Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning

Fusar-Poli, P; Schultze-Lutter, F (2016). Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning. Evidence-Based Mental Health, 19(1), pp. 10-15. BMJ Publishing Group 10.1136/eb-2015-102295

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Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes’ theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment.

Item Type:

Journal Article (Review Article)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy > Research Division

UniBE Contributor:

Schultze-Lutter, Frauke

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1362-0347

Publisher:

BMJ Publishing Group

Language:

English

Submitter:

Fabienne Bolliger

Date Deposited:

18 Feb 2016 11:02

Last Modified:

09 Feb 2017 10:24

Publisher DOI:

10.1136/eb-2015-102295

PubMed ID:

26792832

BORIS DOI:

10.7892/boris.75664

URI:

https://boris.unibe.ch/id/eprint/75664

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