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Machine-learning predictive model of pregnancy-induced hypertension in the first trimester

Abstract

In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (Pā€‰<ā€‰0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805ā€“0.889) in the training set and 0.753 (95% CI: 0.653ā€“0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice.

We develop a model with good performance for clinical prediction of PIH in the first trimester.

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Acknowledgements

We thank the participants involved in this study for their critical contributions. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding

1. Grant for Key Disciplinary Project of Clinical Medicine under the High-level University Development Program, Guangdong, China (2020); 2. Innovation Team Project of Guangdong Universities, China (Natural, No.2019KCXTD003); 3. Supported by 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (2020LKSFG19B); 4. Funding for Guangdong Medical Leading Talent, the First Affiliated Hospital, SUMC, China (2019-2022); 5. Supported by the National Natural Science Foundation of China (No. 82073659); 6. Supported by 2021 Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (2021-88-53); 7. Supported by 2022 Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (2022-124-6).

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Chen, Y., Huang, X., Wu, S. et al. Machine-learning predictive model of pregnancy-induced hypertension in the first trimester. Hypertens Res 46, 2135ā€“2144 (2023). https://doi.org/10.1038/s41440-023-01298-8

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