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Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital

Background: Although there is evidence that individuals who suffer from severe mental disorders are at higher risk for aggressive behavior, only a minority eventually become violent. In 2017, Fazel et al. developed a risk calculator (Oxford Mental Illness and Violence tool, OxMIV) to identify the ri...

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Autores principales: Negatsch, Vincent, Voulgaris, Alexander, Seidel, Peter, Roehle, Robert, Opitz-Welke, Annette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489833/
https://www.ncbi.nlm.nih.gov/pubmed/31065245
http://dx.doi.org/10.3389/fpsyt.2019.00264
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author Negatsch, Vincent
Voulgaris, Alexander
Seidel, Peter
Roehle, Robert
Opitz-Welke, Annette
author_facet Negatsch, Vincent
Voulgaris, Alexander
Seidel, Peter
Roehle, Robert
Opitz-Welke, Annette
author_sort Negatsch, Vincent
collection PubMed
description Background: Although there is evidence that individuals who suffer from severe mental disorders are at higher risk for aggressive behavior, only a minority eventually become violent. In 2017, Fazel et al. developed a risk calculator (Oxford Mental Illness and Violence tool, OxMIV) to identify the risk of violent crime in patients with mental disorders. For the first time, we tested the predictive validity of the OxMIV in the department of psychiatry at the prison hospital in Berlin, Germany, and presented findings from our internal validation. Materials and Methods: We designed a cohort study with 474 patients aged 16–65 years old who met the inclusion criteria of schizophrenia-spectrum or bipolar disorder and classified the patients into two groups: a violent group with 191 patients and a nonviolent group with 283 patients. Violence was defined as the aggressive behavior of a patient with the necessity of special observation. We obtained all the required information retrospectively through patient files, applied the OxMIV tool on each subject, and compared the results of both groups. Sensitivity, specificity, and positive/negative predictive values were determined. We used logistic regression including variable selection and internal validation to identify relevant predictors of aggressive behavior in our cohort. Results: The OxMIV score was significantly higher in the violent group [median 4.21%; Interquartile range (IQR) 8.51%] compared to the nonviolent group (median 1.77%; IQR 2.01%; p < 0.0001). For the risk of violent behavior, using the 5% cutoff for “increased risk,” the sensitivity was 44%, and the specificity was 89%, with a positive predictive value of 72% and a negative predictive value of 70%. Applying logistic regression, four items were statistically significant in predicting violent behavior: previous violent crime (adjusted odds ratio 5.29 [95% CI 3.10–9.05], p < 0.0001), previous drug abuse (1.80 [1.08–3.02], p = 0.025), and previous alcohol abuse (1.89 [1.21–2.95], p = 0.005). The item recent antidepressant treatment (0.28 [0.17–0.47]. p < 0.0001) had a statistically significant risk reduction effect. Conclusions: In our opinion, the risk assessment tool OxMIV succeeded in predicting violent behavior in imprisoned psychiatric patients. As a result, it may be applicable for identification of patients with special needs in a prison environment and, thus, improving prison safety.
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spelling pubmed-64898332019-05-07 Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital Negatsch, Vincent Voulgaris, Alexander Seidel, Peter Roehle, Robert Opitz-Welke, Annette Front Psychiatry Psychiatry Background: Although there is evidence that individuals who suffer from severe mental disorders are at higher risk for aggressive behavior, only a minority eventually become violent. In 2017, Fazel et al. developed a risk calculator (Oxford Mental Illness and Violence tool, OxMIV) to identify the risk of violent crime in patients with mental disorders. For the first time, we tested the predictive validity of the OxMIV in the department of psychiatry at the prison hospital in Berlin, Germany, and presented findings from our internal validation. Materials and Methods: We designed a cohort study with 474 patients aged 16–65 years old who met the inclusion criteria of schizophrenia-spectrum or bipolar disorder and classified the patients into two groups: a violent group with 191 patients and a nonviolent group with 283 patients. Violence was defined as the aggressive behavior of a patient with the necessity of special observation. We obtained all the required information retrospectively through patient files, applied the OxMIV tool on each subject, and compared the results of both groups. Sensitivity, specificity, and positive/negative predictive values were determined. We used logistic regression including variable selection and internal validation to identify relevant predictors of aggressive behavior in our cohort. Results: The OxMIV score was significantly higher in the violent group [median 4.21%; Interquartile range (IQR) 8.51%] compared to the nonviolent group (median 1.77%; IQR 2.01%; p < 0.0001). For the risk of violent behavior, using the 5% cutoff for “increased risk,” the sensitivity was 44%, and the specificity was 89%, with a positive predictive value of 72% and a negative predictive value of 70%. Applying logistic regression, four items were statistically significant in predicting violent behavior: previous violent crime (adjusted odds ratio 5.29 [95% CI 3.10–9.05], p < 0.0001), previous drug abuse (1.80 [1.08–3.02], p = 0.025), and previous alcohol abuse (1.89 [1.21–2.95], p = 0.005). The item recent antidepressant treatment (0.28 [0.17–0.47]. p < 0.0001) had a statistically significant risk reduction effect. Conclusions: In our opinion, the risk assessment tool OxMIV succeeded in predicting violent behavior in imprisoned psychiatric patients. As a result, it may be applicable for identification of patients with special needs in a prison environment and, thus, improving prison safety. Frontiers Media S.A. 2019-04-23 /pmc/articles/PMC6489833/ /pubmed/31065245 http://dx.doi.org/10.3389/fpsyt.2019.00264 Text en Copyright © 2019 Negatsch, Voulgaris, Seidel, Roehle and Opitz-Welke http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Negatsch, Vincent
Voulgaris, Alexander
Seidel, Peter
Roehle, Robert
Opitz-Welke, Annette
Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title_full Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title_fullStr Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title_full_unstemmed Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title_short Identifying Violent Behavior Using the Oxford Mental Illness and Violence Tool in a Psychiatric Ward of a German Prison Hospital
title_sort identifying violent behavior using the oxford mental illness and violence tool in a psychiatric ward of a german prison hospital
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6489833/
https://www.ncbi.nlm.nih.gov/pubmed/31065245
http://dx.doi.org/10.3389/fpsyt.2019.00264
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