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. 2025 Jun 23:1-8.
doi: 10.1192/bjp.2025.107. Online ahead of print.

Characterisation of serious mental illness trajectories through transdiagnostic clinical features

Juan F De la Hoz  1 , Alejandro Arias  2 , Susan K Service  1 , Mauricio Castaño  2 , Ana M Díaz-Zuluaga  1 , Janet Song  1 , Cristian Gallego  2 , Sergio Ruiz-Sánchez  3 , Javier I Escobar  4 , Alex A T Bui  5 , Carrie E Bearden  1 , Victor Reus  6 , Carlos López-Jaramillo  3 , Nelson B Freimer  1 , Loes M Olde Loohuis  1
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Characterisation of serious mental illness trajectories through transdiagnostic clinical features

Juan F De la Hoz et al. Br J Psychiatry. .

Abstract

Background: Electronic health records (EHRs), increasingly available in low- and middle-income countries (LMICs), provide an opportunity to study transdiagnostic features of serious mental illness (SMI) and its trajectories.

Aims: Characterise transdiagnostic features and diagnostic trajectories of SMI using an EHR database in an LMIC institution.

Method: We conducted a retrospective cohort study using EHRs from 2005-2022 at Clínica San Juan de Dios Manizales, a specialised mental health facility in Colombia, including 22 447 patients with schizophrenia (SCZ), bipolar disorder (BPD) or severe/recurrent major depressive disorder (MDD). Using diagnostic codes and clinical notes, we analysed the frequency of suicidality and psychosis across diagnoses, patterns of diagnostic switching and the accumulation of comorbidities. Mixed-effect logistic regression was used to identify factors influencing diagnostic stability.

Results: High frequencies of suicidality and psychosis were observed across diagnoses of SCZ, BPD and MDD. Most patients (64%) received multiple diagnoses over time, including switches between primary SMI diagnoses (19%), diagnostic comorbidities (30%) or both (15%). Predictors of diagnostic switching included mentions of delusions (odds ratio = 1.47, 95% CI 1.34-1.61), prior diagnostic switching (odds ratio = 4.01, 95% CI 3.7-4.34) and time in treatment, independent of age (log of visit number; odds ratio = 0.57, 95% CI 0.54-0.61). Over 80% of patients reached diagnostic stability within 6 years of their first record.

Conclusions: Integrating structured and unstructured EHR data reveals transdiagnostic patterns in SMI and predictors of disease trajectories, highlighting the potential of EHR-based tools for research and precision psychiatry in LMICs.

Keywords: Electronic health records (EHRs); diagnostic trajectories; low-middle income countries (LMIC); natural language processing (NLP); transdiagnostic symptoms.

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Conflict of interest statement

Declaration of Interest – Dr. Nelson Freimer and Dr. Alex Bui receive research funding from Apple Inc. No other authors report financial relationships with commercial interests.

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