利用临床和实验室数据通过机器学习模型预测妊娠早期子痫前期。

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Songchang Chen, Jia Li, Xiao Zhang, Wenqiu Xu, Zhixu Qiu, Siyao Yan, Wenrui Zhao, Zhiguang Zhao, Peirun Tian, Qiang Zhao, Qun Zhang, Weiping Chen, Huahua Li, Xiaohong Ruan, Gefei Xiao, Sufen Zhang, Liqing Hu, Jie Qin, Wuyan Huang, Zhongzhe Li, Shunyao Wang, Rui Zhang, Shang Huang, Xin Wang, Yao Yao, Jian Ran, Danling Cheng, Qi Luo, Teng Pan, Ruyun Gao, Jing Zheng, Yuxuan Wang, Cong Liu, Xianling Cao, Xuanyou Zhou, Naixin Xu, Lanlan Zhang, Xu Han, Haolin Wang, Suihua Feng, Shuyuan Li, Jianguo Zhang, Lijian Zhao, Fengxiang Wei
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引用次数: 0

摘要

背景:本研究旨在描述各种实验室检查指标与临床信息和子痫前期(PE)发展的关系。然后,根据产妇特征和实验室数据,建立早发型子痫前期(EOPE)、晚发型子痫前期(LOPE)和早产子痫前期(preterm PE)的预测模型。方法:在2019年1月至2021年12月期间,我们回顾性地从6家医院招募了144名EOPE、363名LOPE、231名早产儿PE和1458名健康参与者。我们利用所有可获得的临床和实验室数据,在常规产前访问在妊娠早期。EOPE、LOPE和早产儿PE的模型是使用集成机器学习模型和患者临床和实验室数据创建的。结果:通过比较PE患者和健康对照组的实验室变量,我们分别鉴定出7、18、8、15、7、29个EOPE、LOPE和早产儿PE、重度PE、叠加性PE和首次PE的实验室标志物。结合临床和实验室预测因子的综合EOPE和LOPE模型分别优于临床因素模型。综合EOPE模型在区分妊娠早期EOPE与对照组方面表现出良好的敏感性(72.22%,95%可信区间[CI]: 57.59% ~ 86.85%)和特异性(85.25%,95% CI: 80.54% ~ 89.97%)。同样,集合LOPE模型在区分LOPE与健康参与者方面表现出良好的准确性(灵敏度:69.57%,95% CI: 56.27%-82.86%;特异性:85.25%,95% CI: 80.54% ~ 89.97%)。预测得分与入院时的血压呈显著正相关,而与PE患者24小时尿蛋白水平和胎儿生长受限呈负相关。总之,我们的研究确定了预测PE的关键实验室指标。基于临床和实验室数据,所建立的模型在评估子痫前期风险和严重程度方面表现出良好的预测能力。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model.

Background: This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data.

Methods: Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data.

Results: By comparing laboratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 laboratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and laboratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%; specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key laboratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and laboratory data.

Clinical trial number: Not applicable.

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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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