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

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.