Development and validation of nomograms to predict clinical outcomes of preeclampsia
BackgroundPreeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE.MethodsEligible patients with PE...
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1292458/full |
| _version_ | 1797263144812281856 |
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| author | Yan Xia Yan Xia Yao Wang Yao Wang Shijin Yuan Jiaming Hu Jiaming Hu Lu Zhang Lu Zhang Jiamin Xie Jiamin Xie Yang Zhao Yang Zhao Jiahui Hao Yanwei Ren Shengjun Wu Shengjun Wu |
| author_facet | Yan Xia Yan Xia Yao Wang Yao Wang Shijin Yuan Jiaming Hu Jiaming Hu Lu Zhang Lu Zhang Jiamin Xie Jiamin Xie Yang Zhao Yang Zhao Jiahui Hao Yanwei Ren Shengjun Wu Shengjun Wu |
| author_sort | Yan Xia |
| collection | DOAJ |
| description | BackgroundPreeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE.MethodsEligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve.ResultsIn the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability.ConclusionBased on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients. |
| first_indexed | 2024-04-25T00:08:21Z |
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| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-25T00:08:21Z |
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| spelling | doaj.art-aed3d815083f4f6c90fe697ebadb34a62024-03-14T04:22:43ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-03-011510.3389/fendo.2024.12924581292458Development and validation of nomograms to predict clinical outcomes of preeclampsiaYan Xia0Yan Xia1Yao Wang2Yao Wang3Shijin Yuan4Jiaming Hu5Jiaming Hu6Lu Zhang7Lu Zhang8Jiamin Xie9Jiamin Xie10Yang Zhao11Yang Zhao12Jiahui Hao13Yanwei Ren14Shengjun Wu15Shengjun Wu16Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaDepartment of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, ChinaDepartment of Gynaecology and Obstetrics, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaKey Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, ChinaBackgroundPreeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE.MethodsEligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve.ResultsIn the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability.ConclusionBased on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.https://www.frontiersin.org/articles/10.3389/fendo.2024.1292458/fullpre-eclampsianomogrambiomarkerpredictive modelperipheral biomarkers |
| spellingShingle | Yan Xia Yan Xia Yao Wang Yao Wang Shijin Yuan Jiaming Hu Jiaming Hu Lu Zhang Lu Zhang Jiamin Xie Jiamin Xie Yang Zhao Yang Zhao Jiahui Hao Yanwei Ren Shengjun Wu Shengjun Wu Development and validation of nomograms to predict clinical outcomes of preeclampsia Frontiers in Endocrinology pre-eclampsia nomogram biomarker predictive model peripheral biomarkers |
| title | Development and validation of nomograms to predict clinical outcomes of preeclampsia |
| title_full | Development and validation of nomograms to predict clinical outcomes of preeclampsia |
| title_fullStr | Development and validation of nomograms to predict clinical outcomes of preeclampsia |
| title_full_unstemmed | Development and validation of nomograms to predict clinical outcomes of preeclampsia |
| title_short | Development and validation of nomograms to predict clinical outcomes of preeclampsia |
| title_sort | development and validation of nomograms to predict clinical outcomes of preeclampsia |
| topic | pre-eclampsia nomogram biomarker predictive model peripheral biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1292458/full |
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