Prediction of psychosis in prodrome: development and validation of a simple, personalized risk calculator. Issue 12 (14th September 2018)

• Record Type: Journal Article
• Authors: Zhang, TianHong; Xu, LiHua; Tang, YingYing; Li, HuiJun; Tang, XiaoChen; Cui, HuiRu; Wei, YanYan; Wang, Yan; Hu, Qiang; Liu, XiaoHua; Li, ChunBo; Lu, Zheng; McCarley, Robert W.; Seidman, Larry J.; Wang, JiJun
• Is Part Of: Psychological medicine. Volume 49:Issue 12(2019)
• Page Start: 1990
• Page End: 1998
• Publication Date: 2018-09-14
• Subjects: Ultra high risk, -- clinical high risk, -- schizophrenia, -- conversion, -- transition, -- risk factor, -- outcome; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291718002738
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract: Background: This study aim to derive and validate a simple and well-performing risk calculator (RC) for predicting psychosis in individual patients at clinical high risk (CHR). Methods: From the ongoing ShangHai-At-Risk-for-Psychosis (SHARP) program, 417 CHR cases were identified based on the Structured Interview for Prodromal Symptoms (SIPS), of whom 349 had at least 1-year follow-up assessment. Of these 349 cases, 83 converted to psychosis. Logistic regression was used to build a multivariate model to predict conversion. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the effectiveness of the SIPS-RC. Second, an independent sample of 100 CHR subjects was recruited based on an identical baseline and follow-up procedures to validate the performance of the SIPS-RC. Results: Four predictors (each based on a subset of SIPS-based items) were used to construct the SIPS-RC: (1) functional decline; (2) positive symptoms (unusual thoughts, suspiciousness); (3) negative symptoms (social anhedonia, expression of emotion, ideational richness); and (4) general symptoms (dysphoric mood). The SIPS-RC showed moderate discrimination of subsequent transition to psychosis with an AUC of 0.744 ( p < 0.001). A risk estimate of 25% or higher had around 75% accuracy for predicting psychosis. The personalized risk generated by the SIPS-RC provided a solid estimate of conversion outcomes in the independent validation sample, with an AUC of 0.804 [95% confidence interval (CI) 0.662–0.951]. Conclusion: The SIPS-RC, which is simple and easy to use, can perform in the same manner as the NAPLS-2 RC in the Chinese clinical population. Such a tool may be used by clinicians to counsel appropriately their patients about clinical monitor v. potential treatment options.