Characterisation of serious mental illness trajectories through transdiagnostic clinical features

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.

Figure 1.

Transdiagnostic characterization and co-occurrence of clinical features extracted from EHR notes. A) The proportion of patients with each of the four features is stratified by primary diagnosis. B) Number of patients with co-occurrence of 2, 3, or 4 clinical features. All data in these plots are limited to patients with at least two EHR notes.

Figure 2:

Disease trajectories of SMI in patients with at least three visits. A) UpSet plot presenting diagnostic switches (between SMI categories) and comorbidities (SMI and non-SMI categories). Patients with a single SMI diagnosis (blue, green, red, total n=4,620); a single SMI diagnosis and other comorbidities (orange n=3,955); multiple SMI diagnoses and no other comorbidities (teal n=2,468); multiple SMI diagnoses and other comorbidities (purple, n=1,919). Bars with n<100 are not shown. B) Sankey diagram of diagnostic trajectories. The left nodes represent the diagnosis given at the initial visit, and the right nodes represent the most recent SMI code. (Diagnostic switches within SMI are shown in Supplementary Figure 4). ORG: Other mental disorders due to brain damage and dysfunction and to physical disease (F06), SUD: Mental and behavioral disorders due to multiple drug use and use of other psychoactive substances (F19), BPE: Acute and transient psychotic disorders (F23), MDE: Major Depressive Episode (F32), PMD: Persistent mood disorders (F34), UMD: Unspecified mood disorder (F39), ANX: Other anxiety disorders (F41), PTSD: Reaction to severe stress, and adjustment disorders (F43), ADHD: Hyperkinetic disorders (F90), CON: Conduct disorders (F91)

Figure 3:. Diagnostic stability over time.

A) At each visit k, the proportion of patients that will switch primary diagnosis code on their next visit k+1. Stratified by age groups: age at 1^st visit before and after 30 years. B) The x-axis shows the time since the first encounter instead of the visit number. For every year, the observed proportion of visits that will have a diagnostic switch on the next visit. The solid line is the average probability of switching at any given visit during that year, as estimated by the model. Lines and shaded areas correspond to 95% confidence intervals. C) Proportion of patients by year who have reached a stable diagnosis. N=1,952 patients with 10 or more years in the EHR. It takes 6 years for 80% of patients to reach a stable diagnosis.