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Development of the Korea-Polyenvironmental Risk Score for Psychosis
Authors:Eun-Jin Jeon  Shi-Hyun Kang  Yan-Hong Piao  Sung-Wan Kim  Jung-Jin Kim  Bong-Ju Lee  Je-Chun Yu  Kyu-Young Lee  Seung-Hee Won  Seung-Hwan Lee  Seung-Hyun Kim  Eui-Tae Kim  Clara Tammy Kim  Dominic Oliver  Paolo Fusar-Poli  Fatima Zahra Rami  Young-Chul Chung
Abstract:
Objective Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs). Methods We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the “population proportion” (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS). Results We first constructed the “K-PERS-I,” comprising five factors based on the PPS, and then the “K-PERS-II” comprising six factors based on the ERS. The instruments accurately predicted participants’ status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS. Conclusion The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.
Keywords:Polyenvironmental risk factor   Psychosis   Prediction
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