The Gold Standard Diagnosis of Schizophrenia is Counterproductive: Towards Quantitative Research and Diagnostic Algorithmic Rules (RADAR) and their Derived Qualitative Distinct Classes.

Abstract

Recently, we developed Research and Diagnostic Algorithm Rules (RADAR) to assess the clinical and pathway features of mood disorders. The aims of this paper are to review a) the methodology for developing continuous RADAR scores that describe the clinical and pathway features of schizophrenia, and b) a new method to visualize the clinical status of patients and the pathways implicated in RADAR graphs. We review how to interpret clinical RADAR scores, which serve as valuable tools for monitoring the staging of illness, lifetime suicidal behaviors, overall severity of illness, a general cognitive decline index, and a behavior-cognitive-psychosocial (BCPS) index that represents the "defect"; and b) pathway RADAR scores which reflect various protective (including the compensatory immune- inflammatory system) and adverse (including neuro-immune, neuro-oxidative, and neurotoxic biomarkers) outcome pathways. Using RADAR scores and machine learning, we created new, qualitatively different types of schizophrenia, such as major neurocognitive psychosis and simple psychosis. We also made RADAR graphs, which give us a quick way to compare the patient's clinical condition and pathways to those of healthy controls. We generated a personalized fingerprint for each patient, encompassing various clinical and pathway features of the disorder represented through RADAR graphs. The latter is utilized in clinical practice to assess the clinical condition of patients and identify treatment-required pathways to mitigate the risk of recurrent episodes, worsening BCPS, and increasing staging. The quantitative clinical RADAR scores should be used in schizophrenia research as dependent variables and regressed on the pathway RADAR scores.