Risk factors of coercion among psychiatric inpatients: a nationwide register-based cohort study

Thomsen, Christoffer Torgaard; Starkopf, Liis; Hastrup, Lene Halling; Andersen, Per Kragh; Nordentoft, Merete; Benros, Michael E.

doi:10.1007/s00127-017-1363-3

Cited by 41 publications

(56 citation statements)

References 22 publications

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“…This semantic topic identifies patients with somatic diseases, who are admitted involuntarily because of a comorbid mental disorder. This may imply that these patients suffer from organic mental disorders (e.g., delirium and/or dementia), which are known risk factors for MR .…”

Section: Discussionmentioning

confidence: 99%

“…For each admission used in the training set, data from the three databases were collected including clinical information up to the first hour of the admission. Predictors known or hypothesized to be associated with risk of MR were extracted from the registers, such as age, sex, diagnosed mental disorders, number of prior involuntary psychiatric admissions, and out‐patient treatment. An explorative approach was adopted for inclusion/exclusion of predictors in order to preserve important interactions.…”

Section: Methodsmentioning

confidence: 99%

“…Reducing the use of MR in clinical practice relies on being able to identify patients at risk of being subjected to this procedure. Prior studies have identified several risk factors for MR including diagnosis (e.g., schizophrenia or organic mental disorder), male sex, living alone, and involuntary admission . Prior studies have also suggested that early identification of patients at high risk of being exposed to coercive measures in psychiatric hospitals may reduce the use of these measures .…”

Section: Introductionmentioning

confidence: 99%

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Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Danielsen

Fenger

Østergaard

et al. 2019

Acta Psychiatr Scand

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Objective Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first 3 days following admission could be predicted based on analysis of electronic health data available after the first hour of admission. Methods The dataset consisted of clinical notes from electronic health records from the Central Denmark Region and data from the Danish Health Registers from patients admitted to a psychiatric department in the period from 2011 to 2015. Supervised machine learning algorithms were trained on a randomly selected subset of the data and validated using an independent test dataset. Results A total of 5050 patients with 8869 admissions were included in the study. One hundred patients were mechanically restrained in the period between one hour and 3 days after the admission. A Random Forest algorithm predicted MR with an area under the curve of 0.87 (95% CI 0.79–0.93). At 94% specificity, the sensitivity was 56%. Among the ten strongest predictors, nine were derived from the clinical notes. Conclusions These findings open for the development of an early warning system that may guide interventions to reduce the use of MR.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Danielsen

Fenger

Østergaard

et al. 2019

Acta Psychiatr Scand

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“…Limitations to our study should also be considered. First, the exclusion of individuals born before 1985 in the predictor analysis yields a younger sample for these analyses; however, this is unlikely to affect the results as the risk of involuntary measures is highest in the first admission and decreases with subsequent admissions (Thomsen et al, ). Second, involuntary treatment in children and adolescents under 18 years of age might be underreported prior to 2012, as it was only after the summer of 2011 that treatment against the child's will, but with the parents' permission, was to be reported in the register.…”

Section: Discussionmentioning

confidence: 99%

“…Limitations to our study should also be considered. First, the exclusion of individuals born before 1985 in the predictor analysis yields a younger sample for these analyses; however, this is unlikely to affect the results as the risk of involuntary measures is highest in the first admission and decreases with subsequent admissions (Thomsen et al, 2017 The table displays the risk (Hazard Ratio) for a first episode with involuntary treatment in an admission with the presents of a current eating disorder diagnosis at the index admission. a High education is defined as more than high school.…”

Section: Definitions Of Measuresmentioning

confidence: 99%

A Danish register‐based study on involuntary treatment in anorexia nervosa

Clausen

Larsen

Bulik

et al. 2018

Intl J Eating Disorders

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Objective Involuntary treatment is controversial and widely debated, but remains a significant component of treatment for severe anorexia nervosa. Given how little is known about this topic, we describe the frequency of various involuntary measures in a national cohort of all patients diagnosed with anorexia nervosa. In a subsample of patients, we explored predictors of the first involuntary measure recorded. Method Descriptive statistics and Cox proportional hazard analyses were conducted using the national registers of Denmark covering the total population. Data from the National Patient Register and the Psychiatric Central Research Register including all psychiatric visits from 1969 onwards were merged with data from the National Register on Coercion covering 1999 onward. Involuntary measures registered between 2000 and 2013 were analyzed. Results A total of 4,727 patients with a diagnosis of anorexia nervosa representing 16,592 admissions were included. Eighteen percent experienced at least one involuntary measure. A variety of measures were used with tube feeding being the most frequent followed by mechanical restraint, involuntary medication, physical restraint, constant observation, and sedative medication. A subsample of 2% of AN patients had more than 100 involuntary measures recorded. The first recorded involuntary measure was predicted by most but not all psychiatric comorbidities, especially schizophrenia, autism spectrum, and personality disorders, older age at first diagnosis, and previous admissions. Discussion It is important to develop a more granular understanding of patients at risk of requiring involuntary treatment and to determine how best to treat them effectively with minimal use of involuntary measures.

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Post-incident debriefing for people with schizophrenia after coercive measures

Välimäki

Varpula

2023

Cochrane Database of Systematic Reviews

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Risk factors of coercion among psychiatric inpatients: a nationwide register-based cohort study

Abstract: From our nationwide data, we identified a broad range of risk factors associated with coercive measures. Our findings can assist researchers in identifying patients at risk of coercion and thereby help targeting new coercion reduction programs.

Cited by 41 publications

References 22 publications

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data

A Danish register‐based study on involuntary treatment in anorexia nervosa

Post-incident debriefing for people with schizophrenia after coercive measures

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