Acta Psychiatr Scand. 2021 Aug 1. doi: 10.1111/acps.13355. Online ahead of print.
ABSTRACT
OBJECTIVE: Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings is unclear. Here we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR).
METHODS: 110 UHR-individuals underwent 3 Tesla diffusion weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction-model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status, and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression.
RESULTS: Ten UHR-individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ2 =17.595, p=0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88, and AUC of 0.87. Global FA predicted level of UHR-symptoms (R2 =0.055, F=6.084, p=0.016) and functional level (R2 = 0.040, F=4.57, p=0.036) at 6 months, but not at 12 months.
CONCLUSION: Global FA provided prognostic information on clinical outcome and symptom course of UHR-individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.
PMID:34333760 | DOI:10.1111/acps.13355
