmei Hao7Yimin Yang8Xuwen Li9Faculty of Environment and Life, Beijing University of Technology, Beijing, ChinaDepartment of Obstetrics, Peking University People’s Hospital, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaDepartment of Obstetrics, Peking University People’s Hospital, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaFaculty of Environment and Life, Beijing University of Technology, Beijing, ChinaBackgroundHypertensive 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.https://www.frontiersin.org/articles/10.3389/fsurg.2022.1005974/fullhypertensive disorders in pregnancysubtyperisk factor analysismodeling methoddynamic prediction modellasso regression |
| spellingShingle | Xinyu Zhang Qi Xu Lin Yang Ge Sun Guoli Liu Cuiting Lian Ziwei Li Dongmei Hao Yimin Yang Xuwen Li Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy Frontiers in Surgery hypertensive disorders in pregnancy subtype risk factor analysis modeling method dynamic prediction model lasso regression |
| title | Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| title_full | Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| title_fullStr | Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| title_full_unstemmed | Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| title_short | Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| title_sort | dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy |
| topic | hypertensive disorders in pregnancy subtype risk factor analysis modeling method dynamic prediction model lasso regression |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2022.1005974/full |
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