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Aufsatz zugänglich unter
URN: urn:nbn:de:hbz:385-11502
URL: http://ubt.opus.hbz-nrw.de/volltexte/2018/1150/


Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: A methodological proof-of-concept study

Lutz, Wolfgang ; Schwartz, Brian ; Hofmann, Stefan G. ; Fisher, Aaron J. ; Husen, Kristin ; Rubel, Julian

Originalveröffentlichung: (2018) Scientific Reports
pdf-Format:
Dokument 1.pdf (1.305 KB)

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SWD-Schlagwörter: Affektstörung , Angststörung , Therapieabbruch , Prognose , Netzwerkanalyse
Freie Schlagwörter (Englisch): Prognosis , psychology , risk factors
Institut: Psychologie
DDC-Sachgruppe: Psychologie
Sonstige beteiligte Institution: The publication was funded by the Open Access Fund of Universität Trier and the German Research Foundation (DFG)
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2018
Publikationsdatum: 11.06.2018
Bemerkung: DOI: https://doi.org/10.1038/s41598-018-25953-0
Kurzfassung auf Englisch: There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.

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