Brief checklists for assessing violence risk among patients discharged from acute psychiatric facilities: A preliminary study

Centre for Research and Education in Forensic Psychiatry, Ullevaal University Hospital, Bygg 7, Gaustad, NO-0320, Oslo, Norway.
Nordic Journal of Psychiatry (Impact Factor: 1.34). 05/2006; 60(3):243-8. DOI: 10.1080/08039480600780532
Source: PubMed


Violence risk assessment instruments are increasingly being used. Their use has, however, mostly been confined to forensic psychiatry for assessing the risk among perpetrators to repeat violent acts. In general psychiatry, very few studies of violence risk among discharged persons from acute inpatient units have been conducted. The available instruments are extensive and time consuming. This study aimed at the construction of a brief checklist. A 33-item scale, the PS (Preliminary Scheme), strongly influenced by the established HCR-20 (Historical, Clinical and Risk Management Assessment Scheme) was developed to undergo logistic regression analysis and possible item reduction. One hundred and ten patients from an acute inpatient unit, scored with PS at discharge, were monitored for violent episodes throughout the following year. Risk assessments and violence registrations were then compared. Of the 110 patients, 29 (26%) had acted violently during the follow-up, with the PS showing a definite association with post-discharge violence. Receiver operating characteristics (ROC) for the instrument yielded an area under the curve (AUC) of 0.71 (P<0.01). Regression analysis indicated that the number of PS items could be strongly reduced without losing predictive validity. Even a four-item checklist showed a higher AUC (0.77) than the PS did with all 33 items. The four items were: 1) Previous violence, 2) Substance use problems, 3) Lack of empathy and 4) Stress. The development of a brief risk assessment instrument with good predictive properties seems possible. Further clinical trials are planned. Ethical aspects of violence prediction must always be considered.

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Available from: Pål Hartvig, Jan 15, 2014
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    • "Nevertheless, the contribution of schizophrenia to violence is heavily debated and the conclusions still vary across studies (Bo et al., 2011). In previous studies of the accuracy of prediction of violence, the participants were usually from a global group of mental disorders or forensic patients (Dolan et al., 2008; Douglas et al., 1999, 2003; Hartvig et al., 2006; Suchy and Bolger, 1999). These nosological problems did limit comparisons among the studies of violence (Bo et al., 2011). "
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    ABSTRACT: A standard measure to assess and predict violence is important for psychiatric services. No prospective study has examined the history of violence and heterogeneity of violence in predicting specific types of violence among inpatient with schizophrenia. This study aimed to prospectively examine the accuracy of prediction of types of violence using the Chinese modified version of Violence Scale (VS-CM) among inpatients with schizophrenia based on their past history of violence and the real occurrence of violence during hospitalization. A prospective cohort study design. A total of 107 adult patients with schizophrenia spectrum disorders, consecutively admitted to an acute psychiatric ward of a university hospital in Taiwan, were recruited. In addition to data about demographics and clinical illness, count records of the history of violence within one month prior to admission by interview and the actual occurrence of violence during the whole course of hospitalization by participant observation were collected using the VS-CM. Multivariate logistic analysis and area under the Receiver Operating Characteristic curve (AUC) analysis were applied to examine the predictive ability of the VS-CM. A patient's history of violence assessed by the VS-CM predicted the actual occurrence of violence during hospitalization with the Odds Ratio of 17.5 (p=0.001). The predictive accuracy of the VS-CM had high sensitivity (97.0%), moderate positive predictive value (71.4%), and high negative predictive value (87.5%); however, the specificity was relatively low (35.0%). The AUC was 79.5% using the total scale of the VS-CM and 70.7-74.5% using the subscales in predicting corresponding types of violence. The VS-CM is a valid and reliable measure of potential violence. It can be applied to assess and predict specific types of violence among inpatient with schizophrenia.
    International journal of nursing studies 07/2013; 51(2). DOI:10.1016/j.ijnurstu.2013.06.002 · 2.90 Impact Factor
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    • "It is worth noting that our study found that LR, the classic traditional regression model, is robust in terms of any evaluation indicator (AUC values or sensitivity or specificity or accuracy). This finding is consistent with that of previous studies in forensic settings (Gardner et al. 1996; Hartvig et al. 2006; Rosenfeld and Lewis 2005; Stalens et al. 2004; Thomas et al. 2005) and in the broader medical field (Harper 2005; Tu 1996). There should be a benefit from the robustness properties of the LR model estimated by the maximum likelihood estimation procedure (Maria-Pia Victoria-Feser 2000). "
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    ABSTRACT: Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65–0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement. KeywordsViolence reconviction–Risk assessment–Neural networks–Classification and regression tree–HCR-20
    Journal of Quantitative Criminology 12/2011; 27(4):547-573. DOI:10.1007/s10940-011-9137-7 · 2.12 Impact Factor
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    • "The paucity of validated ''easy-to-use'' screens for evaluation of acute psychiatric patients' violence risk led to the development of the Violence Risk Screening-10 (V-RISK-10) [2] [10]. In the present study, our objectives were (a) to investigate the predictive validity and clinical usefulness of V-RISK-10 as a screen of violence risk 3 and 12 months after discharge from two acute psychiatric facilities and, specifically, (b) to test whether the instrument was able to detect risk in patients with no known history of violence. "
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    ABSTRACT: Current violence risk assessment instruments are time-consuming and mainly developed for forensic psychiatry. A paucity of violence screens for acute psychiatry instigated the development and validation of the V-RISK-10. The aim of this prospective naturalistic study was to test the predictive validity of the V-RISK-10 as a screen of violence risk after discharge from two acute psychiatric wards. Patients were screened with V-RISK-10 before discharge, and incidents of violence were recorded 3, 6, 9 and 12 months after discharge. A total of 381 of the 1017 patients that were screened completed the follow up. The ROC-AUC values for any violent behaviour were 0.80 and 0.75 (p<0.001) for the 3 and 12 months follow-up periods, respectively, and significant for both genders. The most accurate risk estimates were obtained for severe violence. For persons without a known history of violence prior to the screening, AUCs were 0.74 (p=0.004) and 0.68 (p=0.002). Results indicate that the V-RISK-10 is a valid and clinically useful screen for violence risk after discharge from acute psychiatry, and even significant for patients without a known previous history of violence.
    European Psychiatry 03/2011; 26(2):85-91. DOI:10.1016/j.eurpsy.2010.04.002 · 3.44 Impact Factor
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