Citation

Haidl TK, Gruen M, Dizinger J, Rosen M, Doll CM, Penzel N, Daum L, Große Hokamp N, Klosterkötter J, Ruhrmann S, Vogeley K, Schultze-Lutter F, Kambeitz J. J. Psychiatr. Res. 2021; 138: 591-597.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.jpsychires.2021.04.040

PMID

unavailable

Abstract

BACKGROUND: Childhood adversities and trauma (CAT) are associated with poor functional outcome. However, the influence of the single CAT aspects on the risk of a poor functional outcome within different mental disorders has not been investigated so far. Our aims were (i) to predict individual functional outcome based on CAT (ii) to examine whether the prediction power differs within different diagnostic groups (clinical high-risk for psychosis (CHR), psychosis, affective disorders, anxiety disorders) (iii) to compare the specific patterns of CAT experiences, influencing functional outcomes in these groups.

METHOD: Clinical data of 707 patients (mean age: 25.09 years (SD = 5.6), 65.5% male) of the Cologne Early Recognition and Intervention Center were assessed with the Trauma And Distress Scale. Functional outcome was estimated by the Social and Occupational Functioning Assessment Scale and school educational attainment. Using machine learning, we generated individualized models to predict functional outcome and to identify specific CAT patterns.

RESULTS: Across the entire sample, the best prediction for the functional outcome achieved a balanced accuracy (BAC) of 0.6. After splitting into the single diagnostic groups, an improvement with best results in the psychosis group (BAC = 0.70) was observed. Considering specific CAT patterns, the most predictive items depicted a positive and caring environment - or the absence of these, a positive self-image and experiences of bullying.

CONCLUSIONS: Our results indicated that CAT was differentially associated with functional outcome in the various mental disorders. Thus, the importance of mediating variables, that might explain the interindividual differences in the vulnerability to CAT, like resilience factors, appeared to be crucial.

Language: en

Keywords

Machine learning; Distress; Abuse; Childhood maltreatment; Functioning; Neglect