Ziama Arkin Infancy Institute, Interdisciplinary Center (IDC) Herzliya, Hanadiv 71, 1st floor, Herzliya 46485, Israel; Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC) Herzliya, HaUniversity 8, Herzliya 4610101, Israel.
Be'er Ya'akov Medical Center, Israel; Tel Aviv University, Sackler School of Medicine, Tel Aviv, Israel.
J Affect Disord. 2022 Jan 1;296:136-149. doi: 10.1016/j.jad.2021.09.014. Epub 2021 Sep 22.
Recent literature identifies childbirth as a potentially traumatic event, following which mothers may develop symptoms of Post-Traumatic-Stress-Following-Childbirth (PTS-FC). Especially when persistent, PTS-FC may interfere with mothers' caregiving and associated infant development, underscoring the need for accurate predictive screening of risk. Drawing on recent developments in advanced statistical modeling, the aim of the current study was to identify a set of prenatal indicators and prediction rules that may accurately identify pregnant women's risk for developing symptoms of PTS-FC which persist throughout the early postpartum period.
182 women from the general population completed a comprehensive set of approximately 200 potentially predictive questions during pregnancy, and subsequently reported on their acute stress and PTS-FC at three days, one month, and three months postpartum (self-report and clinician-administered interview). Based on the postpartum acute stress and PTS-FC data, women were classified into profiles of "Stable-High-PTS-FC" and "Stable-Low-PTS-FC" by means of Latent-Class Analyses. Prenatal data were modeled to identify women at risk for "Stable-High PTS-FC".
Employing machine-learning decision-tree analyses, a total of 36 questions and 7 prediction-rules were selected. Based on a cost-rate of 15 versus 100 for false-negative "Stable-Low-PTS-FC" versus false-negative "Stable-High-PTS-FC", the final model showed 80.6% accuracy for "Stable-High-PTS-FC" prediction.
This study identifies a short set of questions and prediction rules that may be included in future large-scale validation studies aimed at developing and validating a brief PTS-FC screening instrument that could be implemented in general population prenatal healthcare practice. Accurate screening would allow for selective administering of preventive interventions towards women at risk.
最近的文献将分娩确定为一种潜在的创伤性事件，之后母亲可能会出现产后创伤后应激障碍（PTS-FC）的症状。特别是当这些症状持续存在时，可能会干扰母亲的育儿和相关婴儿发育，这凸显了准确预测风险进行筛查的必要性。本研究借鉴了高级统计建模的最新进展，旨在确定一组产前指标和预测规则，这些指标和规则可能会准确识别出在整个产后早期持续出现 PTS-FC 症状的孕妇的风险。
182 名来自普通人群的女性在怀孕期间完成了大约 200 个潜在预测问题的综合评估，随后在产后三天、一个月和三个月报告了她们的急性应激和 PTS-FC（自我报告和临床医生进行的访谈）。基于产后急性应激和 PTS-FC 数据，通过潜在类别分析，将女性分为“稳定高 PTS-FC”和“稳定低 PTS-FC”两种类型。对产前数据进行建模，以确定有发生“稳定高 PTS-FC”风险的女性。
采用机器学习决策树分析，共选择了 36 个问题和 7 个预测规则。基于假阴性“稳定低 PTS-FC”与假阴性“稳定高 PTS-FC”的成本率为 15 比 100，最终模型对“稳定高 PTS-FC”的预测准确率为 80.6%。
本研究确定了一组简短的问题和预测规则，这些问题和规则可能会包含在未来的大规模验证研究中，旨在开发和验证一种简短的 PTS-FC 筛查工具，该工具可以在普通人群的产前保健实践中实施。准确的筛查可以使处于风险中的女性有针对性地接受预防性干预。
