基于孕早期产检指标的子痫前期临床预测模型构建研究

桂琦; 侯伟; 胡甲琳; 姜淑芳; 游艳琴; 卢彦平

doi:10.12435/j.issn.2095-5227.2024.166

基于孕早期产检指标的子痫前期临床预测模型构建研究

Construction of a clinical prediction model for pre-eclampsia based on early pregnancy prenatal examination indicators

摘要

摘要:
背景子痫前期是一种妊娠期特发疾病，早期预测并干预可以避免疾病发生和发展，改善妊娠结局。
目的探究发生子痫前期的孕早期影响因素，构建孕早期子痫前期发生风险的临床预测模型。
方法选取2021年1月— 2023年12月在解放军总医院第一医学中心围产保健并住院分娩的1643名孕妇，收集孕妇的临床数据，按照7：3随机划分为建模组和验证组，采用最小绝对收缩选择算子回归分析方法筛选出子痫前期的独立影响因素并构建预测模型。并对构建的临床预测模型的区分度、校准度和临床实用性进行评估。
结果 1 643名孕妇，共1 658次分娩，年龄(31.8 ± 3.9)岁，其中子痫前期组早发型子痫前期26次妊娠，晚发型子痫前期36次妊娠，子痫前期患病率为3.95%。建模组共纳入1160次分娩，验证组498例。其中建模组通过LASSO回归分析结果显示，BMI＞25 kg/m²、既往存在妊娠期高血压/PE/子痫、纤维蛋白原水平及血尿酸水平是孕早期发生子痫前期的危险因素。由此构建子痫前期的预测模型，预测模型建模组和验证组的AUC分别为0.92(95% CI：0.880 ~ 0.971)和0.884(95% CI：0.819 ~ 0.949)。列线图的校准曲线表明，预测值与观测值之间的一致性较好。临床决策曲线分析表明，列线图具有一定的实际意义。
结论 BMI＞25 kg/m²、既往妊娠期高血压/PE/子痫病史、纤维蛋白原及血尿酸是子痫前期的独立危险因素。基于LASSO回归方法构建的子痫前期预测模型预测性能良好。

Abstract:
Background Preeclampsia is a pregnancy-specific disease. Early prediction and intervention can avoid the occurrence and development of this disease and improve pregnancy outcomes.
Objective To explore the influencing factors of preeclampsia in early pregnancy and construct a clinical prediction model for the risk of preeclampsia in early pregnancy.
Methods Clinical data about 1643 pregnant women who received perinatal care and hospitalized in the First Medical Center of Chinese PLA General Hospital from January 2021 to December 2023 were collected. They were randomly divided into modeling group and validation group according to a ratio of 7:3. The independent influencing factors of preeclampsia were screened out by the least absolute shrinkage selection operator regression analysis method, and a prediction model was constructed. The discrimination, calibration and clinical practicality of the constructed clinical prediction model were evaluated.
Results A total of 1 643 pregnant women who had 1 658 deliveries, with an average age of (31.8 ± 3.9) years, were selected. Among them, the early-onset preeclampsia group had 26 pregnancies, and the late-onset preeclampsia group had 36 pregnancies, resulting in a preeclampsia prevalence rate of 3.95%. The modeling group included 1160 deliveries, and the validation group included 498 deliveries. The results of LASSO regression analysis showed that BMI＞25 kg/m², previous history of gestational hypertension/PE/eclampsia, fibrinogen level and serum uric acid level were risk factors for preeclampsia in early pregnancy. A prediction model for preeclampsia was constructed based on this method. The AUCs of the modeling group and the validation group were 0.920 (95% CI: 0.880-0.971) and 0.884 (95% CI: 0.819-0.949), respectively. The model had a high degree of consistency.
Conclusion BMI＞25 kg/m², previous history of gestational hypertension/PE/eclampsia, fibrinogen and serum uric acid are independent risk factors for preeclampsia. The prediction model for preeclampsia constructed based on the LASSO regression method has good predictive performance.

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