M Francesconi1, A Minichino2, R E Carrión3, R Delle Chiaie4, A Bevilacqua5, M Parisi6, S Rullo7, F Saverio Bersani4, M Biondi4, K Cadenhead8. 1. Department of Neurology and Psychiatry, Sapienza University of Rome, Italy; Department of Psychiatry, UCSD, La Jolla, CA, United States. 2. Department of Neurology and Psychiatry, Sapienza University of Rome, Italy; Department of Psychiatry, UCSD, La Jolla, CA, United States. Electronic address: amedeomin@gmail.com. 3. Division of Psychiatry, Zucker Hillside Hospital, Long Island, NY, United States. 4. Department of Neurology and Psychiatry, Sapienza University of Rome, Italy. 5. Research Center in Neurobiology, Daniel Bovet (CRiN), Rome, Italy; Department of Psychology, Section of Neuroscience, Sapienza University of Rome, Italy. 6. Villa Armonia Nuova, Rome, Italy. 7. Casa di Cura Villa Letizia, Rome, Italy. 8. Department of Psychiatry, UCSD, La Jolla, CA, United States.
Abstract
BACKGROUND: Accuracy of risk algorithms for psychosis prediction in "at risk mental state" (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status. METHODS: 138 non-psychotic outpatients (aged 17-31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD=0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index. RESULTS: 48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS-). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (-6.2%), but increased the sensitivity (+9.5%). CONCLUSIONS: These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes. Copyright Â
BACKGROUND: Accuracy of risk algorithms for psychosis prediction in "at risk mental state" (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status. METHODS: 138 non-psychotic outpatients (aged 17-31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD=0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index. RESULTS: 48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS-). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (-6.2%), but increased the sensitivity (+9.5%). CONCLUSIONS: These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes. Copyright Â
Authors: Dominic Oliver; Maite Arribas; Joaquim Radua; Gonzalo Salazar de Pablo; Andrea De Micheli; Giulia Spada; Martina Maria Mensi; Magdalena Kotlicka-Antczak; Renato Borgatti; Marco Solmi; Jae Il Shin; Scott W Woods; Jean Addington; Philip McGuire; Paolo Fusar-Poli Journal: Mol Psychiatry Date: 2022-06-03 Impact factor: 15.992
Authors: Ricardo E Carrión; Andrea M Auther; Danielle McLaughlin; Ruth Olsen; Jean Addington; Carrie E Bearden; Kristin S Cadenhead; Tyrone D Cannon; Daniel H Mathalon; Thomas H McGlashan; Diana O Perkins; Larry J Seidman; Ming T Tsuang; Elaine F Walker; Scott W Woods; Barbara A Cornblatt Journal: Schizophr Bull Date: 2019-06-18 Impact factor: 9.306