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双胎妊娠中妊娠期高血压疾病预测模型的推导与外部验证:中国东南部的一项回顾性队列研究

Derivation and external validation of prediction model for hypertensive disorders of pregnancy in twin pregnancies: a retrospective cohort study in southeastern China.

作者信息

Zheng Shuisen, Chen Yujuan, Gao Yuting, Chen Xiaoling, Lin Na, Han Qing

机构信息

Department of Obstetrics, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.

Department of Obstetrics, Department of Obstetrics and Gynecology, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, China.

出版信息

BMJ Open. 2024 Dec 3;14(12):e083654. doi: 10.1136/bmjopen-2023-083654.

Abstract

OBJECTIVE

We aimed to develop and validate an effective prediction model for hypertensive disorder of pregnancy (HDP) in twin pregnancies after 28 weeks of gestation.

DESIGN

Retrospective cohort study.

SETTING

Maternity hospital.

PARTICIPANTS

We recruited twin pregnancies who delivered in Fujian Maternity and Child Health Hospital from January 2014 to December 2019 as a training cohort. Besides, we included twin pregnancies delivered at Fujian Maternity and Child Health Hospital; Women and Children's Hospital of Xiamen University from January 2020 to December 2021 as temporal validation set and geographical validation set, respectively.

MAIN OUTCOME MEASURES

We performed univariate analysis, the least absolute shrinkage and selection operator regression and Boruta algorithm to screen variables. Then, we used multivariate logistic regression to construct a nomogram that predicted the risk of HDP in twin pregnancies. We employed the bootstrap resampling method for internal validation, used the receiver operating characteristic (ROC) curve to evaluate the predictive performance of the model and constructed decision curve analysis to assess the clinical benefit of the model. Thereafter validated the nomogram through the index of concordance (C-index) and calibration curves in the temporal validation set and geographical validation set.

RESULTS

Multivariate logistic regression showed that primipara (OR=1.284, 95% CI=1.016 to 1.622), the higher pre-pregnancy body mass index (OR=1.077, 95% CI=1.039 to 1.116), the higher uric acid (OR=1.004, 95% CI=1.002 to 1.005), the higher urea nitrogen (OR=1.198, 95% CI=1.087 to 1.321), the higher creatinine (OR=1.011, 95% CI=1.002 to 1.020), the higher lactate dehydrogenase (OR=1.001, 95% CI=1.000 to 1.002), the higher ratio of large platelets (OR=1.034, 95% CI=1.020 to 1.048), the lower albumin (OR=0.887, 95% CI=0.852 to 0.924), the lower calcium (OR=0.148, 95% CI=0.058 to 0.375) are influencing factors of HDP in twin pregnancies. The area under the ROC curve of the prediction model was 0.763. The C-index were 0.842 and 0.746, respectively, on the temporal validation set and geographical validation set.

CONCLUSIONS

The new model for predicting HDP in twin pregnancies constructed by clinical characteristics and laboratory indicators had high clinical application value. It can be used to individually evaluate the occurrence of HDP in twin pregnancies after 28 weeks of gestation.

摘要

目的

我们旨在开发并验证一种有效的预测模型,用于预测双胎妊娠孕28周后发生妊娠期高血压疾病(HDP)的风险。

设计

回顾性队列研究。

地点

妇产医院。

研究对象

我们招募了2014年1月至2019年12月在福建省妇幼保健院分娩的双胎妊娠作为训练队列。此外,我们分别将2020年1月至2021年12月在福建省妇幼保健院、厦门大学附属妇女儿童医院分娩的双胎妊娠作为时间验证集和地域验证集。

主要观察指标

我们进行单因素分析、最小绝对收缩和选择算子回归以及Boruta算法来筛选变量。然后,我们使用多因素逻辑回归构建一个预测双胎妊娠HDP风险的列线图。我们采用自助重抽样法进行内部验证,使用受试者工作特征(ROC)曲线评估模型的预测性能,并构建决策曲线分析来评估模型的临床益处。此后,通过时间验证集和地域验证集的一致性指数(C指数)和校准曲线对列线图进行验证。

结果

多因素逻辑回归显示,初产妇(OR = 1.284,95%CI = 1.016至1.622)、孕前体重指数较高(OR = 1.077,95%CI = 1.039至1.116)、尿酸较高(OR = 1.004,95%CI = 1.002至1.005)、尿素氮较高(OR = 1.198,95%CI = 1.087至1.321)、肌酐较高(OR = 1.011,95%CI = 1.002至1.020)、乳酸脱氢酶较高(OR = 1.001,95%CI = 1.000至1.002)、大血小板比率较高(OR = 1.034,95%CI = 1.020至1.048)、白蛋白较低(OR = 0.887,95%CI = 0.852至0.924)、钙较低(OR = 0.148,95%CI = 0.058至0.375)是双胎妊娠HDP的影响因素。预测模型的ROC曲线下面积为0.763。时间验证集和地域验证集的C指数分别为0.842和0.746。

结论

由临床特征和实验室指标构建的预测双胎妊娠HDP的新模型具有较高的临床应用价值。它可用于个体化评估孕28周后双胎妊娠HDP的发生情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11f4/11624755/bbdf8ae443df/bmjopen-14-12-g001.jpg

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