Department of Clinical Humanistic Care and Nursing Research Center, School of Nursing, Hebei Medical University, 361 East Zhongshan Road, Shijiazhuang 050017, Hebei Province, China.
Shijiazhuang Obstetrics and Gynecology Hospital, 206 East Zhongshan Road, Shijiazhuang 050017, Hebei Province, China.
J Affect Disord. 2023 Jul 15;333:107-120. doi: 10.1016/j.jad.2023.04.026. Epub 2023 Apr 19.
Clinical prediction models have been widely used to screen and diagnose postpartum depression (PPD). This study systematically reviews and evaluates the risk of bias and the applicability of PPD prediction models.
A systematic search was performed in eight databases from inception to June 1, 2022. The literature was independently screened, and data were extracted by two investigators using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS). The risk of bias and applicability was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
After the screening, 12 studies of PPD risk prediction models were included, with the area under the ROC curve of the models ranging from 0.611 to 0.937. The most-reported predictors of PPD included several aspects, including prenatal mood disorders, endocrine and hormonal influences, psychosocial aspects, the influence of family factors, and somatic illness factors. The applicability of all studies was good. However, there was some bias, mainly due to inadequate outcome events, missing data not appropriately handled, lack of model performance assessment, and overfitting of the models.
This systematic review and evaluation indicate that most present PPD prediction models have a high risk of bias during development and validation. Despite some models' predictive solid performance, the models' clinical practice rate is low. Therefore, future research should develop predictive models with excellent performance in all aspects and clinical applicability to better inform maternal medical decisions.
临床预测模型已被广泛用于产后抑郁症（PPD）的筛查和诊断。本研究系统地评价和评估了 PPD 预测模型的偏倚风险和适用性。
从建库到 2022 年 6 月 1 日，我们在 8 个数据库中进行了系统检索。两名研究者使用预测模型研究的批判性评价和数据提取清单（CHARMS）独立筛选文献，并提取数据。使用预测模型风险偏倚评估工具（PROBAST）评估偏倚风险和适用性。
筛选后纳入 12 项 PPD 风险预测模型研究，模型的 ROC 曲线下面积范围为 0.611 至 0.937。PPD 最常报道的预测因子包括产前情绪障碍、内分泌和激素影响、心理社会方面、家庭因素的影响和躯体疾病因素。所有研究的适用性均良好。但是，存在一些偏倚，主要是由于结局事件不足、未适当处理缺失数据、缺乏模型性能评估以及模型过度拟合。
本系统评价和评估表明，目前大多数 PPD 预测模型在开发和验证过程中存在较高的偏倚风险。尽管一些模型具有良好的预测性能，但模型的临床应用率较低。因此，未来的研究应该开发在各个方面和临床适用性都具有出色性能的预测模型，以更好地为产妇医疗决策提供信息。
