Treatment patterns for patients with treatment resistant depression in France using nationwide claims database
Vimont, A.; Biscond, M.; Leleu, H.; Llorca, P.-M.
Show abstract
BackgroundPrevalence of Treatment-Resistant Depression (TRD) varied widely across studies due to heterogeneous definitions. Several treatment strategies exist to manage patients with TRD but evidence from real-world data is scarce. Investigating their use in real-life settings is important to understand national prescribing practices and to refine prevalence estimation. MethodAll adult patients ([≥] 18 years) with a TRD episode for the year 2019 were identified in a sample of four French regions accounting for 27% of national individuals. After exclusion of patients with psychotic or bipolar disorders, Parkinsons disease, and dementia, TRD was defined by i/ 3 successive sequences of different antidepressants (AD), or ii/ the dispensing of several different AD together, or iii/ an AD with a potentiator (lithium, antiepileptic drugs, or antipsychotic drugs) over the same treatment period. The prevalence rate was estimated for the year 2019 and treatment patterns were described by treatment class and molecule. ResultsFor the year 2019, 66,810 patients were identified with TRD, accounting for 23.9% of all patients treated for depression. The mean age was 56 years ({+/-}15.9) with 63.7% of women. Standardized prevalence of TRD was estimated at 35.1 per 10 000 patients, and 25.8 per 10,000 patients when excluding patients probably treated for another primary diagnosis than depression. Association of an AD with an antipsychotic was the most frequently used strategy, with SSRIs and second-generation antipsychotics being the most often prescribed. ConclusionThis study provides robust population-based estimates of the prevalence of TRD in the French population. Description of treatment patterns highlight the widespread use of second-generation antipsychotics as potentiator of antidepressants.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Subjective mental health and need for care among psychiatric outpatients during the COVID-19 pandemic: results from an outreach initiative in Sweden 93%
- The Relationship between Cannabis Use and Cognition in People with Bipolar Disorder: A Systematic Scoping Review 92%
- The recurrence of illness (ROI) index is a key factor in major depression that indicates increasing immune-linked neurotoxicity and vulnerability to suicidal behaviors. 92%
Similar papers in this journal
- Oral Ketamine for the Treatment of Depression: A randomized controlled trial and meta-analysis 94%
- Multidimensional apathy: A simple and inclusive clinical marker of youth mental health—A longitudinal study 93%
- Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study 92%
Similar papers in this journal
- The impact of the COVID-19 pandemic on Antidepressant Prescribing with a focus on people with learning disability and autism: An interrupted time-series analysis in England using OpenSAFELY-TPP 93%
- Co-prescription of Metformin and Antipsychotics in Severe Mental Illness: A UK Primary Care Cohort Study 92%
- The development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies 90%
Similar papers in this journal
- Patients with affective disorders profit most from telemedical treatment: Evidence from a naturalistic patient cohort during the COVID-19 pandemic 94%
- Psychedelic mushrooms in the USA: Knowledge, patterns of use and association with health outcomes 92%
- Antipsychotics Lower Peripheral Markers of Inflammation in Drug-naive Early Psychosis: A Pilot Study 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.