Department of Obstetrics and Gynecology, Beijing Haidian Maternal and Child Healthcare Hospital, NO.33 Haidian South Road, Haidian District, Beijing, 100080, China.
Department of Obstetrics and Gynecology, the Seventh Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China.
BMC Pediatr. 2022 Sep 14;22(1):547. doi: 10.1186/s12887-022-03591-w.
Preterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application.
Data used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics.
Data from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81-0.84); master or above (OR = 0.82; 95%CI, 0.81-0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95-0.98) and obesity (OR = 0.94; 95%CI, 0.93-0.96), and prenatal care (OR = 0.48; 95%CI, 0.47-0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26-1.29), black race (OR = 1.26; 95%CI, 1.23-1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22-1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24-1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99-2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16-2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88-3.01), and plurality (OR = 12.99; 95%CI, 12.73-13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686-0.689) and 0.762 (95%CI, 0.762-0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women.
The nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth.
早产是全球严重的公共卫生问题，对孕妇早产进行早期预测可能有助于及时进行干预，降低早产率。本研究旨在开发一种易于临床应用的早产预测模型。
本病例对照研究的数据来自 2018 年至 2019 年国家生命统计系统（NVSS）数据库。采用单因素和多因素逻辑回归分析方法，寻找与早产相关的因素。比值比（OR）和 95%置信区间（CI）作为效应量。曲线下面积（AUC）、准确率、灵敏度和特异度作为模型性能评价指标。
2019 年和 2018 年分别使用 3006989 名孕妇和 3039922 名孕妇的数据进行模型建立和外部验证。在这 3006989 名孕妇中，324700 名（10.8%）发生早产。孕妇较高的教育水平（本科：OR=0.82；95%CI，0.81-0.84；硕士及以上：OR=0.82；95%CI，0.81-0.83）、孕前超重（OR=0.96；95%CI，0.95-0.98）和肥胖（OR=0.94；95%CI，0.93-0.96）、产前保健（OR=0.48；95%CI，0.47-0.50）与降低早产风险相关，而年龄≥35 岁（OR=1.27；95%CI，1.26-1.29）、黑种人（OR=1.26；95%CI，1.23-1.29）、孕前体重不足（OR=1.26；95%CI，1.22-1.30）、孕期吸烟（OR=1.27；95%CI，1.24-1.30）、孕前糖尿病（OR=2.08；95%CI，1.99-2.16）、孕前高血压（OR=2.22；95%CI，2.16-2.29）、既往早产史（OR=2.95；95%CI，2.88-3.01）和多胎（OR=12.99；95%CI，12.73-13.24）与早产风险增加相关。该模型在测试集中的 AUC 和准确率分别为 0.688（95%CI，0.686-0.689）和 0.762（95%CI，0.762-0.763）。此外，还建立了一个基于孕妇及其配偶信息的列线图，以预测孕妇早产的风险。
预测孕妇早产风险的列线图具有良好的性能，相关预测指标在临床上易于获得，可能为早产预测提供一种简单的工具。
