Factors associated with repetitive violent behavior of psychiatric inpatients

Characteristics of International Assaultive Psychiatric Patients: Review of Published Findings, 2017-2022

Since the 1960s, empirical research has focused on a better understanding of the characteristics of assaultive psychiatric patients. International research from 1960 to 2017 indicated that male and female patients with schizophrenia and substance use disorder presented the greatest risk for assault with nursing personnel being at higher risk. This present review of studies sought to assess the latest research findings on assaultive patients for the most recent five-year period, 2017-2022. It was hypothesized that patients with schizophrenia and substance use disorders would present the greatest assault risk for nursing personnel. The studies in this review supported this hypothesis. Assaults by patients with schizophrenia and substance abuse has been a consistent finding worldwide for 62 years of published research. Explanations for these findings, the possible role of posttraumatic stress disorder (PTSD) in assaultive patients, and an updated methodological review are presented.

The Use of Alternative Rooms in Forensic and Regular Psychiatric Units: A Scoping Review

(1) Background: Emotional regulation, distress and relational conflicts often occur during hospitalization and rehabilitation on psychiatric units, especially in patients suffering from severe and persistent mental disorders. While widely used in children and geriatric patients, little literature exists on the use and outcomes of alternative rooms in the context of forensic and regular psychiatric units for adult patients. Considering the scarcity of the literature on alternative use, this study is motivated by the following research question: what are the main uses and outcomes of alternative rooms in the context of forensic and regular psychiatric units? The main objective of this study is to conduct a scoping review of the use and outcomes of alternative rooms for the context of psychiatric inpatients. (2) Methods: A systematic search was performed in the electronic databases of MedLine, Web of Science, PsycNet (PsycINFO) and Google Scholar from their inception dates until 2022. (3) Results: A total of nine studies were analyzed. Sensory, multisensory rooms, Snoezelen, and comfort rooms are the types of alternative rooms discussed in these studies. Distress and anxiety reduction, increase in self-esteem, impact on seclusion rates, patient-staff communication and alliances, heart and respiration rate reduction, and improvement of alexithymia were identified among the main uses and outcomes of these rooms. (4) Conclusions: The scarcity of literature available to draw information from for this review and possible impact on improving patient outcomes and quality of treatment in psychiatric units opens the door to future studies to better understand the efficacy of such rooms. Research into the ideal implementation tactics of such rooms, long-term outcomes, and the influence on diverse patient demographics could be areas of improvement in the use of alternative rooms.

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

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.

Associations between violence, self-harm and acute psychiatric service use: Implications for inpatient care

WHAT IS KNOWN ON THE SUBJECT?: Service users who behave violently may suffer. This is because violence can lead to unnecessary medication, seclusion on the ward and strained relationships with other people. It can also affect a service user's self-esteem and lead to feelings of shame. Service users who behave violently can also make life frightening and unpredictable for members of staff, and other service users on the ward. It is important to gain a sound understanding of violence in order to help reduce it. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Previous research has shown that having a past history of violence, being admitted to PICU and being admitted to hospital under section is associated with violent behaviour on the ward. We identified several new factors associated with violent behaviour, such as engaging in self-harm, being the target of another person's violence and being referred to a Psychiatric Liaison Team.

IMPLICATIONS FOR PRACTICE

For some service users, behaving violently may be a response to previous trauma and an expression of distress. It is therefore important that mental health nurses are supported by their employers to work with service users in a trauma-informed manner. Nurses employed across a range of psychiatric settings could benefit from direct interventions such as comprehensive trauma-informed care training and psychological debrief spaces, or systemic interventions to address staff shortages and improve ward conditions.

ABSTRACT

INTRODUCTION: Instances of violence in acute psychiatric settings are frequent, can be devastating for service users and staff, and are costly. Such settings would benefit from a greater understanding of violence.

AIM

We analysed the association between current and historical variables and rates of inpatient violence. To address gaps in current research, we included instances of self-harm and being the target of violence. We also included seldom used service metrics.

METHOD

Data were extracted on admissions to acute adult wards and PICUs 2017-2020 within South London and Maudsley NHS Foundation Trust. A zero-inflated negative binomial regression mixed model was used to analyse the impact of variables on rates of violence.

RESULTS

Variables associated with an increased rate of violence were as follows: an increased number of violent incidents in the year before admission, being admitted on MHA section, being admitted to PICU, instances of self-harm, being the target of violence and referral to a Psychiatric Liaison Team.

DISCUSSION

The novel associations found between enacting violence, self-harm and being the target of violence indicate trauma-informed care is crucial to reduce violent presentations of distress.

IMPLICATIONS FOR PRACTICE

System level interventions are crucial to ensure mental health nurses are supported to provide trauma-informed care.

Effects of Integrated Moral Reasoning Development Intervention for Management of Violence in Schizophrenia: A Randomized Controlled Trial

Moral cognition is an important and multidimensional, but often overlooked, determinant of violence. Very few interventions have systematically examined the role of moral reasoning, anger management and problem-solving together in violence. A randomized controlled trial was conducted to comprehensively evaluate the sustained effects of an integrated Moral Reasoning Development Intervention (MRDI) in the management of repetitive violence in schizophrenia. This study placed special emphasis on essential components related to moral reasoning and violence in patients with schizophrenia. Evaluations, including measures of violence, moral reasoning, ethical valuation and judgement, decision-making, conflict management style, and personality traits, were performed at baseline, end of intervention, and 1-month follow-up after intervention. We found that MRDI was superior to treatment-as-usual, in improving moral reasoning and related variables and violence outcomes (p < 0.05). In comparison with the treatment-as-usual group (n = 22), patients in the MRDI group (n = 21) showed improved levels of moral reasoning, with decreased levels of violent behaviors. The MRDI participants also experienced significantly greater improvements or changes (p < 0.05) in their ethical valuation and judgement, decision-making style and preferences, and conflict management style. Our findings provide important implications for risk assessment and violence management and prevention.

Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

BACKGROUND

Early to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.

METHOD

We enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.

RESULT

Our results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378-0.816)], having cigarette smoking [2.121 (1.191-3.779)], higher positive syndrome [1.016 (1.002-1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026-1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599-0.7748) had better prediction ability than that of other algorithms.

CONCLUSION

ML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

Effects of Integrated Violence Intervention on Alexithymia, Cognitive, and Neurocognitive Features of Violence in Schizophrenia: A Randomized Controlled Trial

Patients with schizophrenia and repetitive violence express core impairments that encompass multiple domains. To date, there have been few interventions integrating neurocognition, social cognition, alexithymia, and emotion regulation together as an approach to manage repetitive violence. The aim of this open-label randomized controlled trial was to examine more comprehensively the effectiveness of a novel Integrated Cognitive Based Violence Intervention Program on management of repetitive violence in patients with schizophrenia (vSZ). Sixty recruited patients were aged ≥20 years, diagnosed with schizophrenia for >2 years, had repetitive violent behavior within one year, and were psychiatrically hospitalized. The vSZ patients were randomly allocated to two groups and received either the intervention or treatment as usual. The intervention module, consisting of all defined 11 cognitive and social cognitive domains as well as emotion regulation, which were grouped into four modules. The intervention placed emphasis on the patients’ behavioral problems or intrinsic conflicts in relation to repetitive violence. The results indicate a statistically significant trend toward reducing impulsivity, anger with resentment, physical aggression, suspicion, and hostility (p < 0.05). The intervention significantly alleviated the intensity of cognitive failure, improved the management of alexithymic features and attribution styles and errors, and fostered adequate decision-making styles and emotion regulation capacity (p < 0.05). The intervention, when applied in conjunction with psychiatric standard care, could exert synergistic effects on alexithymia and cognitive, clinical, and neurocognitive features of repetitive violence in schizophrenia. This intervention provided patients a more active role to manage their violent behavior with the involvement of alexithymia.

[Individual and contextual factors associated with violent behaviours during psychiatric hospitalizations]

BACKGROUND

The prevention of Physical Violent Behavior (VB) toward others during psychiatric hospitalization is a major concern of clinicians. These VBs can have a deleterious impact on the victims, inpatients or caregivers, as well as on the therapeutic milieu. Such violence can also have negative consequences for the assailant patients, such as repeatedly being hospitalized under restraint, stigmatization, and difficulties reintegrating into the community.

OBJECTIVES

This study explored individual (age, gender, marital status, living status, diagnostic) and institutional (type of admission, length of stay, number of previous hospitalizations) risk factors, and how their interactions could increase the risk of VB during psychiatric hospitalizations.

METHOD

The study was carried out over a period of four years in the psychiatry department of the Lausanne University Hospital, on the 15 wards (219 beds) specialized in acute psychiatric care for adults. All the patients admitted to one of these wards during this period (n=4518), aged between 18 and 65 years, were included in the study. The sample was divided in two groups: non-violent patients (NVPs) and violent patients (VPs). VBs, defined as physical aggressions against another person, were assessed by the Staff Observation Aggression Scale - Revised (SOAS - R). Only physical assaults, associated or not with other types of violence, involving hospitalized patients were analyzed. Personal and institutional factors were extracted from the hospital database. Chi² independence tests were used to assess differences between groups. Logistic regression models were used to identify the links between each factor and the VB. Classification and regression trees were used to study the hierarchical effect of factors, and combinations of factors, on VBs.

RESULTS

During the study period, 414 VBs were reported involving 199 patients (4.40 % of all patients). VPs were significantly younger, male, more likely to be unmarried and living in sheltered housing before hospitalization. In this group, the proportion of patients with diagnoses of schizophrenia, and/or schizophrenia with comorbid substance abuse and cognitive impairment, were higher compared to NVPs. VPs were more frequently admitted involuntarily, had a longer average length of stay and a greater number of previous hospitalizations. The logistic regression model performed on individual factors have shown a significant link between age (OR=0.99; CI: 0.97-1.00; P-value=0.024), living in sheltered housing before admission (OR=2.46; CI: 1.61-3.75; P-value<0.000), schizophrenic disorders (OR=2.18; CI: 1.35-3.57; P-value=0.001), schizophrenic disorders with substance abuse comorbidity (OR=2.00; CI: 1.16-3.37; P-value=0.016), cognitive impairment (OR=3.41; CI: 1,21-8.25; P-value=0.010), and VBs. The logistic regression model on institutional factors have shown a significant link between involuntary hospitalization (OR=4.38; CI: 3.20-6.08; P-value<0.000), length of previous stay (OR=1.01; CI: 1.00-1.01; P-value<0.000), number of previous hospitalizations (OR=1.06; CI: 1.00-1.12; P-value=0.031), and VBs. The logistic regression model on individual and institutional factors have shown a significant link between age (OR=0.99; CI: 0.97-1.00; P-value=0.008), living in sheltered housing before admission (OR=2.46: CI: 1.61-3.75; P-value=0.034), cognitive impairment (OR=3.41; CI: 1.21-8.25; P-value=0.074), involuntary hospitalization (OR=3.46; CI: 2.48-4.87; P-value<0.000), length of previous stay (OR=1.01; CI: 1.00-1.01; P-value<0.000), and VBs. The classification and regression trees have shown that the relationship between long length of stay and repeated hospitalizations mainly potentiate the risk of violence.

CONCLUSION

The results of this study have shown the existence of a small group of vulnerable patients who accumulate constrained hospital stays during which violence occurs. Exploring the clinical profiles and institutional pathways of patients could help to better identify these patients and promote a more appropriate mode of support, such as intensive clinical case management. This model could facilitate the development of a clinical network and the links between the structures and partners caring for a patient. This would create a continuous support, avoiding or limiting the lack of continuity of care and care disruption.