Characterisation of serious mental illness trajectories through transdiagnostic clinical features

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported for primary outcomes

• Effect Size: 64% of patients received multiple diagnoses over time, with 19% switching between primary SMI diagnoses

• Primary Outcome: Diagnostic switching patterns and time to diagnostic stability in serious mental illness

• Nnt Or Sensitivity: Over 80% of patients reached diagnostic stability within 6 years of first clinical contact

• Confidence Interval: Delusions OR 1.47 (95% CI 1.34-1.61), Prior switching OR 4.01 (95% CI 3.7-4.34)

Clinical Application

Requires comprehensive EHR systems with structured diagnostic coding and clinical notes - feasible in well-resourced mental health facilities Relevant to Australian mental health services using comprehensive EHR systems. Could inform RACGP guidelines for SMI management and support arguments for sustained mental health funding through PBS mental health items Patients with serious mental illness in specialised mental health services, particularly in LMIC settings

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.

References

1. 1.De la Hoz, J. F., Arias, A., Service, S. K., Castaño, M., Díaz-Zuluaga, A. M., Song, J., Gallego, C., Ruiz-Sánchez, S., Escobar, J. I., Bui, A. A. T., Bearden, C. E., Reus, V., López-Jaramillo, C., Freimer, N. B., & Olde Loohuis, L. M. (2026). Characterisation of serious mental illness trajectories through transdiagnostic clinical features. British Journal of Psychiatry. https://doi.org/10.1111/bdi.13512