目的 筛选妊娠糖尿病(GDM)发生的相关危险因素,构建孕妇GDM发病风险的预测模型,并验证该模型的预测效能。方法 回顾性分析2017-01至2019-01于北京妇产医院建档并分娩的891名孕妇的临床资料。采用随机数字方法,按7∶3的比例分配,将研究对象分为两个子集,即建模集(623名)和验证集(268名)。根据孕24～28周的75 g糖耐量试验结果,将孕妇分为GDM组与糖耐量正常组。比较两组孕妇年龄、孕早期空腹血糖(FBG)、白细胞计数(WBC)、中性粒细胞计数(NE)、淋巴细胞计数(LY)、中性粒细胞/淋巴细胞比值(NLR)、红细胞压积(HCT)、血清铁(Fe)、D二聚体(D-D)、纤维蛋白原(FIB)、总胆固醇(CHO)、三酰甘油(TG)、高密度脂蛋白(HDL)、低密度脂蛋白(LDL)等临床及实验室指标,应用 LASSO 回归分析优化筛选变量,通过多因素logistic回归分析建立预测模型,并绘制列线图。采用受试者工作特征(ROC)曲线、校准曲线和 Hosmer-Lemeshow 拟合优度检验验证和评价预测模型的区分度和校准度;决策曲线分析(DCA)评估预测模型的临床有效性。结果 使用 LASSO 回归分析筛选出12个预测变量:年龄、NE、LY、FBG、HCT、Fe、D-D、FIB、CHO、TG、HDL、LDL,对这12个预测变量进行多因素 logistic 回归分析,结果显示:年龄(OR=1.123,95%CI:1.058～1.192)、NE(OR=1.216,95%CI:1.061～1.393)、FBG(OR=5.528,95%CI:2.907～10.83)、TG(OR=1.193,95%CI:0.949～1.469)、HDL(OR=0.19,95%CI: 0.069～0.496)是发生GDM的危险因素(P＜0.05)。根据这5个危险因素绘制列线图,构建预测模型。预测建模集患者发生GDM的ROC曲线下面积(AUC)为0.772(95%CI:0.7211～0.8235),验证集患者发生GDM的AUC为0.747(95%CI:0.6597～0.8352),建模集与验证集 AUC无统计学差异(P=0.6325)。Hosmer-Lemeshow拟合优度检验显示拟合度较好(建模集P=0.8335;验证集P=0.07015)。建模集与验证集DCA结果显示的阈值概率处于0.01～0.55、0.01～0.50时,具有临床实用价值,预测模型在临床上是有获益的。结论 孕妇发生GDM的风险概率,可通过该列线图预测模型(包含该5个预测变量:年龄、NE、FBG、TG、HDL)进行预测。
Abstract
Objective To screen the related risk factors of gestational diabetes mellitus(GDM), construct a prediction model of the risk of GDM in pregnant women, and verify the predictive efficacy of the model. Methods The clinical data of 891 pregnant women who delivered in Beijing Obstetrics and Gynecology Hospital from January 2017 to January 2019 were retrospectively analyzed. In order to establish and verify the prediction model, the research objects were divided into two subsets, namely modeling set (623 cases) and validation set(268 cases), by random number method and 7:3 ratio. According to the results of 75g glucose tolerance test during 24-28 weeks of gestation, pregnant women were divided into GDM group and normal glucose tolerance group. The age, fasting blood glucose(FBG), white blood cell count(WBC), neutrophil count(NE), lymphocyte count(LY), neutrophil lymphocyte ratio(NLR), hematocrit(HCT), serum ferritin(Fe), D-dimer(D-D), fibrinogen(FIB), total cholesterol(Cho), triglyceride(TG), high-density lipoprotein(HDL) and low-density lipoprotein(LDL) were compared between the two groups. LASSO regression was used to optimize the screening of variables, and multi-factor logistic regression analysis was used to establish the prediction model and draw a nomogram. Receiver operating characteristic(ROC) curve, calibration curve and Hosmer-Lemeshow goodness of fit test were used to verify and evaluate the discrimination and calibration of the prediction model, and decision curve analysis(DCA) was used to evaluate the clinical validity of the prediction model. Results LASSO regression analysis was used to screen out 12 predictive variables: age, NE, Ly, FBG, HCT, Fe, D-D, FIB, Cho, TG, HDL and LDL. Multivariate logistic regression analysis was performed on these 12 predictors and the results showed that age[OR=1.123, 95%CI: 1.058-1.192], NE[OR=1.216, 95%CI: 1.061-1.393], FBG[OR=5.528, 95%CI: 2.907-10.83], TG[OR=1.193, 95%CI: 0.949-1.469] and HDL[OR=0.19, 95%CI: 0.069-0.496] were risk factors for GDM (P<0.05). Based on these 5 risk factors, a nomogram was drawn, and a prediction model was constructed. The area under ROC curve(AUC) for the prediction of GDM in the modeling set was 0.772[95% CI(0.7211, 0.8235)], and 0.747 [95% CI(0.6597, 0.8352)] for GDM in the validation set, with no significant difference in AUC between modeling set and validation set (P=0.6325). Hosmer-lemeshow goodness-of-fit test showed a good fit (modeling set P=0.8335; validation set P=0.07015). When the DCA threshold probabilities were between 0.01-0.55 and 0.01-0.50, the model set and the verification set had clinical practical value, and the prediction model was beneficial in clinic. Conclusions A nomogram prediction model with five predictors (Age, NE, FBG, TG, HDL) can be used to predict the risk of GDM in pregnant women.
关键词
妊娠期糖尿病 /
危险因素 /
列线图 /
预测模型
Key words
gestational diabetes mellitus /
risk factors /
nomogram /
prediction model
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参考文献
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基金
首都医科大学附属北京妇产医院中青年学科骨干培养专项(FCYY202002)
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