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Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis.

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Title: Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis.
Authors: Perry, B. I. (AUTHOR), Upthegrove, R. (AUTHOR), Crawford, O. (AUTHOR), Jang, S. (AUTHOR), Lau, E. (AUTHOR), McGill, I. (AUTHOR), Carver, E. (AUTHOR), Jones, P. B. (AUTHOR), Khandaker, G. M. (AUTHOR)
Source: Acta Psychiatrica Scandinavica. Sep2020, Vol. 142 Issue 3, p215-232. 18p. 1 Diagram, 2 Charts, 2 Graphs.
Subjects: Forecasting, Meta-analysis, Psychoses, Algorithms, At-risk people
Abstract: 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. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. Results: We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. Conclusion: Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high‐risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required. [ABSTRACT FROM AUTHOR]
Copyright of Acta Psychiatrica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis.
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  Data: <searchLink fieldCode="JN" term="%22Acta+Psychiatrica+Scandinavica%22">Acta Psychiatrica Scandinavica</searchLink>. Sep2020, Vol. 142 Issue 3, p215-232. 18p. 1 Diagram, 2 Charts, 2 Graphs.
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  Data: 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. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. Results: We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. Conclusion: Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high‐risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Acta Psychiatrica Scandinavica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Sep2020
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