The Science of Prognosis in Psychiatry: A Review
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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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).”
From Assisted suicide in persons with mental disorders: a review of clinical-ethical arguments and recommendations (2024) · cites this paper as [36]
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).”
From Physician Assisted Death for Psychiatric Suffering: Experiences in the Netherlands (2022) · cites this paper as [16]
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Preventive psychiatry: a blueprint for improving the mental health of young people
Fusar‐Poli, P., Correll, C. U., Arango, C., Berk, M., Patel, V., & Ioannidis, J. P. A. (2021) · World Psychiatry · doi:10.1002/wps.20869 · Cited by 497 · see citing works
Self-citationPrevention of Psychosis: Advances in Detection, Prognosis, and Intervention
Fusar-Poli, P., Salazar de Pablo, G., Correll, C. U., Meyer-Lindenberg, A., Millan, M. J., Borgwardt, S., Galderisi, S., Bechdolf, A., Pfennig, A., Kessing, L. V., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.4779 · Cited by 476 · see citing works
Self-citationSalazar de Pablo, G., Radua, J., Pereira, J., Bonoldi, I., Arienti, V., Besana, F., Soardo, L., Cabras, A., Fortea, L., Catalan, A., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.0830 · Cited by 375 · see citing works
Self-citationKoutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3604 · Cited by 275 · see citing works
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Jacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247 · see citing works
Catalan, A., Salazar de Pablo, G., Aymerich, C., Damiani, S., Sordi, V., Radua, J., Oliver, D., McGuire, P., Giuliano, A. J., Stone, W. S., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.1290 · Cited by 217 · see citing works
Self-citationMeehan, A. J., Lewis, S. J., Fazel, S., Fusar-Poli, P., Steyerberg, E. W., Stahl, D., & Danese, A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01528-4 · Cited by 219 · see citing works
Self-citationJacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177 · see citing works
Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)
Fusar-Poli, P., Davies, C., Solmi, M., Brondino, N., De Micheli, A., Kotlicka-Antczak, M., Shin, J. I., & Radua, J. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00764 · Cited by 108 · see citing works
Self-citationSalazar de Pablo, G., De Micheli, A., Nieman, D. H., Correll, C. U., Kessing, L. V., Pfennig, A., Bechdolf, A., Borgwardt, S., Arango, C., van Amelsvoort, T., et al. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.10.007 · Cited by 104 · see citing works
Self-citationSalazar de Pablo, G., Catalan, A., & Fusar-Poli, P. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3561 · Cited by 103 · see citing works
Self-citationIrving, J., Patel, R., Oliver, D., Colling, C., Pritchard, M., Broadbent, M., Baldwin, H., Stahl, D., Stewart, R., & Fusar-Poli, P. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa126 · Cited by 92 · see citing works
Self-citationFusar-Poli, P., Sullivan, S. A., Shah, J. L., & Uhlhaas, P. J. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00774 · Cited by 92 · see citing works
Self-citationSanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.009 · Cited by 82 · see citing works
Ethical dilemmas posed by mobile health and machine learning in psychiatry research
Jacobson, N. C., Bentley, K. H., Walton, A., Wang, S. B., Fortgang, R. G., Millner, A. J., Coombs, G., Rodman, A. M., & Coppersmith, D. D. L. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237107 · Cited by 76 · see citing works
Raket, L. L., Jaskolowski, J., Kinon, B. J., Brasen, J. C., Jönsson, L., Wehnert, A., & Fusar-Poli, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30024-8 · Cited by 60 · see citing works
Self-citationFlygare, O., Enander, J., Andersson, E., Ljótsson, B., Ivanov, V. Z., Mataix-Cols, D., & Rück, C. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-02655-4 · Cited by 51 · see citing works
Oliver, D., Spada, G., Colling, C., Broadbent, M., Baldwin, H., Patel, R., Stewart, R., Stahl, D., Dobson, R., McGuire, P., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2020.05.007 · Cited by 49 · see citing works
Self-citationOliver, D., Arribas, M., Radua, J., Salazar de Pablo, G., De Micheli, A., Spada, G., Mensi, M. M., Kotlicka-Antczak, M., Borgatti, R., Solmi, M., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01611-w · Cited by 49 · see citing works
Self-citationPostpartum depression: a developed and validated model predicting individual risk in new mothers
Munk-Olsen, T., Liu, X., Madsen, K. B., Kjeldsen, M.-M. Z., Petersen, L. V., Bergink, V., Skalkidou, A., Vigod, S. N., Frokjaer, V. G., Pedersen, C. B., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02190-8 · Cited by 36 · see citing works
Physician Assisted Death for Psychiatric Suffering: Experiences in the Netherlands
van Veen, S., Widdershoven, G., Beekman, A., & Evans, N. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.895387 · Cited by 29 · see citing works
Real-world digital implementation of the Psychosis Polyrisk Score (PPS): A pilot feasibility study
Oliver, D., Spada, G., Englund, A., Chesney, E., Radua, J., Reichenberg, A., Uher, R., McGuire, P., & Fusar-Poli, P. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.04.015 · Cited by 27 · see citing works
Self-citationRisk Calculators in Bipolar Disorder: A Systematic Review
Silva Ribeiro, J., Pereira, D., Salagre, E., Coroa, M., Santos Oliveira, P., Santos, V., Madeira, N., Grande, I., & Vieta, E. (2020) · Brain Sciences · doi:10.3390/brainsci10080525 · Cited by 26 · see citing works
Marckmann, G., & Pollmächer, T. (2024) · Annals of Palliative Medicine · doi:10.21037/apm-23-472 · Cited by 14 · see citing works
Fusar-Poli, P., Oliver, D., Spada, G., Estrade, A., & McGuire, P. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2020.11.031 · Cited by 6 · see citing works
- 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
- Publisher
- American Medical Association (AMA)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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