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Richardson SJ, Cropp AD, Ellis SW, Gibbon J, Sayer AA, Witham MD. The interrelationship between multiple long-term conditions (MLTC) and delirium: a scoping review. Age Ageing 2024; 53:afae120. [PMID: 38965032 PMCID: PMC11223896 DOI: 10.1093/ageing/afae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Indexed: 07/06/2024] Open
Abstract
INTRODUCTION Delirium and multiple long-term conditions (MLTC) share numerous risk factors and have been shown individually to be associated with adverse outcomes following hospitalisation. However, the extent to which these common ageing syndromes have been studied together is unknown. This scoping review aims to summarise our knowledge to date on the interrelationship between MLTC and delirium. METHODS Searches including terms for delirium and MLTC in adult human participants were performed in PubMed, EMBASE, Medline, Psycinfo and CINAHL. Descriptive analysis was used to summarise findings, structured according to Synthesis Without Meta-analysis reporting guidelines. RESULTS After removing duplicates, 5256 abstracts were screened for eligibility, with 313 full-texts sought along with 17 additional full-texts from references in review articles. In total, 140 met inclusion criteria and were included in the final review. Much of the literature explored MLTC as a risk factor for delirium (n = 125). Fewer studies explored the impact of MLTC on delirium presentation (n = 5), duration (n = 3) or outcomes (n = 6) and no studies explored how MLTC impacts the treatment of delirium or whether having delirium increases risk of developing MLTC. The most frequently used measures of MLTC and delirium were the Charlson Comorbidity Index (n = 98/140) and Confusion Assessment Method (n = 81/140), respectively. CONCLUSION Existing literature largely evaluates MLTC as a risk factor for delirium. Major knowledge gaps identified include the impact of MLTC on delirium treatment and the effect of delirium on MLTC trajectories. Current research in this field is limited by significant heterogeneity in defining both MLTC and delirium.
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Affiliation(s)
- Sarah Joanna Richardson
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | | | | | - Jake Gibbon
- South Tyneside and Sunderland NHS Foundation Trust, South Shields, Tyne and Wear, UK
| | - Avan Aihie Sayer
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Miles David Witham
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
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Chen YF, Chang MY, Chow LH, Ma WF. Effectiveness of Music-Based Intervention in Improving Uncomfortable Symptoms in ICU Patients: An Umbrella Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111500. [PMID: 34770015 PMCID: PMC8582781 DOI: 10.3390/ijerph182111500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/18/2021] [Accepted: 10/29/2021] [Indexed: 12/01/2022]
Abstract
Background: Intensive care unit (ICU) patients experience multiple uncomfortable symptoms, which may be alleviated using music-based intervention, a nondrug treatment. This umbrella review aims to combine the data of systematic reviews and/or meta-analyses to evaluate the effectiveness of music-based intervention in improving uncomfortable symptoms in ICU patients. Methods: A comprehensive literature search was performed on the PubMed, Embase, Cochrane Library, Airiti Library, CINAHL, ProQuest, and Web of Science databases, and Epistemonikos. The search had no language restrictions, and articles on the improvement of symptoms using music-based intervention in adult ICU patients were included. This review protocol was registered on PROSPERO (CRD42021240327). Results: This umbrella review retrieved 5 systematic reviews and 41 original studies, including 39 randomized controlled trials, and 2 nonrandomized controlled trials. Diverse music was the most common music type used for music-based intervention, the intervention music was typically decided by the study participants (61%), and most subjects underwent one intervention session (78%). Furthermore, most music intervention sessions lasted for 30 min (44%). The positive results included decreased anxiety, decreased pain, decreased agitation, decreased anesthesia dose and sedative use, decreased chances of delirium, decreased feelings of uncomfort, and improved sleep quality. Conclusions: A systematic review on the effectiveness of music-based intervention in improving uncomfortable symptoms in ICU patients revealed that 20–30 min intervention sessions showed the best improvement in the uncomfortable symptoms in patients. This study provides a basis for using music-based intervention to relieve the uncomfortable symptoms in critically ill ICU patients, and a reference for empirical clinical practice.
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Affiliation(s)
- Yu-Fen Chen
- Department of Public Health, China Medical University, Taichung 406040, Taiwan;
- Department of Nursing, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Mei-Yu Chang
- Department of Nursing, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Lok-Hi Chow
- Department of Anesthesiology, Taipei Veterans General Hospital, Taipei 112201, Taiwan;
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Research Division, Center for Evidence-based Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Wei-Fen Ma
- Ph.D. Program for Health Science and Industry, China Medical University, Taichung 406404, Taiwan
- School of Nursing, China Medical University Hospital, Taichung 406040, Taiwan
- Department of Nursing, China Medical University Hospital, Taichung 404332, Taiwan
- Correspondence: ; Tel.: +886-4-2205-3366 (ext. 7107); Fax: +886-04-2299-0348
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Young M, Holmes N, Robbins R, Marhoon N, Amjad S, Neto AS, Bellomo R. Natural language processing to assess the epidemiology of delirium-suggestive behavioural disturbances in critically ill patients. CRIT CARE RESUSC 2021; 23:144-153. [PMID: 38045514 PMCID: PMC10692527 DOI: 10.51893/2021.2.oa1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: There is no gold standard approach for delirium diagnosis, making the assessment of its epidemiology difficult. Delirium can only be inferred though observation of behavioural disturbance and described with relevant nouns or adjectives. Objective: We aimed to use natural language processing (NLP) and its identification of words descriptive of behavioural disturbance to study the epidemiology of delirium in critically ill patients. Study design: Retrospective study using data collected from the electronic health records of a university-affiliated intensive care unit (ICU) in Melbourne, Australia. Participants: 12 375 patients Intervention: Analysis of electronic progress notes. Identification using NLP of at least one of a list of words describing behavioural disturbance within such notes. Results: We analysed 199 648 progress notes in 12 375 patients. Of these, 5108 patients (41.3%) had NLP-diagnosed behavioural disturbance (NLP-Dx-BD). Compared with those who did not have NLP-Dx-DB, these patients were older, more severely ill, and likely to have medical or unplanned admissions, neurological diagnosis, chronic kidney or liver disease and to receive mechanical ventilation and renal replacement therapy (P < 0.001). The unadjusted hospital mortality for NLP-Dx-BD patients was 14.1% versus 9.6% for patients without NLP-Dx-BD. After adjustment for baseline characteristics and illness severity, NLP-Dx-BD was not associated with increased risk of death (odds ratio [OR], 0.94; 95% CI, 0.80-1.10); a finding robust to multiple sensitivity, subgroups and time of observation subcohort analyses. In mechanically ventilated patients, NLP-Dx-BD was associated with decreased hospital mortality (OR, 0.80; 95% CI, 0.65-0.99) after adjustment for baseline severity of illness and year of admission. Conclusions: NLP enabled rapid assessment of large amounts of data identifying a population of ICU patients with typical high risk characteristics for delirium. Moreover, this technique enabled identification of previously poorly understood associations. Further investigations of this technique appear justified.
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Affiliation(s)
- Marcus Young
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Natasha Holmes
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
| | - Raymond Robbins
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
| | - Nada Marhoon
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
| | - Sobia Amjad
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia
| | - Ary Serpa Neto
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
| | - Rinaldo Bellomo
- Data Analytics Research and Evaluation (DARE) Centre, Austin Health and University of Melbourne, Melbourne, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, School of Publish Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Intensive Care, Austin Hospital, Melbourne, VIC, Australia
- Centre for Integrated Critical Care, School of Medicine, University of Melbourne, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, VIC, Australia
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