Risk Prediction in Psychosis: Progress Made and Challenges Ahead.

Published on Nov 1, 2021in Biological Psychiatry13.382
· DOI :10.1016/J.BIOPSYCH.2021.08.015
Benjamin Ian Perry11
Estimated H-index: 11
(University of Cambridge),
Emanuele F. Osimo8
Estimated H-index: 8
(University of Cambridge),
Gulam Khandaker35
Estimated H-index: 35
(University of Cambridge)
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Abstract
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#1Nikolaos Koutsouleris (MPG: Max Planck Society)H-Index: 51
#2Michelle Worthington (Yale University)H-Index: 5
Last. Jean Addington (U of C: University of Calgary)H-Index: 95
view all 35 authors...
Abstract null null Background null Transition to psychosis is among the most adverse outcomes of the clinical high-risk (CHR) syndromes encompassing ultra-high-risk (UHR) and basic symptoms states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. null null null Methods null We validated the previously ...
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#1Benjamin Ian Perry (University of Cambridge)H-Index: 11
#2Emanuele F. Osimo (University of Cambridge)H-Index: 8
Last. Gulam Khandaker (University of Cambridge)H-Index: 35
view all 11 authors...
BACKGROUND Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from common...
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#1Jack Wilkinson (MAHSC: Manchester Academic Health Science Centre)H-Index: 20
#2Kellyn F Arnold (University of Leeds)H-Index: 6
Last. Peter W G Tennant (The Turing Institute)H-Index: 4
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Summary Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces s...
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#1Benjamin Ian Perry (University of Cambridge)H-Index: 11
#2Rachel Upthegrove (University of Birmingham)H-Index: 23
Last. Gulam Khandaker (University of Cambridge)H-Index: 35
view all 9 authors...
OBJECTIVE Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high-risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis. METHODS We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations...
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#1Laure Wynants (Katholieke Universiteit Leuven)H-Index: 23
#2Ben Van Calster (Katholieke Universiteit Leuven)H-Index: 49
Last. Thomas P.A. Debray (UU: Utrecht University)H-Index: 31
view all 47 authors...
OBJECTIVE: To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. DESIGN: Rapid systematic review and critical appraisal. DATA SOURCES: PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. STUDY SEL...
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#1Richard D Riley (Keele University)H-Index: 72
#2Kym I.E. Snell (Keele University)H-Index: 18
Last. Gary S. Collins (University of Oxford)H-Index: 68
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When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in p...
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#1Robert WolffH-Index: 17
#2Karel G.M. Moons (UU: Utrecht University)H-Index: 117
Last. Susan Mallett (NIHR: National Institute for Health Research)H-Index: 60
view all 9 authors...
Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by...
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#1Juliet A. Usher-Smith (University of Cambridge)H-Index: 21
#2Stephen J. Sharp (University of Cambridge)H-Index: 93
Last. Simon J. Griffin (University of Cambridge)H-Index: 91
view all 3 authors...
The spectrum effect describes the variation between settings in performance of tests used to predict, screen for, and diagnose disease. In particular, the predictive use of a test may be different when it is applied in a general population rather than in the study sample in which it was first developed. This article discusses the impact of the spectrum effect on measures of test performance, and its implications for the development, evaluation, application, and implementation of such tests.
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