Objective: This study aimed to optimize a preeclampsia early warning model by integrating psychosocial factors (such as prenatal psychological stress and social support) with traditional clinical indicators to construct a more precise warning tool and analyze its impact on maternal disease burden.

Methods: This retrospective case-control study included maternal data from obstetric inpatients between January 2018 and December 2023 at a single institution. Controls were selected via propensity score matching (PSM) at a 1:2 ratio. A preeclampsia early warning model incorporating multiple clinical indicators, including psychosocial factors (Modified Pregnancy Stress Scale-Revised [MPSS-R] scores and social support), was constructed through multivariable logistic regression analysis, and its predictive performance was evaluated via receiver operating characteristic (ROC) curves.

Results: A total of 173 preeclampsia patients and 346 controls were included. The model achieved an AUC of 0.823 (95% CI: 0.817-0.829). MPSS-R scores were significantly correlated with disease severity (p < 0.001), and notably, 42.6% of severe preeclampsia patients had significantly elevated psychological health scores (increase ≥2 points) 2-4 weeks before diagnosis. The risk stratification results revealed that the preeclampsia incidence rate in the high-risk group was 38.5%, which was significantly higher than that in the low-risk (8.5%) and medium-risk (24.6%) groups.

Conclusion: This study successfully constructed and validated a preeclampsia early warning model that integrates psychosocial factors, significantly improving the accuracy of early identification and providing a new theoretical basis for personalized screening and intervention in high-risk pregnant women. Psychological health status and social support levels have important predictive value for preeclampsia occurrence. Future studies should further optimize this model and validate its generalizability in multicenter settings.

Keywords: AUC; Preeclampsia; disease burden; early identification; early warning model; psychosocial factors.

目标： 本研究旨在通过整合心理社会因素（如孕期的心理压力和社会支持）与传统的临床指标，优化子痫前期早期预警模型，并构建一个更精确的预警工具，分析其对母体疾病负担的影响。

方法： 这项回顾性病例对照研究包括了2018年1月至2023年12月期间在单个医疗机构产科住院产妇的数据。通过倾向评分匹配（PSM）以1:2的比例选择对照组。通过多变量逻辑回归分析构建了一个整合多种临床指标的子痫前期早期预警模型，包括心理社会因素（改良孕期压力量表-修订版[MPSS-R]分数和社会支持），并通过受试者工作特征曲线（ROC曲线）评估其预测性能。

结果： 共有173名子痫前期患者和346名对照组被纳入研究。模型的AUC值为0.823（95% CI：0.817-0.829）。MPSS-R分数与疾病严重程度显著相关（p

结论： 本研究成功构建并验证了一个整合心理社会因素的子痫前期早期预警模型，显著提高了早期识别的准确性，并为高风险孕妇提供了一种新的个性化筛查与干预理论基础。心理健康状况和社会支持水平对子痫前期的发生具有重要的预测价值。未来的研究应进一步优化该模型，并在多中心环境中验证其普适性。

关键词： AUC；子痫前期；疾病负担；早期识别；早期预警模型；心理社会因素。