COVID-19 Modulates the Effect of Trait Anxiety on Adverse Delivery Outcomes: An Exploratory XAI-Guided Study

Previous reports suggest a link between gestational COVID-19 and adverse gestational outcomes. However, many factors other than COVID-19 impact pregnancy. Our objective was to use an explainable artificial intelligence (XAI) screening strategy to identify maternal exposures associated with both COVID-19 infection and delivery complications, and to test whether COVID-19 acts as an effect modifier of those exposures. In a cross-sectional cohort of 120 pregnant people (42 COVID, 78 CONTROL), we measured fifteen socio-demographic, obstetric and psychological variables, including State-Trait Anxiety Inventory scores. XAI Logistic models with nested cross-validation first predicted COVID-19 status, and then delivery complications, to select features. Variables highlighted by both screens were evaluated by moderation models. XAI showed that trait anxiety was associated with both pregnancy complications and COVID-19. This variable was set as the exposure in a moderation analysis where COVID-19 was a moderator and gestation complications were the outcome. COVID-19 showed no main effect on delivery complications but significantly amplified the effects of trait anxiety. These hypothesis-generating findings illustrate how XAI can uncover patterns that traditional methods might overlook. Future studies intend to include replication in larger cohorts.既往研究表明，妊娠期新冠病毒感染与不良妊娠结局存在关联。然而，除新冠病毒感染外，尚有诸多因素可对妊娠进程产生影响。本研究旨在采用可解释人工智能（Explainable Artificial Intelligence, XAI）筛查策略，识别同时与新冠病毒感染及分娩并发症相关的孕产妇暴露因素，并检验新冠病毒感染是否为上述暴露因素的效应修饰因子。 本研究纳入120名妊娠人群组成横断面队列（新冠感染组42例、对照组78例），采集了15项社会人口学、产科及心理学变量数据，其中包括状态-特质焦虑量表（State-Trait Anxiety Inventory）得分。本研究采用带有嵌套交叉验证的可解释人工智能逻辑回归模型，先对新冠病毒感染状态进行预测，再对分娩并发症进行预测，以此筛选特征变量。经两轮筛查均被筛选出的变量，将通过调节效应模型进行进一步评估。 可解释人工智能分析结果显示，特质焦虑同时与妊娠并发症及新冠病毒感染存在关联。本研究将该变量作为调节效应分析中的暴露因素，以新冠病毒感染作为调节变量，以妊娠并发症作为结局变量。分析结果显示，新冠病毒感染对分娩并发症无显著主效应，但可显著增强特质焦虑的效应量。这些可产生研究假说的研究结果，阐明了可解释人工智能如何发掘传统研究方法可能遗漏的潜在关联模式。未来研究计划在更大规模的队列中开展重复验证试验。