| 1. |
- Fazel, Seena, et al.
(author)
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Identification of low risk of violent crime in severe mental illness with a clinical prediction tool (Oxford Mental Illness and Violence tool [OxMIV]) : a derivation and validation study
- 2017
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In: Lancet psychiatry. - : Elsevier. - 2215-0374 .- 2215-0366. ; 4:6, s. 461-468
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Journal article (peer-reviewed)abstract
- Background: Current approaches to stratify patients with psychiatric disorders into groups on the basis of violence risk are limited by inconsistency, variable accuracy, and unscalability. To address the need for a scalable and valid tool to assess violence risk in patients with schizophrenia spectrum or bipolar disorder, we describe the derivation of a score based on routinely collected factors and present findings from external validation.Methods: On the basis of a national cohort of 75 158 Swedish individuals aged 15-65 years with a diagnosis of severe mental illness (schizophrenia spectrum or bipolar disorder) with 574 018 patient episodes between Jan 1, 2001, and Dec 31, 2008, we developed predictive models for violent offending (primary outcome) within 1 year of hospital discharge for inpatients or clinical contact with psychiatric services for outpatients (patient episode) through linkage of population-based registers. We developed a derivation model to determine the relative influence of prespecified criminal history and sociodemographic and clinical risk factors, which are mostly routinely collected, and then tested it in an external validation. We measured discrimination and calibration for prediction of violent offending at 1 year using specified risk cutoffs.Findings: Of the cohort of 75 158 patients with schizophrenia spectrum or bipolar disorder, we assigned 58 771 (78%) to the derivation sample and 16 387 (22%) to the validation sample. In the derivation sample, 830 (1%) individuals committed a violent offence within 12 months of their patient episode. We developed a 16-item model. The strongest predictors of violent offending within 12 months were conviction for previous violent crime (adjusted odds ratio 5 . 03 [95% CI 4.23-5.98]; p < 0.0001), male sex (2.32 [1.91-2.81]; p < 0.0001), and age (0.63 per 10 years of age [0.58-0.67]; p < 0.0001). In external validation, the model showed good measures of discrimination (c-index 0.89 [0.85-0.93]) and calibration. For risk of violent offending at 1 year, with a 5% cutoff, sensitivity was 62% (95% CI 55-68) and specificity was 94% (93-94). The positive predictive value was 11% and the negative predictive value was more than 99%. We used the model to generate a simple web-based risk calculator (Oxford Mental Illness and Violence tool [OxMIV]).Interpretation: We have developed a prediction score in a national cohort of patients with schizophrenia spectrum or bipolar disorder, which can be used as an adjunct to decision making in clinical practice by identifying those who are at low risk of violent offending. The low positive predictive value suggests that further clinical assessment in individuals at high risk of violent offending is required to establish who might benefit from additional risk management. Further validation in other countries is needed. Copyright (C) The Author(s). Published by Elsevier Ltd.
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| 2. |
- Fazel, Seena, et al.
(author)
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Prediction of violent reoffending on release from prison : derivation and external validation of a scalable tool
- 2016
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In: Lancet psychiatry. - : Elsevier. - 2215-0374 .- 2215-0366. ; 3:6, s. 535-543
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Journal article (peer-reviewed)abstract
- Background: More than 30 million people are released from prison worldwide every year, who include a group at high risk of perpetrating interpersonal violence. Because there is considerable inconsistency and inefficiency in identifying those who would benefit from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent off ending in released prisoners.Methods: We did a cohort study of a population of released prisoners in Sweden. Through linkage of population-based registers, we developed predictive models for violent reoffending for the cohort. First, we developed a derivation model to determine the strength of prespecified, routinely obtained criminal history, sociodemographic, and clinical risk factors using multivariable Cox proportional hazard regression, and then tested them in an external validation. We measured discrimination and calibration for prediction of our primary outcome of violent reoffending at 1 and 2 years using cutoffs of 10% for 1-year risk and 20% for 2-year risk.Findings: We identified a cohort of 47 326 prisoners released in Sweden between 2001 and 2009, with 11 263 incidents of violent reoffending during this period. We developed a 14-item derivation model to predict violent reoffending and tested it in an external validation (assigning 37 100 individuals to the derivation sample and 10 226 to the validation sample). The model showed good measures of discrimination (Harrell's c-index 0.74) and calibration. For risk of violent reoffending at 1 year, sensitivity was 76% (95% CI 73-79) and specificity was 61% (95% CI 60-62). Positive and negative predictive values were 21% (95% CI 19-22) and 95% (95% CI 94-96), respectively. At 2 years, sensitivity was 67% (95% CI 64-69) and specificity was 70% (95% CI 69-72). Positive and negative predictive values were 37% (95% CI 35-39) and 89% (95% CI 88-90), respectively. Of individuals with a predicted risk of violent reoffending of 50% or more, 88% had drug and alcohol use disorders. We used the model to generate a simple, web-based, risk calculator (OxRec) that is free to use.Interpretation: We have developed a prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent off ending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment. Further assessments in other populations and countries are needed.
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| 3. |
- Fazel, Seena, et al.
(author)
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Risk of death by suicide following self-harm presentations to healthcare : development and validation of a multivariable clinical prediction rule (OxSATS)
- 2023
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In: BMJ Mental Health. - : BMJ Publishing Group Ltd. - 2755-9734. ; 26:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes. OBJECTIVE: We developed and validated a predictive model for suicide following self-harm.METHODS: We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work.FINDINGS: An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS).CONCLUSIONS: OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility.CLINICAL IMPLICATIONS: Using a clinical prediction score may assist clinical decision-making and resource allocation.
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| 4. |
- Fazel, Seena, et al.
(author)
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The prediction of suicide in severe mental illness : development and validation of a clinical prediction rule (OxMIS)
- 2019
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In: Translational Psychiatry. - : Nature Publishing Group. - 2158-3188. ; 9:1
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Journal article (peer-reviewed)abstract
- Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75,158 Swedish individuals aged 15-65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) with 574,018 clinical patient episodes between 2001 and 2008, split into development (58,771 patients, 494 suicides) and external validation (16,387 patients, 139 suicides) samples. A multivariable derivation model was developed to determine the strength of pre-specified routinely collected socio-demographic and clinical risk factors, and then tested in external validation. We measured discrimination and calibration for prediction of suicide at 1 year using specified risk cut-offs. A 17-item clinical risk prediction model for suicide was developed and showed moderately good measures of discrimination (c-index 0.71) and calibration. For risk of suicide at 1 year, using a pre-specified 1% cut-off, sensitivity was 55% (95% confidence interval [CI] 47-63%) and specificity was 75% (95% CI 74-75%). Positive and negative predictive values were 2% and 99%, respectively. The model was used to generate a simple freely available web-based probability-based risk calculator (Oxford Mental Illness and Suicide tool or OxMIS) without categorical cut-offs. A scalable prediction score for suicide in individuals with severe mental illness is feasible. If validated in other samples and linked to effective interventions, using a probability score may assist clinical decision-making.
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| 5. |
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| 6. |
- Howick, Jeremy, et al.
(author)
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Effects of empathic and positive communication in healthcare consultations: a systematic review and meta-analysis
- 2018
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In: Journal of the Royal Society of Medicine. - : Sage Publications. - 0141-0768 .- 1758-1095. ; 111:7, s. 240-252
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Journal article (peer-reviewed)abstract
- BackgroundPractitioners who enhance how they express empathy and create positive expectations of benefit could improve patient outcomes. However, the evidence in this area has not been recently synthesised.ObjectiveTo estimate the effects of empathy and expectations interventions for any clinical condition.DesignSystematic review and meta-analysis of randomised trials.Data sourceSix databases from inception to August 2017.Study selectionRandomised trials of empathy or expectations interventions in any clinical setting with patients aged 12 years or older.Review methodsTwo reviewers independently screened citations, extracted data, assessed risk of bias and graded quality of evidence using GRADE. Random effects model was used for meta-analysis.ResultsWe identified 28 eligible (n = 6017). In seven trials, empathic consultations improved pain, anxiety and satisfaction by a small amount (standardised mean difference −0.18 [95% confidence interval −0.32 to −0.03]). Twenty-two trials tested the effects of positive expectations. Eighteen of these (n = 2014) reported psychological outcomes (mostly pain) and showed a modest benefit (standardised mean difference −0.43 [95% confidence interval −0.65 to −0.21]); 11 (n = 1790) reported physical outcomes (including bronchial function/ length of hospital stay) and showed a small benefit (standardised mean difference −0.18 [95% confidence interval −0.32 to −0.05]). Within 11 trials (n = 2706) assessing harms, there was no evidence of adverse effects (odds ratio 1.04; 95% confidence interval 0.67 to 1.63). The risk of bias was low. The main limitations were difficulties in blinding and high heterogeneity for some comparisons.ConclusionGreater practitioner empathy or communication of positive messages can have small patient benefits for a range of clinical conditions, especially pain.Protocol registrationCochrane Database of Systematic Reviews (protocol) DOI: 10.1002/14651858.CD011934.pub2.
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| 7. |
- Howick, Jeremy, et al.
(author)
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Positive "framing" as a powerful medication for pain : A meta-analysis of randomized trials
- 2016
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In: European Journal of Integrative Medicine. - : Elsevier. - 1876-3820 .- 1876-3839. ; 8, s. 57-59
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Journal article (peer-reviewed)abstract
- Introduction: A growing body of evidence suggests that positive framing–inducing positive expectations about the outcome of treatments can reduce pain symptoms. However there is no pooled estimate of the effect size of positive framing for treating pain. Such an estimate is useful to understand the extent to which positive expectations can enhance usual care.Methods: We extracted data from a recent systematic review of interventions that modified all "context factors" (including but not limited to) inducing positive expectations) in adults suffering from pain. The systematic review concluded that positive expectations were effective, but did not pool the results so no effect size was provided. Two authors independently extracted data from the studies and conducted the analysis. Our primary outcome was patient self-reported pain.Results: 10 randomized trials were eligible for meta-analysis. In the trials with continuous outcomes the standardized effect size was −0.39 (95% confidence interval −0.68 to −0.10, p = 0.009, I2 = 79%), suggesting reduced pain on average in groups in which positive expectations were induced. The effect size was similar in magnitude but was not statistically significant when we excluded studies deemed to have a high risk of bias (standard effect size −0.31, 95% CI −0.65 to 0.02, p = 0.07, I2 = 77%).Conclusion: The effect of inducing positive expectations is comparable to the effects of some pharmacological drugs. However many of the studies had a high risk of bias, and heterogeneity was significant. Future research is warranted including investigating ways to implement this evidence into patient care in an ethical way.
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| 8. |
- Sariaslan, Amir, et al.
(author)
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Predicting suicide risk in 137,112 people with severe mental illness in Finland : external validation of the Oxford Mental Illness and Suicide tool (OxMIS)
- 2023
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In: Translational Psychiatry. - 2158-3188. ; 13:1
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Journal article (peer-reviewed)abstract
- Oxford Mental Illness and Suicide tool (OxMIS) is a standardised, scalable, and transparent instrument for suicide risk assessment in people with severe mental illness (SMI) based on 17 sociodemographic, criminal history, familial, and clinical risk factors. However, alongside most prediction models in psychiatry, external validations are currently lacking. We utilised a Finnish population sample of all persons diagnosed by mental health services with SMI (schizophrenia-spectrum and bipolar disorders) between 1996 and 2017 (n = 137,112). To evaluate the performance of OxMIS, we initially calculated the predicted 12-month suicide risk for each individual by weighting risk factors by effect sizes reported in the original OxMIS prediction model and converted to a probability. This probability was then used to assess the discrimination and calibration of the OxMIS model in this external sample. Within a year of assessment, 1.1% of people with SMI (n = 1475) had died by suicide. The overall discrimination of the tool was good, with an area under the curve of 0.70 (95% confidence interval: 0.69–0.71). The model initially overestimated suicide risks in those with elevated predicted risks of >5% over 12 months (Harrell’s Emax = 0.114), which applied to 1.3% (n = 1780) of the cohort. However, when we used a 5% maximum predicted suicide risk threshold as is recommended clinically, the calibration was excellent (ICI = 0.002; Emax = 0.005). Validating clinical prediction tools using routinely collected data can address research gaps in prediction psychiatry and is a necessary step to translating such models into clinical practice.
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