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A Visual Prognostic Model of Delivery Time in Preeclampsia Patients: A Nationwide Retrospective Analysis of 7005 Patients

Authors :
Xiaoyan Ren
Shuangchun Liu
Changyin Wang
Cuisheng Zhao
Xinguo Peng
Jie Liu
Longqiang Xu
Jinbo Liu
Jingli Wang
Lu Zhang
Shiguo Liu
Qian Tang
Dahua Meng
Tao Huang
Wenke Zhang
Fuqiang Cai
Jun Zhou
Yuanhua Ye
Xi-bing Wang
Congying Li
Dongmei Yang
Ying Zhan
Jiayong Zheng
Xueli Dai
Ping Tan
Lanlan Wang
Deguo Lu
Qifang Bo
Rongyi Cao
Chenyu Li
Xin Liu
Yan Zhang
Yan Cai
Yan Xu
Source :
SSRN Electronic Journal.
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

Purpose: Preeclampsia (PE) is a common complication during pregnancy that seriously affects the health of both the mother and child due to its insidious onset and delayed diagnosis. Therefore, a retrospective evaluation is necessary for developing a clinical nomogram for predicting prognosis among PE patients. Methods: We developed a nomogram to predict PE prognosis based on 5058 patients recruited from Shandong Province, China between January 2012 and November 2017; then, we externally validated the nomogram in a separate cohort of 1947 patients across China by testing the calibration and discrimination of the nomogram in predicting the delivery time among PE patients. Results: According to forward stepwise linear regression analyses, the time of hypertension onset (TOH) is the most predictive factor in the prognosis of patients with PE, followed by the urine protein, serum creatinine, and alkaline phosphatase levels and ten other clinical parameters. Then, we developed a prognostic visualized nomogram and a risk assessment table to predict the outcome of PE patients and found that PE patients with high scores may have an earlier delivery time; this finding was validated by performing calibration and discrimination analyses, and the agreement between the nomogram predictions and actual delivery times was excellent in both the primary and validation cohorts. In addition, this nomogram could estimate the fetal prognosis. Conclusion: We established and validated a nomogram that can predict the prognosis of PE patients. This practical prognostic PE model may be helpful for clinicians in making clinical decisions and providing treatment. Funding Statement: This work was supported by the National Key Research and Development Program of China (2016YFC1000300), the National Natural Science Foundation of China (81371499, 81170688, 81470973, and 81770679), the Shandong Provincial Natural Science Foundation, China (ZR2009CM094). Declaration of Interests: The authors declare there is no potential conflict of interests. Ethics Approval Statement: Ethical approval was obtained from the Institutional Review Boards by the respective participating institutions, and all PE patients provided signed written informed consent.

Details

ISSN :
15565068
Database :
OpenAIRE
Journal :
SSRN Electronic Journal
Accession number :
edsair.doi...........aa87fca0eb94154ea253388dd36c790e
Full Text :
https://doi.org/10.2139/ssrn.3343631