Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross‐validation machine learning approach. Issue 2 (8th December 2019)

• Record Type: Journal Article
• Authors: Schwarzmeier, Hanna; Leehr, Elisabeth Johanna; Böhnlein, Joscha; Seeger, Fabian Reinhard; Roesmann, Kati; Gathmann, Bettina; Herrmann, Martin J.; Siminski, Niklas; Junghöfer, Markus; Straube, Thomas; Grotegerd, Dominik; Dannlowski, Udo
• Is Part Of: International journal of methods in psychiatric research. Volume 29:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-08
• Subjects: machine learning -- spider phobia -- theranostic markers; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1812
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: Objectives: Embedded in the Collaborative Research Center "Fear, Anxiety, Anxiety Disorders" (CRC‐TRR58), this bicentric clinical study aims at identifying biobehavioral markers of treatment (non‐)response by applying machine learning methodology with an external cross‐validation protocol. We hypothesize that a priori prediction of treatment (non‐)response is possible in a second, independent sample based on multimodal markers. Methods: One‐session virtual reality exposure treatment (VRET) with patients with spider phobia was conducted on two sites. Clinical, neuroimaging, and genetic data were assessed at baseline, post‐treatment and after 6 months. The primary and secondary outcomes defining treatment response are as follows: 30% reduction regarding the individual score in the Spider Phobia Questionnaire and 50% reduction regarding the individual distance in the behavioral avoidance test. Results: N = 204 patients have been included ( n = 100 in Würzburg, n = 104 in Münster). Sample characteristics for both sites are comparable. Discussion: This study will offer cross‐validated theranostic markers for predicting the individual success of exposure‐based therapy. Findings will support clinical decision‐making on personalized therapy, bridge the gap between basic and clinical research, and bring stratified therapy into reach. The study is registered at ClinicalTrials.gov (ID: NCT03208400).