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  • 1.
    Fusar-Poli, Paolo
    et al.
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Psychosis Studies, Early Psychosis Intervent & Clin Detect EPIC Lab, London, England;South London & Maudsley Natl Hlth Serv Fdn Trust, OASIS Serv, London, England;Univ Pavia, Dept Brain & Behav Sci, Pavia, Italy.
    Hijazi, Ziad
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cardiology.
    Stahl, Daniel
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Biostat & Hlth Informat, London, England.
    Steyerberg, Ewout W.
    Leiden Univ, Med Ctr, Dept Biomed Data Sci Med Stat & Med Decis Making, Leiden, Netherlands.
    The Science of Prognosis in Psychiatry: A Review2018In: JAMA psychiatry, ISSN 2168-6238, E-ISSN 2168-622X, Vol. 75, no 12, p. 1289-1297Article, review/survey (Refereed)
    Abstract [en]

    IMPORTANCE Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.

    OBSERVATIONS The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.

    CONCLUSIONS AND RELEVANCE Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

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