Cited by 197Statistical Methods in EpidemiologyArtificial Intelligence in HealthcareMachine Learning in Healthcare

The Science of Prognosis in Psychiatry: A Review

Fusar-Poli, Paolo, Hijazi, Ziad, Stahl, Daniel, Steyerberg, Ewout W.·2018·JAMA Psychiatry

Abstract

Importance: Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry. Observations: The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given. Conclusions and Relevance: Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

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Crossref · 10.1001/jamapsychiatry.2018.2530

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97 of 97 references verified0 citation errors · 0 not checkable

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  • Mental illnesses, apart from a few exceptions including dementia, often take a course that cannot be reliably predicted in individual cases.

    “Apart from a few exceptions, which include dementia in particular, mental illnesses often take a course that cannot be reliably predicted in individual cases (36,37).”
  • It is very difficult to give a reliable prognosis for psychiatric suffering, even for patients who have extensive treatment histories, because little is known about the etiology of psychiatric suffering, there are hardly any reliable biomarkers, descriptive diagnostic reliability is relatively low, and it is difficult to predict the effects of treatment.

    “Secondly, it is very difficult to give a reliable prognosis for psychiatric suffering, even for patients who have extensive treatment histories (16, 17). This has different reasons: little is known about the etiology of psychiatric suffering, there are hardly any reliable biomarkers, descriptive diagnostic reliability is relatively low and it is difficult to predict the effects of treatment (18).”

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Authors
Fusar-Poli, P., Hijazi, Z., Stahl, D., & Steyerberg, E. W.
Affiliations
  • Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
  • Uppsala University (SE)
  • King's College London (GB)
  • Leiden University Medical Center (NL)
Venue
JAMA Psychiatry
Published
2018-12-01
Volume
75
Issue
12
Pages
1289
ISSN
2168-622X (ISSN-L)
OpenAlex
W2896481654
Cited by
197
References
97
Citations by year
2026: 14 · 2025: 15 · 2024: 17 · 2023: 17 · 2022: 37 · 2021: 33 · 2020: 32 · 2019: 32
FWCI
3.33
Type
journal article
Language
en
Open access
closed
Locations
  • JAMA Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion)
Funders
  • National Institute for Health and Care Research
  • Medical Research Council (MC_PC_16048)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
prognosis, clinical prediction model, mental illness, model validation, generalizability, predictor selection, clinical implementation, machine learning, precision psychiatry
MeSH terms
Machine Learning, Humans, Prognosis, Psychiatry, Risk Assessment, Biomedical Research, Datasets as Topic

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