Implications of Involuntary Psychiatric Admission: Health, Social, and Clinical Effects on Patients

Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review

OBJECTIVES

We aimed to examine the predictive accuracy of functioning, relapse or remission among patients with psychotic disorders, using machine learning methods. We also identified specific features that were associated with these clinical outcomes.

DESIGN

The methodology of this review was guided by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy.

DATA SOURCES

CINAHL, EMBASE, PubMed, PsycINFO, Scopus and ScienceDirect were searched for relevant articles from database inception until 21 November 2024.

ELIGIBILITY CRITERIA

Studies were included if they involved the use of machine learning methods to predict functioning, relapse and/or remission among individuals with psychotic spectrum disorders.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened the records from the database search. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies tool from Cochrane. Synthesised findings were presented in tables.

RESULTS

23 studies were included in the review, which were mostly conducted in the west (91%). Predictive summary area under the curve values for functioning, relapse and remission were 0.63-0.92 (poor to outstanding), 0.45-0.95 (poor to outstanding), 0.70-0.79 (acceptable), respectively. Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries).

CONCLUSION

Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. Future machine learning studies may want to focus on the inclusion of a broader swathe of variables including ecological momentary assessments, with a greater amount of good quality big data covering longer longitudinal illness courses and coupled with external validation of study findings.

PROSPERO REGISTRATION NUMBER

The review was registered on PROSPERO, ID: CRD42023441108.

Positive associations between mean ambient temperature and involuntary admissions to psychiatric facilities

BACKGROUND

Temperature increases in the context of climate change affect numerous mental health outcomes. One such relevant outcome is involuntary admissions as these often relate to severe (life)threatening psychiatric conditions. Due to a shortage of studies into this topic, relationships between mean ambient temperature and involuntary admissions have remained largely elusive.

AIMS

To examine associations between involuntary admissions to psychiatric institutions and various meteorological variables.

METHODS

Involuntary admissions data from 23 psychiatric institutions in the Netherlands were linked to meteorological data from their respective weather stations. Generalized additive models were used, integrating a restricted maximum likelihood method and thin plate regression splines to preserve generalizability and minimize the risk of overfitting. We thus conducted univariable, seasonally stratified, multivariable, and lagged analyses.

RESULTS

A total of 13,746 involuntary admissions were included over 21,549 days. In univariable and multivariable models, we found significant positive associations with involuntary admissions for ambient temperature and windspeed, with projected increases of up to 0.94% in involuntary admissions per degree Celsius temperature elevation. In the univariable analyses using all data, the strongest associations in terms of significance and explained variance were found for mean ambient temperature (p = 2.5 × 10-6, Variance Explained [r2] = 0.096%) and maximum ambient temperature (p = 8.65 × 10-4, r2 = 0.072%). We did not find evidence that the lagged associations explain the associations for ambient temperature better than the direct associations.

CONCLUSION

Mean ambient temperature is consistently but weakly associated with involuntary psychiatric admissions. Our findings set the stage for further epidemiological and mechanistic studies into this topic, as well as for modeling studies examining future involuntary psychiatric admissions.

Intervening factors between risk of violence and aggressive behaviours among forensic inpatients: a scoping review

BACKGROUND

Risk of violence is closely associated with aggression propensity. However, there is a lack of research to explain the mechanisms behind this association, especially among the patients of forensic secure facilities. This review aimed to identify and synthesize the available literature concerning the intervening factors (mediating or moderating factors) in the relationship between the risk of violence and aggressive behavior in forensic secure facilities.

METHODS

Two electronic academic databases were searched: Scopus and Web of Science (WoS) using specific keywords as search terms derived from the PCC framework with no specific time limit. The search strategy was developed based on the JBI Manual for Evidence Synthesis and utilised the PRISMA-ScR guidelines. Data on the risk of violence, intervening factors, and aggressive behavior were extracted from the included studies. Further analysis was performed whereby similar data were grouped and synthesised together.

RESULTS

The initial search produced 342 studies. However, only nine studies fulfilled the inclusion criteria. The nine studies included 1,068 adult forensic inpatients from various psychiatric hospitals. Only mediation studies reported significant mechanisms of influence between the risk of violence and aggressive behavior. It is postulated that the human agency factor may be the underlying factor that influences a person's functioning and the subsequent series of events between the risk of violence and aggression.

CONCLUSIONS

In light of the paucity of evidence in this area, a generalised conclusion cannot be established. More studies are warranted to address the gaps before conclusive recommendations can be proposed to the relevant stakeholders.

Involuntary Hospitalizations in an Italian Acute Psychiatric Ward: A 6-Year Retrospective Analysis

Purpose

We evaluated the differences between demographic (age, sex, nationality, employment, housing, schooling, support administrator), clinical (hospitalization reason, aggressive behaviour, length of hospitalization, psychiatric diagnosis and comorbidities, psychiatric medications, discharge destination, "revolving door" hospitalizations) and environmental (pre-and pandemic period) variables in voluntary (VHs) and involuntary hospitalizations (IHs) in an acute psychiatric ward during a 6-year period.

Patients and Methods

We retrospectively collected the selected variables concerning the hospitalizations of subjects over 18 years of age in the Service for Psychiatric Diagnosis and Care of Mental Health and Drug Abuse Department in Modena from 01/01/2017 to 31/12/2022.

Results

We observed a progressive and sharp reduction in the number of VHs (n = 1800; 61.41%) during the pandemic and a stability of IHs (n = 1131; 38.59%), which in 2022 became prevalent. We highlighted the following differences between VHs and IHs: an increase in hospitalization length in IHs (14.25 mean days ± 15.89 SD) in comparison with VHs (8.78 mean days ± 13.88 SD), which increased more during the pandemic; an increase in aggressive behavior in IHs, especially during the pandemic (Pearson Chi2 = 90.80; p = 0.000); a prevalence of schizophrenia and bipolar disorders (Pearson Chi2 = 283.63; p = 0.000) and more frequent maladaptive social conditions among subjects in IHs.

Conclusion

During the 6-year observation period, we underscored a trend of increasingly reduced recourse to VHs, whereas IHs increased even in the pandemic. Our results suggest that IHs in Psychiatry represented an extreme measure for treating the most severe psychopathological situations such as schizophrenia and bipolar disorders, characterized by aggressive behaviour and precarious social conditions, which needed longer stay than VHs, especially during the pandemic.

A Call for Transformation: Moving Away from Coercive Measures in Mental Health Care

Coercion is common practice in mental health care [...].

Exploring correlates of involuntary treatment in substance use disorders: a global systematic review and meta-analysis

Given the legislative heterogeneity about involuntary treatment and psychoactive substance users, we opted to perform a systematic review and meta-analysis of the correlates of involuntary substance use disorders (SUD) treatment across different countries. We conducted research on the Pubmed database, searching for involuntary SUD treatment data worldwide. The systematic review analysed a total of 36 articles and included a sample of 47,739 patients. Our review highlights the elevated risk of involuntary treatment among male, unmarried individuals with alcohol and/or opioid use disorders. Targeted preventive and therapeutic interventions should focus on addressing the underlying factors contributing to involuntary treatment, such as psychosis, aggressiveness, suicidal ideation, legal problems, and severe social exposure. By targeting these factors and providing comprehensive care, we can strive to improve outcomes and reduce the burden of substance use disorders in this vulnerable population. It is essential to critically examine and understand the factors contributing to the selection of patients for compulsory treatment. By doing so, we can identify potential gaps or inconsistencies in the current processes and work towards ensuring that decisions regarding compulsory treatment are based on sound clinical and ethical principles.

Predictive care: a protocol for a computational ethnographic approach to building fair models of inpatient violence in emergency psychiatry

INTRODUCTION

Managing violence or aggression is an ongoing challenge in emergency psychiatry. Many patients identified as being at risk do not go on to become violent or aggressive. Efforts to automate the assessment of risk involve training machine learning (ML) models on data from electronic health records (EHRs) to predict these behaviours. However, no studies to date have examined which patient groups may be over-represented in false positive predictions, despite evidence of social and clinical biases that may lead to higher perceptions of risk in patients defined by intersecting features (eg, race, gender). Because risk assessment can impact psychiatric care (eg, via coercive measures, such as restraints), it is unclear which patients might be underserved or harmed by the application of ML.

METHODS AND ANALYSIS

We pilot a computational ethnography to study how the integration of ML into risk assessment might impact acute psychiatric care, with a focus on how EHR data is compiled and used to predict a risk of violence or aggression. Our objectives include: (1) evaluating an ML model trained on psychiatric EHRs to predict violent or aggressive incidents for intersectional bias; and (2) completing participant observation and qualitative interviews in an emergency psychiatric setting to explore how social, clinical and structural biases are encoded in the training data. Our overall aim is to study the impact of ML applications in acute psychiatry on marginalised and underserved patient groups.

ETHICS AND DISSEMINATION

The project was approved by the research ethics board at The Centre for Addiction and Mental Health (053/2021). Study findings will be presented in peer-reviewed journals, conferences and shared with service users and providers.

Experiences of coercion amongst involuntary mental health care users in KwaZulu-Natal, South Africa

Background

Involuntary admission is a common practice globally. Previous international studies reported that patients experienced high levels of coercion, threats and a range of negative emotions. Little is known about the patients' experience in South Africa. The aim of this study was to describe the patient's experiences of involuntary admission at two psychiatric hospitals in KwaZulu-Natal.

Methods

A cross-sectional descriptive quantitative study of patients admitted involuntarily was conducted. Demographic information was extracted from clinical records and interviews were conducted with consenting participants at discharge. The MacArthur Perceived Coercion Scale, the MacArthur Negative Pressures Scale, and the MacArthur Procedural Justice Scale, of the MacArthur Admission Experience Survey (short form) were utilized to describe participants' experiences.

Results

This study comprised 131 participants. The response rate was 95.6%. Most participants (n = 96; 73%) experienced high levels of coercion and threats (n = 110; 84%) on admission. About half (n = 61; 46.6%) reported that they felt unheard. Participants reported feeling sad (n = 68; 52%), angry (n = 54; 41.2%), and confused (n = 56; 42.7%). There was a significant association between good insight and a feeling of relief (p = 0.001), and between poor insight and feelings of anger (p = 0.041).

Conclusion

The findings of this study confirm that most patients who were admitted involuntarily experienced high levels of coercion, threats, and exclusion from the decision-making process. Patient involvement and control of the decision-making process must be facilitated to improve clinical and overall health outcomes. The need for involuntary admission must justify the means.

Grounding co-writing: An analysis of the theoretical basis of a new approach in mental health care

This contribution aims to highlight the theoretical and epistemological premises of the co-writing experience, a practice where a clinician and a patient are mutually engaged in jointly or collaboratively writing a narrative related to the patient's experience. Unlike a typical set of therapeutic techniques, co-writing is based on sharing perspectives and meanings about the experience of crisis, recovery, and the therapeutic process. The paper identifies and briefly describes four non-clinical epistemological paradigms on which it is grounded: ethnography, values-based practice, narrative care, and phenomenology. Although they differ in several ways, at the same time, they seem to share some common features that the paper investigates and comments. For clinicians, nurses, researchers and Mental Health Service managers, attention to the users and to the improvement of their active roles represents not only a strategy for the empowerment of results, but also the access door to a different perspective which relies on a renewed conceptualization of the mental disease nature that may lead to overcoming the epistemic asymmetry between the 'expert' and the 'other' in favor of intersubjective dialogue.

Status and clinical influencing factors of involuntary admission in chinese patients with schizophrenia

BACKGROUND

Though controversial for its various disadvantages, involuntary admission (IA) is necessary in providing mental health care for patients suffering from schizophrenia in China. This article examines the IA rate in a representative sample, and under which circumstances are these patients more likely to be admitted involuntarily.

METHODS

Adult patients consecutively admitted to two typical hospitals in Shanghai between 2013 and 2014 with a diagnosis of ICD-10 schizophrenia were included. 2167 patients were included in this study. Sociodemographic and clinical data, as well as personal information of psychiatrists who made risk assessment, were collected. The whole sample was divided into voluntary and involuntary admission groups. Group comparisons were performed with SPSS 17.0, using one-way ANOVA, Wilcoxon rank sum test, Chi-squares and Logistic regression.

RESULTS

Among 2167 inpatients, the majority (2003, 92.4%) were involuntarily admitted. Clinical features, including age of patients (p < 0.001, OR = 1.037), lacking of insight (p < 0.001, OR = 3.691), were statistically significant for IA. Psychiatrist's age (p < 0.001, OR = 1.042) was independently associated with IA. However, risk behaviors had dramatically affected patients' admission status, of which the strongest predictor of IA was noncompliance with treatment (p < 0.001, OR = 3.597). The areas under the curve of the ROC and accuracy for the regression model were 0.815 and 0.927, respectively.

CONCLUSION

IA patients account for a major proportion of all those hospitalized with schizophrenia in China. Insights and risk behaviors contributed the most reasons for admission status of patients. This research shed light on necessity of further qualitative studies learning detailed evaluation processes of IA and high-quality interventional studies aiming to limit the performance of IA among patients with schizophrenia.