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Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviours, and the patient’s lifetime trajectory

Published online by Cambridge University Press:  16 November 2022

Michael Maes Open the ORCID record for Michael Maes [Opens in a new window] ,
Juliana Brum Moraes ,
Ana Congio ,
Heber Vargas  and
Sandra Nunes
Michael Maes*
Affiliation:
Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand Cognitive Fitness and Technology Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
Juliana Brum Moraes
Affiliation:
Health Sciences Graduate Program, Health Sciences Center, State University of Londrina, Londrina, PR, Brazil
Ana Congio
Affiliation:
Health Sciences Graduate Program, Health Sciences Center, State University of Londrina, Londrina, PR, Brazil
Heber Vargas
Affiliation:
Health Sciences Graduate Program, Health Sciences Center, State University of Londrina, Londrina, PR, Brazil
Sandra Nunes
Affiliation:
Health Sciences Graduate Program, Health Sciences Center, State University of Londrina, Londrina, PR, Brazil
*
Author for correspondence: Michael Maes, Email: dr.michaelmaes@hotmail.com
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Abstract

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.

Keywords

psychiatrymood disordersmajor depressionneuroimmuneoxidative stressprecision medicine models
Type
Protocol
Information
Acta Neuropsychiatrica , Volume 35 , Issue 2 , April 2023 , pp. 104 - 117
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

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