se of a Bayesian Network Model to predict psychiatric illness in individuals with `at risk mental states' from a general population cohor

Author(s):Show less -

Loch, Alexandre Andrade ^{[1, 2]} ; Ara, Anderson ^[3] ; Hortencio, Lucas ^[1] ; Marques, Julia Hatagami ^[1] ; Talib, Leda Leme ^{[1, 2]} ; Andrade, Julio Cesar ^[1] ; Serpa, Mauricio Henriques ^{[1, 2, 4]} ; Sanchez, Luciano ^[1] ; Alves, Tania Maria ; van de Bilt, Martinus Theodorus ^{[1, 2]} ; Roessler, Wulf ^{[5, 6, 2]} ; Gattaz, Wagner Farid ^{[1, 2]}

Affiliation

^[1] Univ Sao Paulo, Hosp Clin HCFMUSP, Fac Med, Inst Psiquiatria, Lab Neurociencias LIM 27, Sao Paulo, SP - Brazil

^[2] Inst Nacl Biomarcadores Neuropsiquiatria INBION, Conselho Nacl Desenvolvimento Cientif & Tecnol, Sao Paulo - Brazil

^[3] Univ Fed Parana, Dept Stat, Curitiba, PR - Brazil

^[4] Univ Sao Paulo, Hosp Clin HCFMUSP, Fac Med, Inst Psiquiatr, Lab Neuroimagem Psiquiatria LIM 21, Sao Paulo, SP - Brazil

^[5] Univ Zurich, Hosp Psychiat, Dept Psychiat Psychotherapy & Psychosomat, Zurich - Switzerland

^[6] Charite, Dept Psychiat & Psychotherapy, Berlin - Germany

• Document type: Journal article
• Source: Neuroscience Letters; v. 770, JAN 23 2022.
• Web of Science Citations: 0

Abstract

The `at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning. (AU)

• FAPESP's process: 16/09069-1 - Follow-up of a cohort of individuals with ultra high risk for psychosis: assessment of genetic transition markers
• Grantee: Alexandre Andrade Loch
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/19823-0 - ASSESSMENT OF BIOLOGICAL AND ENVIRONMENTAL MARKERS IN THE PSYCHOSIS CONTINUUM AND IN A SAMPLE OF INDIVIDUALS IN HIGH RISK FOR PSYCHOSIS
• Grantee: Alexandre Andrade Loch
• Support Opportunities: Regular Research Grants