Predicting psychosis risk using a specific measure of cognitive control: a 12-month longitudinal study. Issue 13 (11th October 2020)

• Record Type: Journal Article
• Authors: Guo, Joyce Y.; Niendam, Tara A.; Auther, Andrea M.; Carrión, Ricardo E.; Cornblatt, Barbara A.; Ragland, J. Daniel; Adelsheim, Steven; Calkins, Roderick; Sale, Tamara G.; Taylor, Stephan F.; McFarlane, William R.; Carter, Cameron S.
• Is Part Of: Psychological medicine. Volume 50:Issue 13(2020)
• Page Start: 2230
• Page End: 2239
• Publication Date: 2020-10-11
• Subjects: AX-CPT, -- clinical-high-risk for psychosis, -- cognitive impairments; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291719002332
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract: Background: Identifying risk factors of individuals in a clinical-high-risk state for psychosis are vital to prevention and early intervention efforts. Among prodromal abnormalities, cognitive functioning has shown intermediate levels of impairment in CHR relative to first-episode psychosis and healthy controls, highlighting a potential role as a risk factor for transition to psychosis and other negative clinical outcomes. The current study used the AX-CPT, a brief 15-min computerized task, to determine whether cognitive control impairments in CHR at baseline could predict clinical status at 12-month follow-up. Methods: Baseline AX-CPT data were obtained from 117 CHR individuals participating in two studies, the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP) and the Understanding Early Psychosis Programs (EP) and used to predict clinical status at 12-month follow-up. At 12 months, 19 individuals converted to a first episode of psychosis (CHR-C), 52 remitted (CHR-R), and 46 had persistent sub-threshold symptoms (CHR-P). Binary logistic regression and multinomial logistic regression were used to test prediction models. Results: Baseline AX-CPT performance (d-prime context) was less impaired in CHR-R compared to CHR-P and CHR-C patient groups. AX-CPT predictive validity was robust (0.723) for discriminating converters v. non-converters, and even greater (0.771) when predicting CHR three subgroups. Conclusions: These longitudinal outcome data indicate that cognitive control deficits as measured by AX-CPT d-prime context are a strong predictor of clinical outcome in CHR individuals. The AX-CPT is brief, easily implemented and cost-effective measure that may be valuable for large-scale prediction efforts.