Haßdenteufel Kathrin, Lingenfelder Katrin, Schwarze Cornelia E, Feisst Manuel, Brusniak Katharina, Matthies Lina Maria, Goetz Maren, Wallwiener Markus, Wallwiener Stephanie
Department of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.
Department of Psychology, Heidelberg University, Heidelberg, Germany.
JMIR Ment Health. 2021 Dec 10;8(12):e26665. doi: 10.2196/26665.
Postpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage.
The aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm.
In total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy.
Digitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum.
The best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women's personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term.
产后抑郁症（PPD）是一种严重的精神障碍，常常导致母婴依恋关系不佳，并对婴儿发育产生负面影响。最近建议进行普遍筛查以识别有风险的女性，但迄今为止尚未确定孕期的最佳筛查时间。因此，在育龄妇女中广泛使用的基于网络的技术为早期检测有不良心理健康结局高风险的妊娠创造了新机会。
本研究的目的是通过使用收集电子患者报告结局（ePROs）的基于网络的筛查工具作为筛查算法的基础，对产后抑郁症的风险进行分层，并确定孕期的最佳筛查时间。
总共214名女性在孕期接受了5次、产后接受了3次爱丁堡产后抑郁量表（EPDS）测试，该量表可在本研究作者开发的基于网络的妊娠平台上获取。对于每次产前评估，计算产后抑郁症的曲线下面积（AUC）、敏感性、特异性和预测值。应用多变量逻辑回归分析来识别其他潜在预测因素，如年龄、教育程度、产次、关系质量和焦虑，以提高预测准确性。
分析了214名孕妇的数字收集数据。产后3个月和6个月时抑郁症状的预测准确性在孕中期（AUC = 0.85）和孕晚期（AUC = 0.75）筛查方面为合理至良好。多变量逻辑回归分析在产后3个月时得到了出色的AUC为0.93，产后6个月时得到了良好的AUC为0.87。
已表明在妊娠第24至28周（GW）之间进行筛查对产后抑郁症具有最佳预测准确性，并且似乎有利于识别有风险的女性。结合上述预测因素，鉴别能力有所提高，尤其是在产后3个月时。孕期抑郁症筛查与女性个人风险概况相结合，可作为开发数字筛查算法的起点。因此，基于网络的评估工具构成了可行、高效且具有成本效益的方法。因此，它们似乎有利于检测高风险妊娠，以便从长远来看改善母婴出生结局。
