Association between <scp>non‐English</scp> language and use of physical and chemical restraints among medical inpatients with delirium

Reppas‐Rindlisbacher, Christina; Shin, Saeha; Purohit, Ushma; Verma, Amol A.; Razak, Fahad; Rochon, Paula A.; Sheehan, Kathleen; Rawal, Shail

doi:10.1111/jgs.17989

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…Patients with LEP are at increased risk for physical and chemical restraint use. 6 In our case, inability to consistently assess the etiology of agitation and discomfort and reorient the patient led to ineffective agitation management and suboptimal pharmacologic interventions.…”

Section: Discussionmentioning

confidence: 74%

Challenges in Assessment and Management of Postoperative Agitation and Delirium in a Stroke Patient with Limited English Proficiency: Case Report

Chwa,

Bradley,

Szokol

et al. 2024

A&A Practice

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While Title VI of the Civil Rights Act of 1964 mandates use of interpreters for patients with limited English proficiency, significant disparities persist in intensive postsurgical care. We present the case of a 60-year-old Vietnamese-speaking man with a Type A aortic dissection requiring postoperative mechanical ventilation and stroke care. Despite use of a remote video interpreter, our language-discordant nursing and physician providers faced challenges in managing agitation and delirium and assessing neurological function. This case highlights the need for adequate interpretation equipment, linguistic diversity among providers, and interventions to promote and enable consistent certified and professional medical interpreter use.

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Section: Discussionmentioning

confidence: 74%

Challenges in Assessment and Management of Postoperative Agitation and Delirium in a Stroke Patient with Limited English Proficiency: Case Report

Chwa,

Bradley,

Szokol

et al. 2024

A&A Practice

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“…Others have reported similar disparities in restraint use in in-patient and pediatrics settings, suggesting that this association is not limited to adult EDs. Similarly, racial disparities in chemical restraint use, and in analysis of primary language and risk of restraint use in EDs have been reported.…”

Section: Discussionmentioning

confidence: 99%

Racial Disparities in Emergency Department Physical Restraint Use

Eswaran,

Molina,

Hwong

et al. 2023

JAMA Intern Med

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ImportanceRecent studies have demonstrated that people of color are more likely to be restrained in emergency department (ED) settings compared with other patients, but many of these studies are based at a single site or health care system, limiting their generalizability.ObjectiveTo synthesize existing literature on risk of physical restraint use in adult EDs, specifically in reference to patients of different racial and ethnic backgrounds.Data SourcesA systematic search of PubMed, Embase, Web of Science, and CINAHL was performed from database inception to February 8, 2022.Study SelectionIncluded peer-reviewed studies met 3 criteria: (1) published in English, (2) original human participants research performed in an adult ED, and (3) reported an outcome of physical restraint use by patient race or ethnicity. Studies were excluded if they were conducted outside of the US, or if full text was unavailable.Data Extraction and SynthesisFour independent reviewers (V.E., M.M., D.D., and A.H.) abstracted data from selected articles following Meta-Analysis of Observational Studies in Epidemiology guidelines. A modified Newcastle-Ottawa scale was used to assess quality. A meta-analysis of restraint outcomes among minoritized racial and ethnic groups was performed using a random-effects model in 2022.Main Outcome(s) and Measure(s)Risk of physical restraint use in adult ED patients by racial and ethnic background.ResultsThe search yielded 1597 articles, of which 10 met inclusion criteria (0.63%). These studies represented 2 557 983 patient encounters and 24 030 events of physical restraint (0.94%). In the meta-analysis, Black patients were more likely to be restrained compared with White patients (RR, 1.31; 95% CI, 1.19-1.43) and to all non-Black patients (RR, 1.27; 95% CI, 1.23-1.31). With respect to ethnicity, Hispanic patients were less likely to be restrained compared with non-Hispanic patients (RR, 0.85; 95% CI, 0.81-0.89).Conclusions and RelevancePhysical restraint was uncommon, occurring in less than 1% of encounters, but adult Black patients experienced a significantly higher risk of physical restraint in ED settings compared with other racial groups. Hispanic patients were less likely to be restrained compared with non-Hispanic patients, though this observation may have occurred if Black patients, with a higher risk of restraint, were included in the non-Hispanic group. Further work, including qualitative studies, to explore and address mechanisms of racism at the interpersonal, institutional, and structural levels are needed.

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“…A systematic review of ICU delirium prediction models [2] found that while many models performed well, they only predicted the condition using transversal data from a single point in time, failing to take into account the dynamic nature of delirium. Additionnally, when patients speak a different language than their healthcare providers, the use of these instruments and questionnaires becomes even more challenging [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: a Derivation Study (Preprint)

Giguère,

Niaussat,

Noël-Hunter

et al. 2024

Preprint

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BACKGROUND Delirium is a prevalent condition in intensive care units (ICUs), often leading to adverse outcomes. Hypoactive delirium is particularly difficult to detect. Despite advancements, timely identification of hypoactive delirium remains challenging due to its dynamic nature, lack of human resources, lack of reliable monitoring tools, and subtle clinical signs that include hypovigilance. Machine learning detection models could support the identification of hypoactive delirium episodes by better detecting episodes of hypovigilance. OBJECTIVE In this study, we aim to develop a machine learning algorithm capable of detecting hypovigilance events using routinely collected physiological data in the ICU. METHODS This derivation study used prospective observational data collected from eligible ICU patients in Lévis, Québec. We included patients admitted to the ICU between October 2021 and June 2022 who were at least 18 years old and had an anticipated ICU stay of at least 48 hours. ICU nurses identified hypovigilant states every hour using the Richmond Agitation and Sedation Scale (RASS) or the Ramsay Sedation Scale (RSS). Routine vital signs (heart rate, respiratory rate, blood pressure, and oxygen saturation), as well as other physiological and clinical variables (premature ventricular contractions, intubation, use of sedative medication, and fever), were automatically collected using a GE CARESCAPE Gateway or manually collected through chart review. Time series were generated around hypovigilance episodes for analysis. Random Forest, XGBoost, and LightGBM classifiers were then used to detect hypovigilant episodes on the basis of analyzing time series. Hyperparameter optimization was performed using random search in a 10-fold group cross-validation setup. We report the results of this study using the TRIPOD+AI guidelines and potential biases were assessed using PROBAST. RESULTS Out of 146 potentially eligible participants, data from 30 patients (mean age: 69 years old; 63% male) were collected for analysis. Of the group, 30% were admitted to the ICU for surgical reasons. Following data preprocessing, the study included 1,493 hypovigilance episodes and 764 non-hypovigilant episodes. Among the three sets of models evaluated, LightGBM demonstrated the best performance. It achieved an average accuracy of 68% to detect hypovigilant episodes, with a precision of 76%, a recall of 74%, an AUC of 60%, and an F1 score of 69%. Notably, the model was particularly influenced by intubation, respiratory rate, and systolic blood pressure acquired non-invasively. CONCLUSIONS All of the classifiers showed promising precision and recall, with slightly different yet comparable performance in classifying hypovigilant episodes. Machine learning algorithms designed to detect hypovigilance have the potential to support early detection of hypoactive delirium in ICU patients.

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Association between non‐English language and use of physical and chemical restraints among medical inpatients with delirium

Cited by 11 publications

References 12 publications

Challenges in Assessment and Management of Postoperative Agitation and Delirium in a Stroke Patient with Limited English Proficiency: Case Report

Challenges in Assessment and Management of Postoperative Agitation and Delirium in a Stroke Patient with Limited English Proficiency: Case Report

Racial Disparities in Emergency Department Physical Restraint Use

Identifying Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: a Derivation Study (Preprint)

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