Evidence map›Paper›PMID 42136514›Full record

ArticleWorld psychiatry : official journal of the World Psychiatric Association (WPA)2026

Predicting long-term poor outcomes in individuals at clinical high risk for psychosis using real-world clinical data: the OASIS1000 prospective study.

Yanakan Logeswaran, Andrea De Micheli, Ilaria Toniol, Tiexin Chen, Antonio Melillo, Maite Arribas, Nicholas R Livingston, Kamil Krakowski, Dominic Oliver, Daniel Stahl and 1 more

Abstract read
In one paragraph

Article in World psychiatry : official journal of the World Psychiatric Association (WPA), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Clinical high-risk vs. population-level prevention of mental disorders: a false dichotomy.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yanakan LogeswaranEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Andrea De MicheliEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Ilaria ToniolEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Tiexin ChenSouth London and Maudsley NHS Foundation Trust, London, UK.
Antonio MelilloEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Maite ArribasEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Nicholas R LivingstonDepartment of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Kamil KrakowskiEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Dominic OliverEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Daniel StahlDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Paolo Fusar-PoliEarly Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most research into the clinical high risk for psychosis (CHR-P) state has focused on predicting transition to psychosis in the short term, and largely in research samples that are not representative of real-world preventive care. However, CHR-P individuals experience a broad range of poor long-term clinical outcomes in real-world settings, such as hospitalization for psychiatric reasons, initiation of a new antipsychotic treatment, or suicide. Improving the detection of individuals at greater risk of these long-term poor outcomes is critical to advance preventive care in real-world clinical settings. This study aims to develop and internally validate a personalized clinical prediction model for real-world, long-term poor outcomes beyond transition risk in CHR-P individuals. This RECORD-compliant, real-world, long-term prospective cohort study included electronic health records (EHR) data from all CHR-P individuals receiving preventive care from the Outreach and Support in South London (OASIS) service in South London and Maudsley National Health Service Foundation Trust in the UK from 2001 to 2024. The primary outcome was the long-term cumulative risk of first real-world poor outcome, operationalized by pragmatic parameters informing clinical practice: transition to psychosis, receiving a first antipsychotic treatment at a dosage necessary to treat first-episode psychosis, receiving a first voluntary or compulsory hospitalization for psychiatric reasons, or dying by suicide. A clinical prediction model (regularized Cox regression) was developed and validated using internal-external cross-validation to predict long-term poor outcomes, utilizing real-world predictors available in routine care. Model performance was indexed by discrimination (Harrell's C), calibration (slope, intercept), overall performance (Brier score), and potential clinical utility (decision curve analysis). One thousand CHR-P OASIS patients were included (mean age: 22.51±4.99 years; 53.60% males, 44.73% White) and followed up to a maximum of 21 years. The cumulative risk of real-world poor outcome was 0.644 (95% CI: 0.547-0.742) at 14-18 years. The validated clinical prediction model showed statistically significant discrimination (C=0.69; 95% CI: 0.63-0.74), calibration (slope = 1.61, 95% CI: 0.74-2.48; intercept = -0.03, 95% CI: -0.62 to 0.55) and overall performance (Brier score = 0.18; 95% CI: 0.13-0.22). Decision curve analysis demonstrated that the model was associated with greater net benefit than the clinical alternatives at risk thresholds from 0% to 50%. These data suggest that most CHR-P individuals have long-term poor outcomes, beyond transition to psychosis, in real-world care, which should become the focus of a new generation of research. The clinical prediction model presented here can identify these individuals and facilitate the personalized provision of preventive care, thereby improving outcomes in this population. CHR-P services should extend their duration of care to address the substantial long-term clinical outcomes experienced by young individuals.

Indexed as

Clinical high risk for psychosisclinical prediction modellingfirst‐episode psychosislong‐term clinical outcomespreventive carereal‐world settings

Identifiers

PMID42136514
PMCPMC13176866

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.