A novel longitudinal clustering approach to psychopathology across diagnostic entities in the hospital-based PsyCourse study

Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric i...

Full description

Saved in:
Bibliographic Details
Published in:Schizophrenia research 2022-06, Vol.244, p.29-38
Main Authors: Schulte, Eva C., Kondofersky, Ivan, Budde, Monika, Papiol, Sergi, Senner, Fanny, Schaupp, Sabrina K., Reich-Erkelenz, Daniela, Klöhn-Saghatolislam, Farahnaz, Kalman, Janos L., Gade, Katrin, Hake, Maria, Comes, Ashley L., Anderson-Schmidt, Heike, Adorjan, Kristina, Juckel, Georg, Schmauß, Max, Zimmermann, Jörg, Reimer, Jens, Wiltfang, Jens, Reininghaus, Eva Z., Anghelescu, Ion-George, Konrad, Carsten, Figge, Christian, von Hagen, Martin, Jäger, Markus, Dietrich, Detlef E., Spitzer, Carsten, Witt, Stephanie H., Forstner, Andreas J., Rietschel, Marcella, Nöthen, Markus M., Falkai, Peter, Heilbronner, Urs, Mueller, Nikola S., Schulze, Thomas G.
Format: Article
Language:English
Subjects:
Bipolar disorder
Cluster analysis
Machine learning
Psychopathology
Schizophrenia
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric information with the ultimate aim of identifying individuals with higher risk for severe illness. 397 participants of the PsyCourse study with schizophrenia- or bipolar-spectrum diagnoses were prospectively phenotyped over 18 months. Factor analysis of mixed data of different rating scales and subsequent longitudinal clustering were used to cluster disease trajectories. Five clusters of longitudinal trajectories were identified in the psychopathologic dimensions. Clusters differed significantly with regard to Global Assessment of Functioning, disease course, and—in some cases—diagnosis while there were no significant differences regarding sex, age at baseline or onset, duration of illness, or polygenic burden for schizophrenia. Longitudinal clustering may aid in identifying transdiagnostic homogeneous subgroups of individuals with severe psychiatric disease.
ISSN:0920-9964
1573-2509
DOI:10.1016/j.schres.2022.05.001