Risk stratification of new-onset psychiatric disorders using clinically distinct traumatic brain injury phenotypes

Journal article

Nelofar Kureshi, Abraham Nunes, Cindy Feng, David B. Clarke, Syed Sibte Raza Abidi
Archives of Public Health, 2024

Semantic Scholar DOI PubMedCentral PubMed

Cite

APA Click to copy
Kureshi, N., Nunes, A., Feng, C., Clarke, D. B., & Abidi, S. S. R. (2024). Risk stratification of new-onset psychiatric disorders using clinically distinct traumatic brain injury phenotypes. Archives of Public Health.

Chicago/Turabian Click to copy
Kureshi, Nelofar, Abraham Nunes, Cindy Feng, David B. Clarke, and Syed Sibte Raza Abidi. “Risk Stratification of New-Onset Psychiatric Disorders Using Clinically Distinct Traumatic Brain Injury Phenotypes.” Archives of Public Health (2024).

MLA Click to copy
Kureshi, Nelofar, et al. “Risk Stratification of New-Onset Psychiatric Disorders Using Clinically Distinct Traumatic Brain Injury Phenotypes.” Archives of Public Health, 2024.

BibTeX Click to copy

@article{nelofar2024a,
  title = {Risk stratification of new-onset psychiatric disorders using clinically distinct traumatic brain injury phenotypes},
  year = {2024},
  journal = {Archives of Public Health},
  author = {Kureshi, Nelofar and Nunes, Abraham and Feng, Cindy and Clarke, David B. and Abidi, Syed Sibte Raza}
}

Abstract

Background Patients with traumatic brain injury (TBI) constitute a highly heterogeneous population, with varying risks for New-onset Psychiatric Disorders (NPDs). The objectives of this study were to identify TBI phenotypes and determine how NPDs differ among these phenotypes. Methods Hospitalized TBI patients from 2003 to 2019 were obtained from the provincial trauma registry. Propensity score matching was conducted to balance covariates among patients with TBI and controls. To uncover heterogeneity in TBI, latent class analysis (LCA)-based clustering was applied. LCA was conducted separately for two TBI cohorts: those with and without pre-injury psychiatric conditions The effect of classes on NPDs was assessed using log binomial regression models. Results A total of 3,453 patients with TBI and 13,112 controls were included in the analysis. In a conditional regression involving propensity matched patients with TBI and controls, TBI was significantly associated with the development of NPD-A (OR: 2.78; 95% CI: 2.49–3.09), as well as NPD-P (OR: 2.36; 95% CI: 2.07–2.70). Eight distinct latent classes were identified which differed in the incidence of NPDs. Four classes displayed a 53% (RR:1.53; 95% CI: 1.31–1.78), 48% (RR:1.48; 95% CI: 1.26–1.74), 28% (RR:1.28; 95% CI: 1.08–1.54), and 20% (RR: 1.20, 95%CI: 1.03–1.39), increased NPD risk. Conclusion TBI is a significant predictor of NPDs. There are clinically distinguishable phenotypes with different patterns of NPD risk among patients with TBI. Identifying individuals with respect to their phenotype may improve risk stratification of patients with TBI and promote early intervention for psychiatric care in this vulnerable population.