Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

Studerus, Erich; Ramyead, Avinash; Riecher-Rössler, Anita

doi:10.1017/s0033291716003494

Cited by 81 publications

(79 citation statements)

References 109 publications

(256 reference statements)

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“…7,12,27,28 Furthermore, predictability should be compared across partly overlapping clinical syndromes, such as the CHR state and major depressive disorder, and benchmarked in large, geographically diverse cohorts of vulnerable persons. 29 Although it has been conceptually suggested, the question of whether behavioral and MRI-based data could be efficiently combined within sequential prognostic algorithms to optimize predictive power has yet to be empirically tested. 30 The clinical implementation of such algorithms does not only depend on the evidence for their generalizability, but also on the accuracy margin between models and the practices of health care professionals.…”

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confidence: 99%

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression

et al. 2018

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; for the PRONIA Consortium IMPORTANCE Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. OBJECTIVE To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning. DESIGN, SETTING, AND PARTICIPANTS This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018. AIN OUTCOMES AND MEASURES Performance and generalizability of prognostic models. RESULTS A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parietooccipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD. CONCLUSIONS AND RELEVANCE Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

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confidence: 99%

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression

et al. 2018

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“…The CHR paradigm has become a well-established clinical avenue to early detect and potentially treat the psychosis high-risk states. Based on the CHR paradigm, researchers have investigated the nature of the prepsychotic phase from both pathophysiological and epidemiological perspectives (4,5). However, these efforts have been challenged by a constantly declining incidence rate of psychosis among CHR patients (4,6), with roughly one third of not-transitioned CHR cases still experiencing subthreshold symptoms, psychosocial impairments (7), and lower level of quality of life (8).…”

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confidence: 99%

“…One way to tackle these issues is to use a meta-analytic approach to quantitatively investigate models' performance across different outcomes, algorithms, and data modalities. Although important contributions to this goal have been made (5,29,31), to the best of our knowledge, the field is still lacking such an analysis. Investigating the field's heterogeneity would allow a comprehensive assessment of accuracy and validity of the existing diagnostic and prognostic models, an important prerequisite for establishing reliable tools for psychosis risk quantification in clinical care.…”

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confidence: 99%

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

Sanfelici

Dwyer

Antonucci

et al. 2020

Biological Psychiatry

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BACKGROUND: The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied. METHODS: We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. RESULTS: A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects. CONCLUSIONS: Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

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“…However, predictive tools need to be validated in independent samples and different clinical contexts, as their performance depends on several factors, such as recruitment strategies, sample characteristics and instruments used for assessment. Since high-quality validation studies are largely lacking (Studerus 2017), the applicability of existing risk prediction tools is currently still limited to the research setting.…”

Section: Prognostic Considerations – How To Interpret a Clinical Highmentioning

confidence: 99%

Assessment and treatment of individuals at high risk for psychosis

2019

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SUMMARYEarly detection and specialised early intervention for people at high risk for psychotic disorders have received growing attention in the past few decades, with the aim of delaying or preventing the outbreak of explicit psychotic symptoms and improving functional outcomes. This article summarises criteria for a diagnosis of high psychosis risk, the implications for such a diagnosis and recommendations for treatment.LEARNING OBJECTIVESAfter reading this article you will be able to: •recognise signs and symptoms indicating increased psychosis risk•understand uses and limitations of screening for high psychosis risk, and interpretation of results•recognise evidence-based treatment options for patients at clinical high risk for psychosis.DECLARATION OF INTERESTC.A. has received non-financial support from Sunovion and Lundbeck in the past 36 months.

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Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

Cited by 81 publications

References 109 publications

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

Assessment and treatment of individuals at high risk for psychosis

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