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:
    背景 子痫前期是一种妊娠期特发疾病,早期预测并干预可避免疾病的发生和发展,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生保健并住院分娩的1643例孕妇,收集孕妇的临床数据,按照7∶3的比例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(LASSO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。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 纳入1643例孕妇,年龄(31.8 ± 3.9)岁,共1658次分娩,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。建模组通过LASSO回归分析结果显示,体质量指数>25 kg/m2、既往存在妊娠期高血压/PE/子痫、高纤维蛋白原水平和高血尿酸水平是孕早期发生子痫前期的独立关联因素。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受试者工作曲线下面积分别为0.920(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,预测值与观测值的一致性较好。临床决策曲线分析表明,列线图具有一定的临床应用价值。
    结论 体质量指数>25 kg/m2、既往妊娠期高血压/PE/子痫病史、高纤维蛋白原水平和高血尿酸水平是子痫前期的独立预测因素。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 factors associated with 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 divided into modeling group and validation group according to a ratio of 7:3. The factors independently associated with preeclampsia were screened out by the least absolute shrinkage selection operator (LASSO) 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 1 160 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 independently associated with 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 has certain clinical application value.
    Conclusion BMI>25 kg/m2, previous history of gestational hypertension/PE/eclampsia, fibrinogen and serum uric acid are independently associated with preeclampsia. The prediction model for preeclampsia constructed based on the LASSO regression method has good predictive performance.

     

/

返回文章
返回