IEPA 11

Tianhong Zhang1, HuiJun Li2, Lihua Xu1, Kristen A. Woodberry3, Daniel I. Shapiro4, Margaret A. Niznikiewicz5, Martha E. Shenton6, Matcheri S. Keshavan5, William S. Stone3, JiJun Wang1, Robert W. McCarley5, Larry J. Seidman3; 1Shanghai Mental Health Center, 2Florida A & M University, Department of Psychology, Tallahassee, Florida 32307, USA, 3Harvard Medical School Department of Psychiatry, Beth Israel Deaconess Medical Center, 75 Fenwood Rd, Boston, MA 02115, USA, 4Emory University, Department of Psychology; Dekalb Community Service Board Prevention & Early Intervention Program, Atlanta, Georgia, 5Harvard Medical School Department of Psychiatry, Veteran’s Administration Medical Center, Boston, MA 02130, 6Brigham and Women’s Hospital, Departments of Psychiatry and Radiology, and Harvard Medical School, and VA Boston Healthcare System, Boston, MA, USA

Objective: As with other serious diseases, individuals at risk of psychosis really want to know how much specific risk have for developing a psychotic disorder. To this end, a web-based risk calculator(http://riskcalc.org:3838/napls/) for clinical high risk(CHR) population was developed from NAPLS-2 project. The present study aims to validate the predictive accuracy of the NAPLS-2 psychosis risk calculator in a CHR sample from the SHARP(ShangHai At Risk for Psychosis) program in Shanghai, China using comparable inclusion/exclusion criteria and assessments. Method: Three hundred CHR individuals were identified by the Chinese version of the Structured Interview for Prodromal Symptoms. Of these, 228(76.0%) completed neuro-cognitive assessments at baseline and 199(66.3%) had at least a one-year follow-up assessment. Six key predictors were entered into the NAPLS-2 model to generate a psychosis risk estimate for each case. The area under the receiver operating characteristic curve(AUC) was used to test the effectiveness of this discrimination. Results: The NAPLS risk calculator showed moderate discrimination of subsequent transition to psychosis in the SHARP sample with an AUC of 0.631(p=0.007). Whether discriminating either transition or poor treatment/clinical outcomes, the AUC of the model increased to 0.754(p<0.001). A risk estimate of 30% or higher had moderate sensitivity(53%) and excellent specificity(86%) for prediction of poor treatment/clinical outcome. Conclusions: The NAPLS-2 risk calculator largely generalizes to a Shanghai CHR sample but is meaningfully improved when predicting an individual’s poor clinical outcome as well as conversion as outcome states. Our findings provide a critical step in the implementation of CHR risk calculation in China.