Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness in the Paisa population: a case-control study.

Background: Severe mental illness diagnoses have overlapping symptomatology and shared genetic risk, motivating cross-diagnostic investigations of disease-relevant quantitative measures. We analysed relationships between neurocognitive performance, symptom domains, and diagnoses in a large sample of people with severe mental illness not ascertained for a specific diagnosis (cases), and people without mental illness (controls) from a single, homogeneous population.

Methods: In this case-control study, cases with severe mental illness were ascertained through electronic medical records at Clínica San Juan de Dios de Manizales (Manizales, Caldas, Colombia) and the Hospital Universitario San Vicente Fundación (Medellín, Antioquía, Colombia).

The Early Psychosis Screener for Internet (EPSI)-SR: Predicting 12 month psychotic conversion using machine learning.

Introduction: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention.

Methods: Self-report Likert-scale survey items were administered to individuals being screened with the Structured Interview for Psychosis-risk Syndromes (SIPS) and followed at eight early psychosis clinics. An a priori analytic plan included Spectral Clustering Analysis to reduce the item pool, followed by development of Support Vector Machine (SVM) classifiers.

Rapid and Accurate Behavioral Health Diagnostic Screening: Initial Validation Study of a Web-Based, Self-Report Tool (the SAGE-SR).

Background: The Structured Clinical Interview for DSM (SCID) is considered the gold standard assessment for accurate, reliable psychiatric diagnoses; however, because of its length, complexity, and training required, the SCID is rarely used outside of research.

Objective: This paper aims to describe the development and initial validation of a Web-based, self-report screening instrument (the Screening Assessment for Guiding Evaluation-Self-Report, SAGE-SR) based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the SCID-5-Clinician Version (CV) intended to make accurate, broad-based behavioral health diagnostic screening more accessible within clinical care.

Methods: First, study staff drafted approximately 1200 self-report items representing individual granular symptoms in the diagnostic criteria for the 8 primary SCID-CV modules.

The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.

Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire-Brief Version (PQ-B) and 148 additional items were administered to 229 individuals being screened with the SIPS at 7 North American Prodrome Longitudinal Study sites and at Columbia University. Fifty individuals were found to have SIPS scores of 0, 1, or 2, making them clinically low risk (CLR) controls; 144 were classified as clinically high risk (CHR) (SIPS 3-5) and 35 were found to have first episode psychosis (FEP) (SIPS 6).

The Early Psychosis Screener (EPS): Item development and qualitative validation.

A panel of experts assembled and analyzed a comprehensive item bank from which a highly sensitive and specific early psychosis screener could be developed. Twenty well-established assessments relating to the prodromal stage, early psychosis, and psychosis were identified. Using DSM-5 criteria, we identified the core concepts represented by each of the items in each of the assessments.