Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust

Fusar‐Poli, Paolo; Werbeloff, Nomi; Rutigliano, Grazia; Oliver, Dominic; Davies, Cathy; Ståhl, Daniel; McGuire, Philip; Osborn, David

doi:10.1093/schbul/sby070

Cited by 81 publications

(96 citation statements)

References 55 publications

(101 reference statements)

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“…Given such profound differences, it was expected that the developed model could not be easily transported to new settings. 9 Even though lower in degree, our model kept a valid and beyond chance prognostic capacity in parsing future risk of depression among the adolescents in the independent cohorts, especially when heterogeneity among samples was accounted for (Supplement 3 and Figure S3a-d, available online).…”

Section: Discussionmentioning

confidence: 88%

“…In line with the current literature on the early detection of psychopathology in youth, 37 we believe that a transdiagnostic approach could be considered, despite its limitations, 38 as specificity of psychiatric prognostic models is likely to be low and as less specific preventive interventions could promote meaningful changes in psychiatric burden, either from individual or public health perspectives. 9,39 In conclusion, we present the development of a prognostic model for MDD among Brazilian adolescents, externally evaluated in two samples from diverse sociocultural contexts using different strategies for data collection than the original cohort. Heterogeneity among studies was high and possibly accounted for major discrepancies in prognostic performance, probably related not only to different case mix but also to weight of coefficients.…”

Section: Discussionmentioning

confidence: 99%

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Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents

Rocha

Fisher

Caye

et al. 2021

Journal of the American Academy of Child & Adolescent Psych

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Objective: Prediction models have become frequent in the medical literature, but most published studies are conducted in a single setting. Heterogeneity between development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18 and to evaluate its generalizability in two samples from diverse socioeconomic and cultural settings. Method: Data from the 1993 Pelotas Birth Cohort were used to develop the prediction model, and its generalizability was evaluated in two representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study. Results: At age 15, 2,192 adolescents with no evidence of current or previous depression were included (44.6% male). The apparent C-statistic of the models derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among samples improved the model's calibration in both samples. Conclusion: An adolescent depression risk score comprising easily obtainable predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents

Rocha

Fisher

Caye

et al. 2021

Journal of the American Academy of Child & Adolescent Psych

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“…It is therefore clear that we need to improve our ability to detect CHR-P individuals in secondary mental health care, primary care, and in the community. Some experimental approaches have been validated, which include automatic risk calculators for use in mental health trusts (57,103,105) or in the community (106).…”

Section: Evidence-based Efficacy Of Preventive Treatments For Chr-p Imentioning

confidence: 99%

“…In this regard, living meta-analyses could provide new evidence earlier than updating a conventional meta-analysis. started delivering precision medicine tools for stratifying CHR-P individuals and may permit an individualized prediction of their outcomes (57,132,133). These consortia are adopting a multimodal prediction approach which is spanning clinical, neurocognitive, neurobiological, as well as genetic predictors (134) to improve the individualized prognosis of outcomes in this population.…”

Section: Stratification and Precision Medicinementioning

confidence: 99%

Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)

et al. 2019

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Background: Indicated primary prevention in young people at Clinical High Risk for Psychosis (CHR-P) is a promising avenue for improving outcomes of one of the most severe mental disorders but their effectiveness has recently been questioned. Methods: Umbrella review. A multi-step independent literature search of Web of Science until January 11, 2019, identified interventional meta-analyses in CHR-P individuals. The individual randomised controlled trials that were analysed by the meta-analyses were extracted. A review of ongoing trials and a simulation of living meta-analysis complemented the analysis. Results: Seven meta-analyses investigating preventive treatments in CHR-P individuals were included. None of them produced pooled effect sizes across psychological, pharmacological, or other types of interventions. The outcomes analysed encompassed risk of psychosis onset, the acceptability of treatments, the severity of attenuated positive/negative psychotic symptoms, depression, symptom-related distress, social functioning, general functioning, and quality of life. These meta-analyses were based on 20 randomised controlled trials: the vast majority defined the prevention of psychosis onset as their primary outcome of interest and only powered to large effect sizes. There was no evidence to favour any preventive intervention over any other (or control condition) for improving any of these clinical outcomes. Caution is required when making clinical recommendations for the prevention of psychosis in individuals at risk. Discussion: Prevention of psychosis from a CHR-P state has been, and should remain, the primary outcome of interventional research, refined and complemented by other clinically meaningful outcomes. Stagnation of knowledge should promote innovative and collaborative research efforts, in line with the progressive and incremental nature of medical knowledge. Advancements will most likely be associated with the development of new experimental therapeutics that are ongoing along with the ability to deconstruct the high heterogeneity within CHR-P populations. This would require the estimation of treatment-specific effect sizes through living individual participant data meta-analyses,

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“…This model leverages electronic health record (EHR) data, therefore allowing for the automatic detection of at-risk individuals. This prognostic model has shown adequate performance in a first external validation in the SLaM boroughs of Lewisham and Croydon ( n = 54,716, Harrell’s C = 0.79) 7 and in a second external validation in the Camden and Islington NHS Foundation Trust (C&I; n = 13,702, Harrell’s C = 0.73) 11 , with Harrell’s C demonstrating the probability that a randomly selected patient who experienced an event will have a higher score than a patient who did not. This prognostic model is also currently being piloted for real-world implementation in clinical routine in the UK 12 .…”

Section: Introductionmentioning

confidence: 99%

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

et al. 2020

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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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Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust

Abstract: This risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided.

Cited by 81 publications

References 55 publications

Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents

Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents

Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

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