Literature DB >> 33770539

Pharmacological treatment profiles in the FACE-BD cohort: An unsupervised machine learning study, applied to a nationwide bipolar cohort.

Sébastien Brodeur1, Hugo Terrisse2, Arnaud Pouchon1, Ophelia Godin3, Bruno Aouizerate4, Valerie Aubin5, Frank Bellivier6, Raoul Belzeaux7, Thierry Bougerol1, Philippe Courtet8, Caroline Dubertret9, Sebastien Gard4, Emmanuel Haffen10, Chantal Henry11, Marion Leboyer3, Emilie Olié8, Paul Roux12, Ludovic Samalin13, Raymund Schwan14, Bruno Etain6, Jean-Luc Bosson2, Mircea Polosan15.   

Abstract

BACKGROUND: Despite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles.
METHODS: This multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters.
RESULTS: Four groups were identified among the 1795 included patients: group 1 ("heterogeneous" n = 1099), group 2 ("lithium" n = 265), group 3 ("valproate" n = 268), and group 4 ("lamotrigine" n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales. LIMITATIONS: The one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder.
CONCLUSIONS: Treatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bipolar disorder; Functioning; Maintenance treatment; Mood stabilizers; Unsupervised machine learning

Year:  2021        PMID: 33770539     DOI: 10.1016/j.jad.2021.02.036

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  2 in total

1.  Preventive Medication Patterns in Bipolar Disorder and Their Relationship With Comorbid Substance Use Disorders in a Cross-National Observational Study.

Authors:  Romain Icick; Ingrid Melle; Bruno Etain; Margrethe Collier Høegh; Sébastien Gard; Sofie R Aminoff; Marion Leboyer; Ole A Andreassen; Raoul Belzeaux; Chantal Henry; Thomas D Bjella; Jean-Pierre Kahn; Nils Eiel Steen; Frank Bellivier; Trine Vik Lagerberg
Journal:  Front Psychiatry       Date:  2022-05-03       Impact factor: 5.435

2.  Pharmacological treatment profiles in the FACE-BD cohort: Treatment description and complete data for bipolar subtypes.

Authors:  Sébastien Brodeur; Hugo Terrisse; Arnaud Pouchon; Ophelia Godin; Bruno Aouizerate; Valerie Aubin; Frank Bellivier; Raoul Belzeaux; Thierry Bougerol; Philippe Courtet; Caroline Dubertret; Sebastien Gard; Emmanuel Haffen; Chantal Henry; Marion Leboyer; Emilie Olié; Paul Roux; Ludovic Samalin; Raymund Schwan; Bruno Etain; Jean-Luc Bosson; Mircea Polosan
Journal:  Data Brief       Date:  2021-03-25
  2 in total

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