Abstract：
OBJECTIVE: To develop a prediction model for hypertensive disorders in pregnancy (HDP) and gestational diabetes (GDM) in twin pregnancies utilizing characteristics at the prenatal care entry level.
METHODS: Cross-sectional study using the US national live birth data between 2016 and 2021. The association of all prenatal candidate variables with HDP and GDM was tested with uni- and multi-variable logistic regression analyses. Prediction models were built with generalized linear models using the logit link function and classification and regression tree approach (XGboost) machine learning (ML) algorithm. Performance was assessed with repeated 2-fold cross-validation and performance metrics we considered were area under the curve (AUC). P value <0.001 was considered statistically significant.
RESULTS: A total of 707,198 twin pregnancies were included in the HDP analysis and 723,882 twin pregnancies for the GDM analysis. The incidence of HDP and GDM significantly increased from 12.2% in 2016 to 15.4% in 2021 and from 8.1% in 2016 to 10.7% in 2021, respectively. Factors that increase the risk of HDP in twin gestations are maternal age <20, age≥35, infertility, prepregnancy DM, non-Hispanic Black population, obesity, and those with Medicaid insurance (p<0.001). Factors that more than doubled the risk are obesity class II and III (p<0.001). Factors that increase the risk of GDM in twin gestations are age <25, age≥30, history of infertility, prepregnancy hypertension, non-Hispanic Asian population, non-US nativity, and obesity (p<0.001). Factors that more than doubled the risk are maternal age ≥ 30 years, non-Hispanic Asian, and class I, II, and III maternal obesity ( p<0.001). For both HDP and GDM, the performance of the ML and logistic regression model was mostly similar with negligible difference in terms of all tested performance domains. The AUC of the final ML model for HDP and GDM were 0.62±0.004, and 0.67±0.004, respectively.
CONCLUSIONS: The incidence of HDP and GDM in twin gestations is increasing. The predictive accuracy of the machine learning model for both HDP and GDM in twin gestations is similar to that of the logistic regression model. Both models had modest performance, well-calibrated, and neither had a poor fit. This article is protected by copyright. All rights reserved.
METHODS: Cross-sectional study using the US national live birth data between 2016 and 2021. The association of all prenatal candidate variables with HDP and GDM was tested with uni- and multi-variable logistic regression analyses. Prediction models were built with generalized linear models using the logit link function and classification and regression tree approach (XGboost) machine learning (ML) algorithm. Performance was assessed with repeated 2-fold cross-validation and performance metrics we considered were area under the curve (AUC). P value <0.001 was considered statistically significant.
RESULTS: A total of 707,198 twin pregnancies were included in the HDP analysis and 723,882 twin pregnancies for the GDM analysis. The incidence of HDP and GDM significantly increased from 12.2% in 2016 to 15.4% in 2021 and from 8.1% in 2016 to 10.7% in 2021, respectively. Factors that increase the risk of HDP in twin gestations are maternal age <20, age≥35, infertility, prepregnancy DM, non-Hispanic Black population, obesity, and those with Medicaid insurance (p<0.001). Factors that more than doubled the risk are obesity class II and III (p<0.001). Factors that increase the risk of GDM in twin gestations are age <25, age≥30, history of infertility, prepregnancy hypertension, non-Hispanic Asian population, non-US nativity, and obesity (p<0.001). Factors that more than doubled the risk are maternal age ≥ 30 years, non-Hispanic Asian, and class I, II, and III maternal obesity ( p<0.001). For both HDP and GDM, the performance of the ML and logistic regression model was mostly similar with negligible difference in terms of all tested performance domains. The AUC of the final ML model for HDP and GDM were 0.62±0.004, and 0.67±0.004, respectively.
CONCLUSIONS: The incidence of HDP and GDM in twin gestations is increasing. The predictive accuracy of the machine learning model for both HDP and GDM in twin gestations is similar to that of the logistic regression model. Both models had modest performance, well-calibrated, and neither had a poor fit. This article is protected by copyright. All rights reserved.
摘要：
目的：利用产前护理入门级特征，建立双胎妊娠中妊娠期高血压疾病（HDP）和妊娠期糖尿病（GDM）的预测模型。
方法：使用2016年至2021年美国国家活产数据进行横断面研究。所有产前候选变量与HDP和GDM的关联通过单变量和多变量逻辑回归分析进行检验。使用logit链接函数以及分类和回归树方法（XGboost）机器学习（ML）算法，使用广义线性模型建立预测模型。通过重复的2倍交叉验证来评估性能,并且我们考虑的性能指标是曲线下面积(AUC)。P值<0.001被认为是统计学上显著的。
结果：HDP分析中包括了707,198例双胎妊娠，GDM分析中包括了723,882例双胎妊娠。HDP和GDM的发病率分别从2016年的12.2%大幅上升至2021年的15.4%，从2016年的8.1%上升至2021年的10.7%。增加双胎妊娠HDP风险的因素是产妇年龄<20岁，年龄≥35岁，不孕症，孕前DM,非西班牙裔黑人,肥胖，以及那些有医疗补助保险的人(p<0.001)。使风险增加一倍以上的因素是II级和III级肥胖（p<0.001）。增加双胎妊娠GDM风险的因素是年龄<25岁，年龄≥30岁，不孕史，孕前高血压,非西班牙裔亚洲人口，非美国诞生，和肥胖（p<0.001）。风险增加一倍以上的因素是产妇年龄≥30岁，非西班牙裔亚洲人，还有I班,II,和III母亲肥胖（p<0.001）。对于HDP和GDM，ML和逻辑回归模型的性能大多相似,但在所有测试性能领域的差异可忽略不计.HDP和GDM的最终ML模型的AUC分别为0.62±0.004和0.67±0.004。
结论：双胎妊娠中HDP和GDM的发病率呈上升趋势。双胎妊娠中HDP和GDM的机器学习模型的预测准确性与逻辑回归模型的预测准确性相似。两种型号的性能都不高，校准良好,两者都不适合。本文受版权保护。保留所有权利。
方法：使用2016年至2021年美国国家活产数据进行横断面研究。所有产前候选变量与HDP和GDM的关联通过单变量和多变量逻辑回归分析进行检验。使用logit链接函数以及分类和回归树方法（XGboost）机器学习（ML）算法，使用广义线性模型建立预测模型。通过重复的2倍交叉验证来评估性能,并且我们考虑的性能指标是曲线下面积(AUC)。P值<0.001被认为是统计学上显著的。
结果：HDP分析中包括了707,198例双胎妊娠，GDM分析中包括了723,882例双胎妊娠。HDP和GDM的发病率分别从2016年的12.2%大幅上升至2021年的15.4%，从2016年的8.1%上升至2021年的10.7%。增加双胎妊娠HDP风险的因素是产妇年龄<20岁，年龄≥35岁，不孕症，孕前DM,非西班牙裔黑人,肥胖，以及那些有医疗补助保险的人(p<0.001)。使风险增加一倍以上的因素是II级和III级肥胖（p<0.001）。增加双胎妊娠GDM风险的因素是年龄<25岁，年龄≥30岁，不孕史，孕前高血压,非西班牙裔亚洲人口，非美国诞生，和肥胖（p<0.001）。风险增加一倍以上的因素是产妇年龄≥30岁，非西班牙裔亚洲人，还有I班,II,和III母亲肥胖（p<0.001）。对于HDP和GDM，ML和逻辑回归模型的性能大多相似,但在所有测试性能领域的差异可忽略不计.HDP和GDM的最终ML模型的AUC分别为0.62±0.004和0.67±0.004。
结论：双胎妊娠中HDP和GDM的发病率呈上升趋势。双胎妊娠中HDP和GDM的机器学习模型的预测准确性与逻辑回归模型的预测准确性相似。两种型号的性能都不高，校准良好,两者都不适合。本文受版权保护。保留所有权利。
