Think about a affected person sitting in entrance of you, weary of life, urgently asking you for assist. As a therapist, you do what you do greatest on this state of affairs: you are taking a coin out of your pocket, toss it within the air, and see which facet it lands on: Heads. “Properly,” you say cautiously to the affected person, “it seems like you’re susceptible to suicide.” Based mostly on this end result, you classify the affected person as suicidal and provoke the usual suicide prevention methods.
Okay, let’s overlook that situation shortly: who would determine a matter life-or-death by flipping a coin, proper?
It seems that assessing danger elements for suicide (De Beurs, 2024) doesn’t work significantly better than a coin flip, as a long time of proof reveals. In a complete meta-analysis of fifty years of analysis on danger elements for suicidal ideas and behaviours, Franklin and colleagues (2017) concluded that “prediction was solely barely higher than probability for all outcomes.”
Transferring on from easy prediction fashions with particular person danger elements (e.g., loneliness, a most cancers prognosis or home violence), the sphere transitioned to establishing extra complicated prediction fashions together with multivariable predictors as within the ideation-to-action frameworks (Klonsky, Saffer & Bryan, 2018; Torino et al., 2026), and extra subtle statistical approaches corresponding to machine studying (e.g., Boudreaux et al. 2021).
]Of their systematic evaluation on statistical prediction fashions for self-harm and suicide, Seyedsalehi and colleagues (2025) have synthesised proof from 91 articles about 167 fashions and critically appraised their predictive efficiency. Do these fashions make extra correct predictions than earlier analysis has been in a position to obtain? And what can we study from them for scientific follow?
Strategies
The reviewers searched 5 databases (MEDLINE, EMBASE, PsycINFO, CINAHL and International Well being) from inception to 30 November 2021. An up to date search together with exterior validations was carried out on 25 October 2024. Inclusion standards have been the event and/or exterior validation of statistical prediction fashions for self-harm and/or suicide. Fashions predicting suicidal ideation have been excluded, as have been danger evaluation scales, checklists and research of unassisted scientific judgement: solely multivariable fashions with statistically derived weights have been included. The Prediction mannequin Danger of Bias Evaluation Instrument (PROBAST) (Wolff et al., 2019) was used for risk-of-bias evaluation.
Outcomes
In complete, the systematic evaluation recognized 91 research reporting on 167 statistical danger prediction fashions (self-harm: 76 fashions; suicide: 51 fashions; mixed: 40 fashions) and 29 exterior validations. A minority of the fashions have been externally validated (8%, 14/167) or described in sufficient element to allow validation (17%, 28/167). Over 60% of fashions and exterior validations used knowledge from the USA (n = 125, 64%), and most (72%) have been based mostly on routine knowledge corresponding to digital well being data or administrative databases. As an indicator of mannequin complexity, the variety of predictor parameters within the last fashions ranged from 2 to eight,071 (median 13, IQR 6 to 29), and the variety of candidate parameters thought of ranged from 9 to over 89,000 (median 150).
One vital discovering was about how properly the fashions discriminated between danger and no danger. To evaluate their discriminatory power, the so-called C-index was used as a statistical measure (additionally termed concordance index; Harrell et al. 1982). A price of 0.5 is sort of a coin toss, whereas values nearer to 1 present that the mannequin is healthier at distinguishing between individuals at greater or decrease danger of suicide/self-harm.
- Within the mannequin growth research, C-indices different between 0.61 and 0.97 (median 0.82); that means that fashions predicted danger higher than probability.
- In exterior validation research, C-indices ranged from 0.60 to 0.86 (median 0.81), which is near the event determine and corresponding to prediction fashions in cardiovascular medication, respiratory medication and COVID-19. Discrimination did fall for self-harm fashions (0.85 to 0.73) and suicide fashions (0.82 to 0.76), however rose for fashions predicting the composite consequence (0.79 to 0.85).
- Median C-indices for every mannequin sort are introduced in desk 1.
Desk 1. Median C index of prediction fashions.
| Prediction Fashions | Growth Fashions | Exterior Validation |
| Self-harm (76 growth fashions) | 0.85 (IQR 0.78 to 0.89) | 0.73 (IQR 0.70 to 0.81) |
| Suicide (51 growth fashions) | 0.82 (IQR 0.74 to 0.85) | 0.76 (IQR 0.71 to 0.80) |
| Suicide & self-harm (40 growth fashions) | 0.79 (IQR 0.76 to 0.85) | 0.85 (IQR 0.82 to 0.85) |
Word: the exterior validation column relies on 29 exterior validations, not on the mannequin numbers proven.
Predicting danger is one factor (i.e. somebody is susceptible to suicide); figuring out whether or not that predicted danger corresponds to the precise danger is one other (i.e. a suicide try). Calibration was assessed for less than 15 of 167 fashions (9%) in growth research and in 9 of 29 exterior validations (31%), protecting six fashions in complete. Amongst these, two mannequin households confirmed enough discrimination and calibration in exterior validation: OxMIS and the Simon fashions, 5 fashions in complete. What precisely do they predict?
- OxMIS (Oxford Mental Illness and Suicide software) is a freely accessible web-based 17-item mannequin predicting suicide at 1 yr in individuals with extreme psychological sickness, utilizing socio-demographic and scientific danger elements. The unique growth paper (Fazel et al., 2019) reported sensitivity of 55% (95% confidence interval [CI] 47 to 63%), specificity of 75% (95% CI 74 to 75%), and constructive and destructive predictive values of two% and 99%. On this evaluation, OxMIS was the one mannequin whose exterior validations have been rated at low danger of bias.
- The 4 Simon fashions (Simon et al. 2018) predict 90-day danger of suicide try and suicide demise following psychological well being specialty and normal medical visits, utilizing 313 demographic and scientific traits from digital well being data. Throughout the 4 fashions, the unique paper reported sensitivity of seven.0% to 48.1%, specificity of 95.0% to 95.2%, constructive predictive values of 0.26% to five.4% and destructive predictive values of 99.6% to 99.9%.
Danger of bias was excessive for all mannequin growth research and all however two exterior validations (each of OxMIS). The principle causes have been incomplete or inappropriate analysis of predictive efficiency (92%), inadequate pattern sizes (77%), inappropriate dealing with of lacking knowledge (66%), and failure to account for overfitting and optimism in efficiency estimates (63%).
One discovering is simple to overlook. The sophisticated fashions did no higher than the straightforward ones. Excessive-dimensional fashions had a median C-index of 0.82, precisely the identical as low-dimensional fashions, and fashions constructed on routine knowledge (0.84) carried out very similar to these constructed on prospectively collected knowledge (0.81).
Conclusions
Although the coin-toss metaphor could not appear applicable for such an vital matter, precisely predicting suicide stays an actual problem. Thus, promising scientific outcomes ought to nonetheless be interpreted with real looking scepticism. On the one hand, the authors have recognized 5 fashions that demonstrated good predictive efficiency in exterior knowledge units (Seyedsalehi et al. 2025); thus, suggesting that:
“ […] blanket criticisms of the predictive efficiency of danger fashions for suicide outcomes are usually not evidence-based.”
Then again, the scientific usefulness of those fashions stays questionable. This might be mentioned additional within the scientific implication part.
Strengths and limitations
Strengths
- The authors have addressed a number of limitations of earlier critiques and supply a complete overview of a posh proof base.
- Research protocols (TRIPOD-SRMA; PRISMA) have been adhered to.
Limitations
- No meta-analysis was carried out, as defined by the authors, which limits the quantitative synthesis of the accessible proof.
- One vital limitation is that suicide makes an attempt and non-suicidal self-injury weren’t distinguished (p. 2). The authors observe the NICE definition of self-harm, any act of intentional self-injury or self-poisoning no matter intent, however these stay two distinct constructs (e.g., Brausch & Gutierrez, 2010; Muehlenkamp & Kerr, 2010). A European Scoping Overview highlights heterogenous terminologies and recommends a global settlement for future analysis (Jakobsen et al., 2023).
- A lot of the screening, knowledge extraction and danger of bias evaluation was carried out by a single reviewer, with solely 10% independently checked by a second.
- The evaluation didn’t contain sufferers or scientific consultants, which can clarify its predominantly scientific slightly than practice-oriented focus.
- A limitation of the proof base is the methodological weak point of current research, as criticised by the authors:
The event of so many suicide prediction fashions, usually utilizing sub-optimal strategies, and plenty of answering the identical analysis query, is a major supply of analysis waste.
Implications for follow
From a scientific perspective, this evaluation is extremely fascinating, methodologically robust and customarily well-written. From a scientific perspective, nonetheless, its quick sensible implications are much less clear. An vital query subsequently stays: How can these findings be translated into scientific follow?
Notably, solely eleven of 167 fashions (7%) might be accessed by clinicians as a software to calculate suicide danger (for instance utilizing a choice tree). A ‘fast and straightforward’ resolution for on a regular basis scientific follow sounds promising, however that doesn’t assure that it’s going to really be possible. Who gives entry to the software? How does it work in follow? Is restricted coaching obligatory?
The authors recommend their findings must be thought of in future updates to scientific pointers (p. 15), naming the NICE self-harm steering and NHS England suicide prevention steering, each of which at the moment advise towards danger prediction instruments. That deserves additional dialogue. Suicide danger evaluation presents a posh problem. People can’t be lowered to predefined classes or fashions, so no single mannequin is more likely to be adequate for precisely assessing suicide danger. As Teismann and colleagues (2026) summarise:
Current meta-analyses show that neither particular person danger elements, composite danger scores, scientific judgment, nor adherence to theoretical fashions or synthetic intelligence permits sufficiently correct prediction of suicidal habits.
As a substitute of specializing in danger prediction, we may concentrate on suicide prevention methods (Teismann et al., 2026). In truth, this may increasingly not require a lot, as a latest systematic evaluation by Homan and colleagues (2026) discovered that temporary interventions after suicide makes an attempt work (Hemming, 2026). Nonetheless, suicide prevention requires greater than interventions at a person stage; it additionally requires a public well being method (Lawson, 2024).
To bridge the hole between scientific findings and scientific follow, shut collaboration amongst public well being professionals, clinicians and researchers is important. This evaluation gives an vital scientific basis for such interdisciplinary efforts, in order that finally, prediction not turns into a matter of a coin toss however might be guided by evidence-based approaches.
Assertion of pursuits
Laura Melzer has no conflicts of curiosity to reveal. AI was used for enhancing functions solely.
Editor
Edited by Laura Hemming.
Hyperlinks
Major paper
Aida Seyedsalehi, James Bailey, Maya Ogonah, Thomas Fanshawe, Seena Fazel (2025). Prediction fashions for self-harm and suicide: a scientific evaluation and demanding appraisal. BMC medication, 23(1), 549. https://doi.org/10.1186/s12916-025-04367-6
Different references
Boudreaux, E. D., Rundensteiner, E., Liu, F., Wang, B., Larkin, C., Agu, E., Ghosh, S., Semeter, J., Simon, G., & Davis-Martin, R. E. (2021). Making use of Machine Studying Approaches to Suicide Prediction Utilizing Healthcare Information: Overview and Future Instructions. Frontiers in psychiatry, 12, 707916. https://doi.org/10.3389/fpsyt.2021.707916
Brausch, A.M., Gutierrez, P.M. Variations in Non-Suicidal Self-Harm and Suicide Makes an attempt in Adolescents. J Youth Adolescence 39, 233–242 (2010). https://doi.org/10.1007/s10964-009-9482-0
De Beurs, D. The nice unknown? Assessing suicide danger in trials of psychological interventions for melancholy. The Psychological Elf, August 2024.
Fazel, S., Wolf, A., Larsson, H. et al. The prediction of suicide in extreme psychological sickness: growth and validation of a scientific prediction rule (OxMIS). Transl Psychiatry 9, 98 (2019). https://doi.org/10.1038/s41398-019-0428-3
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