Predicting hypertensive disorders in pregnancy using multiple methods: Models with the placental growth factor parameter

Sun, Ge; Xu, Qi; Zhang, Song; Yang, Lin; Li, Guoli; Meng, Yu; Chen, Aiqing; Yang, Yimin; Li, Xuwen; Hao, Dongmei; Liu, Xiaohong; Shao, Jing

doi:10.3233/thc-218040

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…According to clinical needs and model effect, the best HDP prediction model with PlGF parameters in the second trimester (15-26 weeks) was finally selected. However, their performance needs to be improved [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

The Risk and Clinical Treatment of Hypertensive Diseases in Pregnant Women

Yu²,

Wang³

2022

BioMed Research International

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Hypertensive disorders of pregnancy are a group of pregnancy-related diseases characterized by the coexistence of pregnancy and elevated blood pressure, which seriously endanger the health of mothers and infants, and are one of the main causes of maternal and perinatal deaths. The purpose of this paper is to investigate the clinical analysis of vitamin E and astragalus in the adjuvant treatment of hypertensive disorders in pregnancy and to describe the learning model. This paper puts forward the problem of clinical treatment, which is established on the basis of adjuvant therapy, then narrates around the clinical characteristics of gestational hypertension, and designs and analyzes the experimental design and analysis of adjuvant therapy with vitamin E and astragalus. The experimental results showed that the delivery methods of the three groups of patients were compared P < 0.05 . Compared with the traditional Chinese medicine control group and the vitamin E control group, there were more vaginal births in the experimental group, 36 patients in total. It shows that astragalus and vitamin E can alleviate the disease in different aspects and can effectively intervene in gestational hypertension.

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Section: Related Workmentioning

confidence: 99%

The Risk and Clinical Treatment of Hypertensive Diseases in Pregnant Women

Yu²,

Wang³

2022

BioMed Research International

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“…Poon et al ( 29 ) found that the early-onset preeclampsia prediction model had a high detection rate of 93.1% for early-onset preeclampsia, but only 35.7% and 18.3% for late-onset preeclampsia and gestational hypertension. Sun et al ( 30 ) compared the prediction effects of different methods on HDP and found that the Lasso regression method had the best prediction effect.…”

Section: Introductionmentioning

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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Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters

Mousavi,

Tierney,

Robichaux

et al. 2024

Preprint

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Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention. This study developed an ML model to identify HDP risk before clinical onset using affordable methods. Features were extracted from blood pressure (BP) measurements, body mass index values (BMI) recorded during the first and second trimesters, and maternal demographic information. We employed a random forest classification model for its robustness and ability to handle complex datasets. Our dataset, gathered from large academic medical centers in Atlanta, Georgia, United States (2010-2022), comprised 1,190 patients with 1,216 records collected during the first and second trimesters. Despite the limited number of features, the model's performance demonstrated a strong ability to accurately predict HDPs. The model achieved an F1-score, accuracy, positive predictive value, and area under the receiver-operating characteristic curve of 0.76, 0.72, 0.75, and 0.78, respectively. In conclusion, the model was shown to be effective in capturing the relevant patterns in the feature set necessary for predicting HDPs. Moreover, it can be implemented using simple devices, such as BP monitors and weight scales, providing a practical solution for early HDPs prediction in low-resource settings with proper testing and validation. By improving the early detection of HDPs, this approach can potentially help with the management of adverse pregnancy outcomes.

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Predicting hypertensive disorders in pregnancy using multiple methods: Models with the placental growth factor parameter

Cited by 5 publications

References 18 publications

The Risk and Clinical Treatment of Hypertensive Diseases in Pregnant Women

The Risk and Clinical Treatment of Hypertensive Diseases in Pregnant Women

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

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters

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