Development and validation of nomograms to predict clinical outcomes of preeclampsia

Yan Xia; Yao Wang; Shijin Yuan; Jiaming Hu; Lu Zhang; Jiamin Xie; Yang Zhao; Jiahui Hao; Yanwei Ren; Shengjun Wu

doi:10.3389/fendo.2024.1292458

Development and validation of nomograms to predict clinical outcomes of preeclampsia

Front Endocrinol (Lausanne). 2024 Mar 14:15:1292458. doi: 10.3389/fendo.2024.1292458. eCollection 2024.

Authors

Yan Xia^#^{1

2}, Yao Wang^#^{1

2}, Shijin Yuan³, Jiaming Hu^{1

2}, Lu Zhang^{1

2}, Jiamin Xie^{1

2}, Yang Zhao^{1

2}, Jiahui Hao⁴, Yanwei Ren⁵, Shengjun Wu^{1

2}

Affiliations

¹ Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China.
³ Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁴ School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
⁵ Department of Gynaecology and Obstetrics, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

^# Contributed equally.

Abstract

Background: Preeclampsia (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.

Methods: Eligible 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.

Results: In 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.

Conclusion: Based 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.

Keywords: pre-eclampsia; biomarker; nomogram; peripheral biomarkers; predictive model.

MeSH terms

Biomarkers
Female
Humans
Nomograms*
Pre-Eclampsia* / diagnosis
Pregnancy
Pregnancy Trimester, First
Pregnancy, Twin

Substances

Biomarkers

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Project supported by Medical and Health Science and Technology Program of Zhejiang Province (Grant No. 2023KY811), National Natural Science Foundation of China (Grant No. 82102476) and Natural Science Foundation of Zhejiang province (Grant No. LQ21H190005).