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Abstract
The real-world impact of psychosis prevention is reliant on effective strategies for identifying individuals at risk. A transdiagnostic, individualized, clinically-based risk calculator to improve this has been developed and externally validated twice in two different UK healthcare trusts with convincing results. The prognostic performance of this risk calculator outside the UK is unknown. All individuals who accessed primary or secondary health care services belonging to the IBM® MarketScan® Commercial Database between January 2015 and December 2017, and received a first ICD-10 index diagnosis of nonorganic/nonpsychotic mental disorder, were included. According to the risk calculator, age, gender, ethnicity, age-by-gender, and ICD-10 cluster diagnosis at index date were used to predict development of any ICD-10 nonorganic psychotic disorder. Because patient-level ethnicity data were not available city-level ethnicity proportions were used as proxy. The study included 2,430,333 patients with a mean follow-up of 15.36 months and cumulative incidence of psychosis at two years of 1.43%. There were profound differences compared to the original development UK database in terms of case-mix, psychosis incidence, distribution of baseline predictors (ICD-10 cluster diagnoses), availability of patient-level ethnicity data, follow-up time and availability of specialized clinical services for at-risk individuals. Despite these important differences, the model retained accuracy significantly above chance (Harrell’s C = 0.676, 95% CI: 0.672–0.679). To date, this is the largest international external replication of an individualized prognostic model in the field of psychiatry. This risk calculator is transportable on an international scale to improve the automatic detection of individuals at risk of psychosis.
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1 King’s College London, Early Psychosis: Interventions and Clinical detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764)
2 Lundbeck Singapore Pte, Ltd., Singapore, Singapore (GRID:grid.13097.3c)
3 H. Lundbeck A/S, Valby, Denmark (GRID:grid.424580.f) (ISNI:0000 0004 0476 7612)
4 H. Lundbeck A/S, Valby, Denmark (GRID:grid.424580.f) (ISNI:0000 0004 0476 7612); Karolinska Institutet, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
5 Lundbeck Pharmaceuticals LLC, Deerfield, USA (GRID:grid.4714.6)
6 King’s College London, Department of Biostatistics, Institute of Psychiatry, Psychology and Neuroscience, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764)
7 OASIS Service, South London and the Maudsley NHS National Health Service Foundation Trust, London, UK (GRID:grid.13097.3c); King’s College London, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764)
8 H. Lundbeck A/S, Valby, Denmark (GRID:grid.424580.f) (ISNI:0000 0004 0476 7612); Lund University, Clinical Memory Research Unit, Lund, Sweden (GRID:grid.4514.4) (ISNI:0000 0001 0930 2361)
9 King’s College London, Early Psychosis: Interventions and Clinical detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); OASIS Service, South London and the Maudsley NHS National Health Service Foundation Trust, London, UK (GRID:grid.13097.3c); University of Pavia, Department of Brain and Behavioural Sciences, Pavia, Italy (GRID:grid.8982.b) (ISNI:0000 0004 1762 5736)