Accuracy of Clinician Predictions of Future Self‐Harm: A Systematic Review and Meta‐Analysis of Predictive Studies

Woodford, Rachel; Spittal, Matthew J; Milner, Allison; McGill, Katie; Kapur, Navneet; Pirkis, Jane; Mitchell, Alex J.; Carter, Gregory

doi:10.1111/sltb.12395

Cited by 64 publications

(57 citation statements)

References 37 publications

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“…Our findings place the findings on accuracy of suicide risk assessment in conflict with recent research in this area that has found clinical judgement (Woodford et al, ) and risk scales (Chan et al, ) to be inaccurate predictors. The primary reason for this is these studies used completed suicide as the criteria for risk being present.…”

Section: Discussioncontrasting

confidence: 99%

Predictive validity and interrater reliability of the FACE‐CARAS toolkit in a CAMHS setting

Evans

Young

Tiffin

2019

Criminal Behav Ment Health

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Background The FACE‐CARAS (Functional Analysis in Care Environments‐Child and Adolescent Risk‐Assessment Suite) toolkit has been developed to support practitioners in Child and Adolescent Mental Health Services (CAMHS) in performing a structured risk assessment. It covers a number of risk domains including violence, suicide, self‐harm, experienced abuse, and exploitation. Interrater and internal reliability has already been established but not predictive validity. Aims/Hypothesis Our aim was to establish the predictive validity of the FACE‐CARAS in a CAMHS population. Methods Records from 123 young people with FACE‐CARAS ratings completed by clinicians were examined in a retrospective file review to extract data on a relevant list of adverse outcomes at three and at 6 months following the assessment. Although this was not a prospective longitudinal study, researchers were blind to the clinicians' ratings, allowing valid testing of predictive power. Cases were drawn from across generic and specialist CAMHS teams in approximately equal proportions. Data were analysed using receiver operator characteristic statistics. Results Areas under the curve values in five of the seven risk areas approached or were greater than 0.8 indicated that the FACE‐CARAS profile score was a good potential predictor of risks of self‐harm, suicidal behaviours, serious self‐neglect, abuse or exploitation by others, and violence to others at both 3 and 6 months. It was weakly “predictive” of accidental self‐harm and no better than chance at signalling physical ill health. Conclusions Findings support the use of the “profile summary” section of the tool as likely to generate clinically useful risk predictions. We were concerned that clinical use of the scale did not conform to research standards and often left subscales incompletely rated; however, the fact that the tool nonetheless proved a good predictor of most key adversities under scrutiny may add weight to its value in clinical practice. Further work with the FACE‐CARAS subscales is recommended.

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Section: Discussioncontrasting

confidence: 99%

Predictive validity and interrater reliability of the FACE‐CARAS toolkit in a CAMHS setting

Evans

Young

Tiffin

2019

Criminal Behav Ment Health

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“…Most international guidelines recommend a needs-based assessment in high-risk settings [37], yet carrying out such assessments can be time and resource intensive, lead to the use of informal triage rules [66,67], or rely on unvalidated locally-developed proformas [67]. Furthermore, there are major limitations to relying on clinical judgements for a range of outcomes, including future self-harm [68,69]. Together, this reiterates the challenges health services and clinicians face when trying to prevent self-harm.…”

Section: Plos Onementioning

confidence: 99%

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study

Iorfino

Carpenter

et al. 2020

PLoS ONE

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Background A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making regarding a service response in terms of more detailed assessments and/or intervention. The aim of this study was to predict self-harm within six-months after initial presentation. Method The study included 1962 young people (12–30 years) presenting to youth mental health services in Australia. Six machine learning algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis. Results Out of 1962 young people, 320 (16%) engaged in self-harm in the six months after first assessment and 1642 (84%) did not. The top 25% of young people as ranked by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair overall prediction (AUROCs; 0.744–0.755) and calibration which indicates that predicted probabilities were close to the true probabilities (brier scores; 0.185–0.196). The net benefit of these models were positive and superior to the ‘treat everyone’ strategy. The strongest predictors were (in ranked order); a history of self-harm, age, social and occupational functioning, sex, bipolar disorder, psychosis-like experiences, treatment with antipsychotics, and a history of suicide ideation. Conclusion Prediction models for self-harm may have utility to identify a large sub population who would benefit from further assessment and targeted (low intensity) interventions. Such models could enhance health service approaches to identify and reduce self-harm, a considerable source of distress, morbidity, ongoing health care utilisation and mortality.

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“…Predicting suicide is a difficult and complex challenge (Carter et al, 2017; Franklin et al, 2017; Woodford et al, 2019). However, findings from a study by Nock et al (2010) suggest that a new behavioral test and algorithm assessing implicit identification with death or suicide can accurately predict suicidal behavior.…”

mentioning

confidence: 99%

Forecasting a Fatal Decision: Direct Replication of the Predictive Validity of the Suicide–Implicit Association Test

et al. 2019

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A previous study by Nock et al. (2010) suggested that people’s implicit identification with “death” or “suicide” can accurately predict whether they will attempt suicide several months in advance. We report the first direct and independent replication of this promising finding. Participants were 165 patients seeking treatment at a psychiatric unit in France. At baseline, patients completed the Suicide–Implicit Association Test (S–IAT), a semistructured interview, and a self-report measure of suicide ideation. Six months later, we contacted participants by phone and examined their hospital medical records to determine whether they had made a new suicide attempt. Results showed that the S–IAT did not distinguish between patients who were admitted to the hospital following suicide attempts and those who were admitted for other reasons. As in the original study, however, the S–IAT predicted suicide attempts within the 6-month follow-up period beyond well-known predictors. The test correctly classified 85% of patients (95% confidence interval = [76.91, 91.53]), supporting its diagnostic value for identifying who will make a suicide attempt.

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Accuracy of Clinician Predictions of Future Self‐Harm: A Systematic Review and Meta‐Analysis of Predictive Studies

Cited by 64 publications

References 37 publications

Predictive validity and interrater reliability of the FACE‐CARAS toolkit in a CAMHS setting

Predictive validity and interrater reliability of the FACE‐CARAS toolkit in a CAMHS setting

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study

Forecasting a Fatal Decision: Direct Replication of the Predictive Validity of the Suicide–Implicit Association Test

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