Aim: To develop a cost-effective, predictive model for hypertensive disorders of pregnancy (HDP) in advanced-aged pregnant women based on demographic and lifestyle factors. Methods: A large prospective, population-based, multicenter cohort study was conducted among advanced maternal-age pregnancies in China. Demographic and blood pressure data were collected from questionnaires of the first prenatal visits. The least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection of risk factors, followed by XGBoost model construction and SHAP (SHapley Additive exPlanations) visualization. Results: Lasso regression identified 9 risk factors, including systolic blood pressure in the first trimester (SBP1), diastolic blood pressure in the first trimester (DBP1), body mass index (BMI), family history of hypertension, multiparous parity, age, alcohol assumption, assisted reproductive technology (ART), and screen use. The XGBoost model was set with an optimized tune grid. The AUC of the model was 0.82, AUPRC of 0.41, with an accuracy of 0.88, sensitivity of 0.46, and specificity of 0.92. The SHAP demonstrated a novel predictive performance and clinical applicability. Conclusion: The XGBoost-derived model offers a practical and simplified tool for individualized risk assessment in advanced maternal age pregnancies, facilitating early intervention and enhanced prenatal care.
Data availability
The datasets generated and analyzed during the current study are not publicly available due to ongoing research using the same cohort but are available from the corresponding author upon reasonable request.
References
Say, L. et al. Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health, 2, 6, p. e323–e 333, https://doi.org/10.1016/S2214-109X(14)70227-X
Jiang, L. et al. A global view of hypertensive disorders and diabetes mellitus during pregnancy. Nat. Rev. Endocrinol. 18 (12), 760–775. https://doi.org/10.1038/s41574-022-00734-y (2022).
Kassebaum, N. J. et al. Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384 (9947), 980–1004. https://doi.org/10.1016/S0140-6736(14)60696-6 (2014).
Chaemsaithong, P., Sahota, D. S. & Poon, L. C. First trimester preeclampsia screening and prediction. Am. J. Obstet. Gynecol. 226 (2), S1071–S. https://doi.org/10.1016/j.ajog.2020.07.020 (2022).
Magee, L. A. et al. The 2021 International Society for the Study of Hypertension in Pregnancy classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens. 27, 148–169. https://doi.org/10.1016/j.preghy.2021.09.008 (2022).
Poon, L. C. et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first‐trimester screening and prevention. Int. J. Gynecol. Obstet. 145 (S1), 1–33. https://doi.org/10.1002/ijgo.12802 (2019).
Wu, P., Green, M. & Myers, J. E. Hypertensive disorders of pregnancy. BMJ (2023). https://doi.org/10.1136/bmj-2022-071653
Tang, S., Liu, Y., Gu, Y., Yang, Q. & Wang, Q. Effects of electronic screen exposure time on hypertensive disorders in pregnancy: a retrospective cohort study. BMC Public. Health. 24 (1), 1367. https://doi.org/10.1186/s12889-024-18793-3 (2024).
Tatarian, T. et al. Impact of Surgeon Workload on Pregnancy Outcomes in Surgical Trainees and Faculty. Ann. Surg. https://doi.org/10.1097/SLA.0000000000006426
Mozurkewich, E. Working conditions and adverse pregnancy outcome: a meta-analysis. Obstet. Gynecol. 95 (4), 623–635. https://doi.org/10.1016/S0029-7844(99)00598-0 (2000).
Lu, Q. et al. Sleep disturbances during pregnancy and adverse maternal and fetal outcomes: A systematic review and meta-analysis. Sleep. Med. Rev. 58, 101436. https://doi.org/10.1016/j.smrv.2021.101436 (2021).
Melzer, K., Schutz, Y., Boulvain, M. & Kayser, B. Physical Activity and Pregnancy: Cardiovascular Adaptations, Recommendations and Pregnancy Outcomes. Sports Med. 40 (6), 493–507. https://doi.org/10.2165/11532290-000000000-00000 (2010).
Mottola, M. F. et al. 367–2019 Canadian Guideline for Physical Activity throughout Pregnancy. J. Obstet. Gynaecol. Can. 40, 1528–1537 (2018).
Miller, H. E. et al. Associations between anxiety, sleep, and blood pressure parameters in pregnancy: a prospective pilot cohort study. BMC Pregnancy Childbirth. 24 (1), 366. https://doi.org/10.1186/s12884-024-06540-w (2024).
Cao, J. et al. Trends in maternal age and the relationship between advanced age and adverse pregnancy outcomes: a population-based register study in Wuhan, China, 2010–2017. Public. Health. 206, 8–14. https://doi.org/10.1016/j.puhe.2022.02.015 (2022).
Tian, M. L. et al. The Effect of 2016 Chinese second-child policy and different maternal age on pregnancy outcomes in Hebei Province, China. BMC Pregnancy Childbirth. 23 (1), 267. https://doi.org/10.1186/s12884-023-05552-2 (2023).
Zhu, H. et al. Trajectories tracking of maternal and neonatal health in eastern China from 2010 to 2021: A multicentre cross-sectional study. J. Glob Health. 14, 04069. https://doi.org/10.7189/jogh.14.04069 (2024).
Zhou, Y. et al. Association of maternal age with adverse pregnancy outcomes: A prospective multicenter cohort study in China. J. Glob Health. 13, 04161. https://doi.org/10.7189/jogh.13.04161 (2023).
Hypertensive Disorders in Pregnancy Subgroup, Chinese Society of Obstetrics and Gynecology & Association, C. M. [Diagnosis and treatment of hypertension and pre-eclampsia in pregnancy: a clinical practice guideline in China(2020)]. Zhonghua Fu Chan Ke Za Zhi. 55 (4), 227–238. https://doi.org/10.3760/cma.j.cn112141-20200114-00039 (2020).
Stepan, H. et al. Clinical utility of sFlt-1 and PlGF in screening, prediction, diagnosis and monitoring of pre-eclampsia and fetal growth restriction. Ultrasound Obstet. Gynecol. Off J. Int. Soc. Ultrasound Obstet. Gynecol. 61 (2), 168–180. https://doi.org/10.1002/uog.26032 (2023).
Spinillo, A. et al. The Effect of Work Activity in Pregnancy on the Risk of Severe Preeclampsia. Aust N Z. J. Obstet. Gynaecol. 35 (4), 380–385. https://doi.org/10.1111/j.1479-828X.1995.tb02146.x (1995).
Agarwal, I. et al. First-trimester occupational exposures and hypertensive disorders of pregnancy among US nurses. Occup. Med. 73 (7), 419–426. https://doi.org/10.1093/occmed/kqad089 (2023).
Cai, C. et al. The impact of occupational shift work and working hours during pregnancy on health outcomes: a systematic review and meta-analysis. Am. J. Obstet. Gynecol. 221 (6), 563–576. https://doi.org/10.1016/j.ajog.2019.06.051 (2019).
Suzumori, N. et al. Effects of long working hours and shift work during pregnancy on obstetric and perinatal outcomes: A large prospective cohort study—Japan Environment and Children’s Study. Birth 47 (1), 67–79. https://doi.org/10.1111/birt.12463 (2020).
Palmer, K. T., Bonzini, M., Harris, E. C., Linaker, C. & Bonde, J. P. Work activities and risk of prematurity, low birth weight and pre-eclampsia: an updated review with meta-analysis. Occup. Environ. Med. 70 (4), 213–222. https://doi.org/10.1136/oemed-2012-101032 (2013).
Nicholls-Dempsey, L., Badeghiesh, A., Baghlaf, H. & Dahan, M. H. How does high socioeconomic status affect maternal and neonatal pregnancy outcomes? A population-based study. Am. J. Obstet. Gynecol. 226 (1), S452–S453. https://doi.org/10.1016/j.ajog.2021.11.751 (2022).
Morales-Suárez-Varela, M. et al. Unemployment and pregnancy outcomes: A study within the Danish National Birth Cohort. Scand. J. Public. Health. 39 (5), 449–456. https://doi.org/10.1177/1403494811407672 (2011).
Funding
This study was supported by the Shanghai Medical Research Program of the Science and Technology Innovation Action Plan (No. 21Y11907800) and the Shanghai Municipal Health Commission (No. 202240082).
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W. Gu contributed to the design of the study and provided the original data. J. Wang analyzed the data and wrote the first draft of the manuscript. H. Zhu revised the manuscript. All authors have read and approved the final manuscript.
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The study was approved by the Peking University Third Hospital (the leading hospital of the project) Medical Science Research Ethics Committee (No. 145-03, 2016). All participants were recruited from the following hospitals: the Peking University Third Hospital, Peking University First Hospital, Obstetrics and Gynecology Hospital of Fudan University, West China Second University Hospital of Sichuan University, the Third Affiliated Hospital of Guangzhou Medical University, Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science & Technology, the First Affiliated Hospital of Chongqing Medical University. Each participating center obtained independent ethical approval from their respective institutional review boards before patient enrollment.
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Wang, J., Zhu, H. & Gu, W. Prediction of hypertensive disorders of pregnancy in advanced-age pregnant women using SHAP value and XGBoost. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44411-w
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DOI: https://doi.org/10.1038/s41598-026-44411-w
