Table_4_How Parental Predictors Jointly Affect the Risk of Offspring Congenital Heart Disease: A Nationwide Multicenter Study Based on the China Birth Cohort.docx

ObjectiveCongenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD.
MethodsThis was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD.
ResultsDominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10–1.19], increased paternal age (1.05, 95% CI: 1.02–1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22–3.76), paternal drinking (1.41, 95% CI: 1.08–1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95–5.87), maternal fever (3.35, 95% CI: 2.49–4.50), assisted reproductive technology (2.89, 95% CI: 2.13–3.94), and environmental pollution (1.61, 95% CI: 1.18–2.20). A higher household annual income (100,000–400,000 CNY: 0.47, 95% CI: 0.34–0.63; > 400,000 CNY: 0.23, 95% CI: 0.15–0.36), higher maternal education level (13–16 years: 0.68, 95% CI: 0.50–0.93; ≥ 17 years: 0.87, 95% CI: 0.55–1.37), maternal folic acid (0.21, 95% CI: 0.16–0.27), and multivitamin supplementation (0.33, 95% CI: 0.26–0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD.
ConclusionWe revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.

**研究目的**：先天性心脏病（Congenital Heart Disease, CHD）病因复杂，其遗传致病机制已得到较为充分的研究，但针对非遗传危险因素的相关探究仍相对匮乏。本研究旨在筛选主要的父母相关预测因子，并构建子代CHD发病风险的预测模型与列线图（nomogram）。
**研究方法**：本研究为2017年11月至2021年12月开展的回顾性队列研究，共纳入44578名研究对象。其中，来自华东地区4家医院的研究对象被划分为建模队列，来自华中及西部地区5家医院的研究对象则作为外部验证队列。研究从人口统计学特征、生活方式行为、环境污染暴露、孕产妇病史及本次妊娠相关信息中，通过单因素与多因素分析筛选出CHD的主要预测因子。采用多因素logistic回归分析，基于筛选得到的预测因子构建预测模型与列线图。分别通过内部与外部验证对该预测模型及列线图的性能进行评估。此外，本研究还开发了一款基于网页的列线图工具，用于预测个体子代CHD的发病概率。
**研究结果**：子代CHD的主要危险因素包括：产妇年龄增加[比值比（odds ratio, OR）=1.14，95%置信区间（confidence interval, CI）=1.10~1.19]、父亲年龄增加（OR=1.05，95%CI=1.02~1.09）、产妇二手烟暴露（OR=2.89，95%CI=2.22~3.76）、父亲饮酒（OR=1.41，95%CI=1.08~1.84）、产妇孕前糖尿病（OR=3.39，95%CI=1.95~5.87）、产妇发热（OR=3.35，95%CI=2.49~4.50）、辅助生殖技术(assisted reproductive technology)（OR=2.89，95%CI=2.13~3.94）及环境污染暴露（OR=1.61，95%CI=1.18~2.20）。而保护性因素包括：较高的家庭年收入（10万~40万元人民币：OR=0.47，95%CI=0.34~0.63；＞40万元人民币：OR=0.23，95%CI=0.15~0.36）、较高的产妇受教育程度（受教育年限13~16年：OR=0.68，95%CI=0.50~0.93；≥17年：OR=0.87，95%CI=0.55~1.37）、产妇孕前补充叶酸（OR=0.21，95%CI=0.16~0.27）及多种维生素（OR=0.33，95%CI=0.26~0.42）。该列线图在内部验证及外部验证中均表现出良好的区分度：内部验证的受试者工作特征曲线（receiver-operating characteristic curve, ROC）下面积（area under the curve, AUC）为0.843，建模队列外部验证AUC为0.849，外部验证队列AUC为0.837。校准曲线显示，列线图预测的CHD发病概率与实际发病情况具有良好的一致性。
**研究结论**：本研究明确了主要的父母相关预测因子，并开发了一款基于网页的子代CHD发病风险列线图工具，该工具可作为量化个体子代CHD发病风险、快速识别高危人群的有效手段。