Short-term risk assessments in an acute psychiatric inpatient setting: A re-examination of the Brøset Violence Checklist using repeated measurements – Differentiating violence characteristics and gender

Lockertsen, Øyvind; Varvin, Sverre; Færden, Ann; Vatnar, Solveig Karin Bø

doi:10.1016/j.apnu.2020.11.003

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…pointed out that BVC > 2 was more appropriate as the threshold of high violence tendency, while Lockertsen et al. believed that BVC > 4 was more appropriate as the threshold of high violence tendency (Jeon & Polich, 2003; Lockertsen et al., 2021). Different values of the threshold of high violence tendency may lead to inaccurate judgment of violence risk and thus affect the judgment of P300 latency and amplitude change.…”

Section: Discussionmentioning

confidence: 99%

Study on clinical characteristics of event‐related potential P300 in elderly schizophrenics and associated risk factors

Zhou

et al. 2023

Brain and Behavior

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Objective To investigate the clinical characteristics of event‐related potential P300 in elderly schizophrenics with different levels of violence and the risk factors of severe violence. Methods A total of 138 elderly schizophrenic patients from January 2020 to December 2021 in the First Hospital of Hebei Medical University were enrolled in this retrospective analysis. Based on the violence risk assessment, 61, 102, and 145 patients were divided into high‐risk, medium‐risk, and low‐risk groups, respectively. Clinical characteristics, P300 latency, and P300 amplitude were compared among the three groups followed by a logistic regression analysis of elderly schizophrenics with severe violence. Results The latency of P300 in the high‐risk group was higher than that in the low‐risk group (p < .05). The P300 amplitude of patients in the high‐risk group was significantly lower than that in the low‐risk group (p < .05). Univariate logistic regression analysis showed that previous history of violence, delusion of persecution, P300 latency, and amplitude were independent influencing factors of severe violence in elderly schizophrenics (odds ratio [OR]: 0.022, 95% confidence interval [CI]: 0.007–0.067, p < .001; OR: 0.118, 95% CI: 0.043–1.763, p = .037; OR: 1.289, 95% CI: 1.142–1.673, p < .001; and OR: 0.049, 95% CI: 0.021–0.067, p < 0.001, respectively). After adjusting gender, age, and other confounding factors, multivariate logistic regression analysis showed that delusion of persecution, P300 latency, and P300 amplitude were associated with severe violence in elderly schizophrenics (OR: 2.211, 95% CI: 0.061–4.067, p < .001; OR: 2.006, 95% CI: 1.421–2.721, p = .017; and OR: 0.067, 95% CI: 0.037–0.276; p < .001; respectively). Conclusion The latency and amplitude of P300 can be used as effective neuroelectrophysiological indicators to evaluate the violence level of elderly schizophrenics. Delusion of persecution, P300 latency, and P300 amplitude were independent influencing factors of severe violence in elderly schizophrenics.

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Section: Discussionmentioning

confidence: 99%

Study on clinical characteristics of event‐related potential P300 in elderly schizophrenics and associated risk factors

Zhou

et al. 2023

Brain and Behavior

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“…Bonta et al, as cited by Steinert and Whittington (2013), described being male as one of the strongest individual predictors of violent behavior. In addition, van de Sande et al (2013a) found a greater likelihood of male patients being in seclusion, and Lockertsen et al (2021) found that females were significantly less likely than males to be violent. In the current study also for gender an interaction effect was found with global functioning on the first day.…”

Section: Occurrence Of Disruptive Behavior and Patients' Characteristicsmentioning

confidence: 99%

“…Nevertheless, the number of data points that were filled was reasonably high. Longitudinal studies in similar settings lasted for only one year (Lockertsen et al, 2021). By contrast, this study utilized daily assessments across three years from one of the acute psychiatric units and across six years from the other unit.…”

Section: Strength and Limitationmentioning

confidence: 99%

“…Furthermore, verbal threats involving no physical contact may also be classified as disruptive behavior (Clarke et al, 2010). Between 3% and 44% of patients in acute psychiatric care are aggressive during their hospitalization (Grassie et al, 2001;Lockertsen et al, 2021;Lozzino et al, 2015). Disruptive behavior has negative effects on both patients and staff (Dack et al, 2013), and it leads to anxiety and insecurity in other patients (Hamrin et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Disruptive Behavior over Time from Changes in Patients’ Global Functioning in Acute Psychiatric Care

de Boer,

Pietersma,

Tiemens

2024

Adm Policy Ment Health

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Disruptive behavior of patients in acute psychiatric care is a problem for both patients and staff. Preventing a patient’s impending disruption requires recognizing and understanding early signals. There are indications that a change in a patient’s global functioning may be such a signal. The global functioning of patients is a multidimensional view on their functioning. It captures a patient’s psychological symptoms, social skills, symptoms of violence, and activities in daily living. The aim of this study was to gain insight into the predictive value of global functioning on the risk of disruptive behavior of patients in acute psychiatric care. Also assessed was the time elapsed between the change in global functioning and a patient’s disruptive behavior, which is necessary to know for purposes of early intervention. In a longitudinal retrospective study, we used daily measurements with the Brøset Violence Checklist (BVC) and the Kennedy Axis V (K-As) of each patient admitted to two acute psychiatric units over a period of six years. Data from 931 patients for the first 28 days after their admission were used for survival analysis and cox regression analysis. Disruptive behavior was mostly observed during the first days of hospitalization. Global functioning predicted disruptive behavior from the very first day of hospitalization. A cut-off score of 48 or lower on the K-As on the first admission day predicted a higher risk of disruptive behavior. If functioning remained poor or deteriorated substantially over three days, this was an additional signal of increased risk of disruptive behavior. Improvement in global functioning was associated with a decreased risk of disruptive behavior. More attention is needed for early interventions on global functioning to prevent disruptive behavior.

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“…A multitude of studies have been conducted on aggression/violence in psychiatric inpatient units [ 4 , 5 , 8 – 30 ]. Most have focused on individual patient risk factors for aggression/violence.…”

Section: Introductionmentioning

confidence: 99%

An analysis of Chinese nursing electronic medical records to predict violence in psychiatric inpatients using text mining and machine learning techniques

Hung

et al. 2023

PLoS ONE

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Background The prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance. Objective The present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients. Methods We collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data. Results The results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence. Conclusions Our findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.

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Short-term risk assessments in an acute psychiatric inpatient setting: A re-examination of the Brøset Violence Checklist using repeated measurements – Differentiating violence characteristics and gender

Cited by 6 publications

References 54 publications

Study on clinical characteristics of event‐related potential P300 in elderly schizophrenics and associated risk factors

Study on clinical characteristics of event‐related potential P300 in elderly schizophrenics and associated risk factors

Prediction of Disruptive Behavior over Time from Changes in Patients’ Global Functioning in Acute Psychiatric Care

An analysis of Chinese nursing electronic medical records to predict violence in psychiatric inpatients using text mining and machine learning techniques

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