Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study
Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study
复制标题
DOI：
10.1016/j.jad.2019.10.034
复制
发表时间：
2020-02-01
影响因子：
6.6
通讯作者：
Danese, Andrea
中科院分区：
文献类型：
--
作者：
Meehan, Alan J.;Latham, Rachel M.;Danese, Andrea
背景：受害儿童比非受害同龄人有更大的精神病理学风险。然而，并非所有受害儿童都会发展成精神疾病，准确识别哪些受害儿童的精神病理学风险最大，对于提供有针对性的干预措施至关重要。本研究旨在开发和内部验证个性化的风险预测模型的psychopathology among victimized children.Methods：参与者是成员的环境风险（E-风险）纵向双胞胎研究，具有全国代表性的英国出生队列的2,232双胞胎出生于1994-1995年。受害暴露是在5至12岁之间前瞻性测量的，同时还有一系列个人、家庭和社区层面的精神病理学预测因素。在18岁评估时进行了结构化的精神病学访谈。Logistic回归模型使用最小绝对收缩和选择算子（LASSO）正则化进行估计，以避免与当前样本过度拟合，并使用10倍嵌套交叉验证进行内部验证。26.5%（n = 591）的E风险参与者曾暴露于至少一种形式的严重儿童受害，60.4%（n = 334）的受害儿童在18岁时符合任何精神障碍的诊断标准。任何精神障碍，内化障碍和外化障碍的独立预测模型选择了简约的预测子集。三个内部验证的模型表现出足够的歧视，根据曲线下面积估计值（范围= = 0.66-0.73），和良好的calibration.Limitations：外部验证完全独立的数据是需要临床implementation.Conclusions：研究结果提供了证明的原则证据，预测模型可以是有用的，在支持识别受害儿童的最大风险的精神病理学。这有可能为有针对性的干预措施和合理的资源分配提供信息。
Background: Victimized children are at greater risk for psychopathology than non-victimized peers. However, not all victimized children develop psychiatric disorders, and accurately identifying which victimized children are at greatest risk for psychopathology is important to provide targeted interventions. This study sought to develop and internally validate individualized risk prediction models for psychopathology among victimized children.Methods: Participants were members of the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative British birth cohort of 2,232 twins born in 1994-1995. Victimization exposure was measured prospectively between ages 5 and 12 years, alongside a comprehensive range of individual-, family-, and community-level predictors of psychopathology. Structured psychiatric interviews took place at age-18 assessment. Logistic regression models were estimated with Least Absolute Shrinkage and Selection Operator (LASSO) regularization to avoid over-fitting to the current sample, and internally validated using 10-fold nested cross-validation.Results: 26.5% (n = 591) of E-Risk participants had been exposed to at least one form of severe childhood victimization, and 60.4% (n = 334) of victimized children met diagnostic criteria for any psychiatric disorder at age 18. Separate prediction models for any psychiatric disorder, internalizing disorders, and externalizing disorders selected parsimonious subsets of predictors. The three internally validated models showed adequate discrimination, based on area-under-the-curve estimates (range = = 0.66-0.73), and good calibration.Limitations: External validation in wholly-independent data is needed before clinical implementation.Conclusions: Findings offer proof-of-principle evidence that prediction modeling can be useful in supporting identification of victimized children at greatest risk for psychopathology. This has the potential to inform targeted interventions and rational resource allocation.