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Background: Adverse childhood experiences were previously identified as relevant risk factors for the development of anxiety disorders. Furthermore, anxiety disorders were shown to be associated with impairments of personality functioning. The objective of this study was to investigate adverse and protective childhood experiences as well as personality functioning, as defined by the Operationalized Psychodynamic Diagnosis system, as potential predictors for the speed of recovery during psychotherapy for patients with anxiety disorders., Methods: The sample consisted of n = 312 completed psychotherapies. The speed of recovery, defined as symptom abatement over time, was calculated using a two-stage hierarchical linear model. The effects of adverse and protective childhood experiences as well as personality functioning on the speed of recovery during psychotherapy were then examined using a structural equation model., Results: The presence of adverse childhood experiences predicted a lower speed of recovery during psychotherapy. In addition, a higher number of adverse childhood experiences was associated with greater impairments in the abilities of perception and regulation as dimensions of personality functioning. A higher number of protective childhood experiences was associated with fewer impairments in the communication and attachment dimensions. Impairments in personality functioning in patients with anxiety disorders did not predict the speed of recovery during psychotherapy., Conclusions: Among patients with anxiety disorders, adverse childhood experiences lead to a lower speed of recovery during psychotherapy. Therefore, childhood adversity should be routinely assessed before and thoroughly addressed during psychotherapy in patients with anxiety disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Nowak, Nikendei, Rollmann, Orth, Friederich and Kindermann.)

Introduction: Although outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives., Methods: For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines., Results: In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021., Discussion: We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rollmann, Gebhardt, Stahl-Toyota, Simon, Sutcliffe, Friederich and Nikendei.)