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BackgroundAn accurate risk prediction algorithm could improve psychosis outcomes by reducing duration of untreated psychosis.ObjectiveTo develop and validate a risk prediction model for psychosis, for use by family doctors, using linked electronic health records.MethodsA prospective prediction study. Records from family practices were used between 1/1/2010 to 31/12/2017 of 300,000 patients who had consulted their family doctor for any nonpsychotic mental health problem. Records were selected from Clinical Practice Research Datalink Gold, a routine database of UK family doctor records linked to Hospital Episode Statistics, a routine database of UK secondary care records. Each patient had 5-8 years of follow up data. Study predictors were consultations, diagnoses and/or prescribed medications, during the study period or historically, for 13 nonpsychotic mental health problems and behaviours, age, gender, number of mental health consultations, social deprivation, geographical location, and ethnicity. The outcome was time to an ICD10 psychosis diagnosis.Findings830 diagnoses of psychosis were made. Patients were from 216 family practices; mean age was 45.3 years and 43.5 % were male. Median follow-up was 6.5 years (IQR 5.6, 7.8). Overall 8-year psychosis incidence was 45.8 (95 % CI 42.8, 49.0)/100,000 person years at risk. A risk prediction model including age, sex, ethnicity, social deprivation, consultations for suicidal behaviour, depression/anxiety, substance abuse, history of consultations for suicidal behaviour, smoking history and prescribed medications for depression/anxiety/PTSD/OCD and total number of consultations had good discrimination (Harrell's C = 0.774). Identifying patients aged 17-100 years with predicted risk exceeding 1.0 % over 6 years had sensitivity of 71 % and specificity of 84 %.FundingNIHR, School for Primary Care Research, Biomedical Research Centre.

Original publication




Journal article


Schizophrenia research

Publication Date





241 - 249


Centre for Academic Mental Health, Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK; National Institute for Health Research, Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK. Electronic address:


Humans, Prognosis, Prospective Studies, Psychotic Disorders, Middle Aged, Primary Health Care, Female, Male, Electronic Health Records