How accurate are suicide risk prediction models? Asking the right questions for clinical practice

Whiting, Daniel; Fazel, Seena

doi:10.1136/ebmental-2019-300102

Cited by 52 publications

(39 citation statements)

References 14 publications

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“…Whether prediction models and risk assessment tools can be applied to suicide prevention remains an open question. Future work needs to move towards real-world clinical evaluations that examine the incremental benefits of using these tools to support clinical decision-making ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment

et al. 2020

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Antidepressants are prescribed for the treatment of a number of psychiatric disorders in children and adolescents, however there is still controversy about whether they should be used in this population. This meta-review aimed to assess the effects of antidepressants for the acute treatment of attention-deficit/hyperactivity disorder (ADHD), anxiety disorders (ADs), autistic spectrum disorder (ASD), enuresis, major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) in children and adolescents. Efficacy was measured as response to treatment (either as mean overall change in symptoms or as a dichotomous outcome) and tolerability was measured as the proportion of patients discontinuing treatment due to adverse events. Suicidality was measured as suicidal ideation, behavior (including suicide attempts) and completed suicide. PubMed, EMBASE, and Web of Science were systematically searched (until 31 October 2019) for existing systematic reviews and/or meta-analyses of double-blind randomized controlled trials. The quality of the included reviews was appraised using AMSTAR-2. Our meta-review included nine systematic reviews/meta-analyses (2 on ADHD; 1 on AD; 2 on ASD; 1 on enuresis; 1 on MDD, 1 on OCD and 1 on PTSD). In terms of efficacy this review found that, compared to placebo: fluoxetine was more efficacious in the treatment of MDD, fluvoxamine and paroxetine were better in the treatment of AD; fluoxetine and sertraline were more efficacious in the treatment of OCD; bupropion and desipramine improved clinician and teacher-rated ADHD symptoms; clomipramine and tianeptine were superior on some of the core symptoms of ASD; and no antidepressant was more efficacious for PTSD and enuresis. With regard to tolerability: imipramine, venlafaxine, and duloxetine were less well tolerated in MDD; no differences were found for any of the antidepressants in the treatment of anxiety disorders (ADs), ADHD, and PTSD; tianeptine and citalopram, but not clomipramine, were less well tolerated in children and adolescents with ASD. For suicidal behavior/ideation, venlafaxine (in MDD) and paroxetine (in AD) were associated with a significantly increased risk; by contrast, sertraline (in AD) was associated with a reduced risk. The majority of included systematic reviews/meta-analyses were rated as being of high or moderate in quality by the AMSTAR-2 critical appraisal tool (one and five, respectively). One included study was of low quality and two were of critically low quality. Compared to placebo, selected antidepressants can be efficacious in the acute treatment of some common psychiatric disorders, although statistically significant differences do not always translate into clinically significant results. Little information was available about tolerability of antidepressants in RCTs of OCD and in the treatment of ADHD, ASD, MDD, and PTSD. There is a paucity of data on suicidal ideation/behavior, but paroxetine may increase the risk of suicidality in the treatment of AD and ven...

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Section: Discussionmentioning

confidence: 99%

Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment

et al. 2020

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“…According to a review by Kawashima et al 39 there is little evidence concerning the effects of different prevention measures. Also, the use of risk assessment models is still wanting, and the requirements for a widespread clinical implementation have not yet been met 40 …”

Section: Discussionmentioning

confidence: 99%

Exposure to suicidality in professionals working with oncology patients: An online survey

2020

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Objective: To explore and describe exposure to suicidality in healthcare providers (HCP) working with oncological patients. Special emphasis was put on five central aspects from the HCPs perspective: Exposure, Confidence, Expertise, Distress, and Education. Methods: A 48-item online questionnaire was developed and distributed to HCPs working with cancer patients. Three hundred fifty-four answered questionnaires were analyzed. Results: Overall 83.3% of HCPs reported to have encountered at least one suicidal patient in the last year. Feeling confident in talking about suicidality was reported by 72.1% of HCPs, with 71.2% of nurses reporting feeling insecure compared with only 5.1% of psychotherapists. Similarly, 22.3% of HCPs felt overwhelmed when confronted with a patient who substantiated his suicidality during consultation. A lack of personal knowledge concerning suicidality in general and in oncological patients in particular, was reported by 39.6% and 49.8%, respectively. In total, 88.1% of HCPs reported feeling distressed when confronted with suicidality, while 81.1% of participants wanted further education regarding suicidality in cancer patients despite that 73.2% had already received some sort of psycho-oncology education. Conclusions: Despite the well-documented fact of elevated suicide rates in cancer patients, there remain deficits in knowledge, which induce feelings of insecurity and helplessness in HCPs. There is a demand for further education concerning the treatment of suicidal cancer patients. Therefore, special curricula addressing this topic should be devised. A general debate about suicidality in cancer patients could help raise awareness of this problem and generate means of prevention.

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“…Although currently not feasible to implement, difficult to understand by clinicians, and lacking transparency [ 5 ], machine learning algorithms have been applied to large-scale data such as electronic medical records for predicting suicidal behavior. In machine learning analytics, selecting candidate predictors may benefit from established theories and clinical expertise.…”

Section: Introductionmentioning

confidence: 99%

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data

et al. 2020

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Background Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. Methods and findings The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87–0.89) and 0.89 (95% CI = 0.88–0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. Conclusions By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation.

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How accurate are suicide risk prediction models? Asking the right questions for clinical practice

Cited by 52 publications

References 14 publications

Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment

Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment

Exposure to suicidality in professionals working with oncology patients: An online survey

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data

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