European psychiatry : the journal of the Association of European Psychiatrists · 2026
Predictors of conversion from major depressive disorder to bipolar disorder: A population-based PADRIS-PRESTO cohort study in Catalonia.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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González-Campos Meritxell, Anmella Gerard, De Prisco Michele, Oliva Vincenzo, Valenzuela-Pascual Clàudia, Mas Ariadna, Korniyenko Marta, Voltá I Solerdelcoll Ruth, Escala Clara, Fico Giovanna, Valentí Marc, Blanch Jordi, Young Allan H, Vieta Eduard, Hidalgo-Mazzei Diego
Аннотация
BACKGROUND: Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD), with diagnostic delays averaging 6-10 years leading to suboptimal treatment and poorer outcomes. Early identification of patients at risk for diagnostic conversion remains a critical clinical priority. We aimed to identify clinical predictors of BD conversion in a large population-based cohort and develop a clinically applicable prediction model.
METHODS: We conducted a retrospective cohort study using electronic health records from the Catalan PADRIS-PRESTO database (2010-2019). Patients aged ≥18 years with incident MDD diagnosed in specialized mental health services (2015-2019) were followed until BD diagnosis, death, or study end. Time-varying Cox regression addressed proportional hazards violations, with competing risks analysis accounting for mortality.
RESULTS: Among 52,001 patients (median follow-up 4.0 years), 3,958 (7.61%) converted to BD. Psychotic features emerged as the strongest predictor (HR = 3.18, 95% CI: 2.92-3.45, E-value = 5.82), with consistent effects across follow-up. Anxiety disorders showed time-varying protection (baseline HR = 0.41, 95% CI: 0.35-0.48) that attenuated over time (interaction HR = 1.30 per period). The time-varying model achieved good discrimination (C-index = 0.745). Machine learning validation confirmed predictor importance rankings (Spearman r = 0.87).
CONCLUSIONS: Psychotic features during depressive episodes robustly predict BD conversion, warranting enhanced clinical monitoring. Our time-varying prediction model using routinely collected clinical data enables early risk stratification to guide diagnostic vigilance and treatment decisions.
METHODS: We conducted a retrospective cohort study using electronic health records from the Catalan PADRIS-PRESTO database (2010-2019). Patients aged ≥18 years with incident MDD diagnosed in specialized mental health services (2015-2019) were followed until BD diagnosis, death, or study end. Time-varying Cox regression addressed proportional hazards violations, with competing risks analysis accounting for mortality.
RESULTS: Among 52,001 patients (median follow-up 4.0 years), 3,958 (7.61%) converted to BD. Psychotic features emerged as the strongest predictor (HR = 3.18, 95% CI: 2.92-3.45, E-value = 5.82), with consistent effects across follow-up. Anxiety disorders showed time-varying protection (baseline HR = 0.41, 95% CI: 0.35-0.48) that attenuated over time (interaction HR = 1.30 per period). The time-varying model achieved good discrimination (C-index = 0.745). Machine learning validation confirmed predictor importance rankings (Spearman r = 0.87).
CONCLUSIONS: Psychotic features during depressive episodes robustly predict BD conversion, warranting enhanced clinical monitoring. Our time-varying prediction model using routinely collected clinical data enables early risk stratification to guide diagnostic vigilance and treatment decisions.