Predicting Discharge to Institutional Long‐Term Care After Stroke: A Systematic Review and Metaanalysis

Burton, Jennifer; Ferguson, E.; Barugh, Amanda; Walesby, Katherine; MacLullich, Alasdair M.J.; Shenkin, Susan D.; Quinn, Terence J.

doi:10.1111/jgs.15101

Cited by 65 publications

(60 citation statements)

References 41 publications

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“…However, for the other fifteen prognostic factors, no significant association with discharge destination in our sample of frail and multimorbid older stroke patients could be identified. This is rather unexpected, because a significant relationship of these prognostic factors with discharge destination was observed in one or more previous studies among the general and/or older population of stroke patients [5][6][7][8][9][10][12][13][14][15][16][17][18][19][20][21][22].. The fact that our findings are inconsistent with current literature can be explained by several factors.…”

Section: Discussioncontrasting

confidence: 48%

“…1. Demographic characteristics: high age [5,6,13,14,16,17,19,20,22], non-white race [13], female sex [7,13,14,17]. 2.…”

Section: Introductionmentioning

confidence: 99%

“…3. Stroke related health status: stroke history [13,17], hemorrhagic stroke [ 7,13,17], more severe stroke [2,16,19,22], larger stroke volume [13,14,16], loss of consciousness [8,13,16,17,19], cognitive disability [6-10, 12-17, 19], neglect [5,7,8,14,16,17,19], apraxia [16,17,19], unawareness of illness [8,14,17], severe paralysis [8,14,16,17,19], impairment in movement [17,19,20] spasticity [8], disorientation in time and place [16,17,19], emotional problems [13,19], dysphagia [15,16], urinary and bowel incontinence [6,8,…”

Section: Introductionmentioning

confidence: 99%

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Factors associated with successful home discharge after inpatient rehabilitation in frail older stroke patients

et al. 2020

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Background: Stroke is a highly prevalent disease among older people and can have a major impact on daily functioning and quality of life. When community-dwelling older people are hospitalized due to stroke, discharge to an intermediate care facility for geriatric rehabilitation is indicated when return to the previous living situation is expected but not yet possible. However, a substantial proportion is still unable to return home after discharge and has to be admitted to a residential care setting. This study aims to identify which factors are associated with home discharge after inpatient rehabilitation among frail and multimorbid older stroke patients. Methods: This study is a longitudinal cohort study among 92 community-dwelling stroke patients aged 65 years or over. All patients were admitted to one of eight participating intermediate care facilities for geriatric rehabilitation, under the expectation to return home after rehabilitation. We examined whether 16 potentially relevant factors (age; sex; household situation before admission; stroke history; cardiovascular disorders; diabetes mellitus; multimorbidity; cognitive disability; neglect; apraxia; dysphagia; urinary and bowel incontinence; emotional problems; sitting balance; daily activity level; and independence in activities of daily living) measured at admission were associated with discharge to the former living situation. Logistic regression analysis was used for statistical analysis. Results: Mean age of the patients was 79.0 years (SD 6.4) and 51.1% was female. A total of 71 patients (77.1%) were discharged to the former living situation within 6 months after the start of geriatric rehabilitation. Of the 16 factors analysed, only a higher level of independence in activities of daily living at admission was significantly associated with home discharge. Conclusions: Our study shows that the vast majority of previously identified factors predicting home discharge among stroke patients, could not predict home discharge among a group of frail and multimorbid older persons admitted to geriatric rehabilitation. Only a higher level of independence in activities of daily living at admission was significantly related to home discharge. Additional insight in other factors that might predict home discharge after geriatric rehabilitation among this specific group of frail older stroke patients, is needed. Trial registration: ISRCTN ISRCTN62286281. Registered 19-3-2010.

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Section: Discussioncontrasting

confidence: 48%

“…1. Demographic characteristics: high age [5,6,13,14,16,17,19,20,22], non-white race [13], female sex [7,13,14,17]. 2.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factors associated with successful home discharge after inpatient rehabilitation in frail older stroke patients

et al. 2020

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“…Stroke is an important cause of death and the most frequent cause of adult-acquired disability, with up to 80% of stroke survivors suffering from upper-limb impairments that have a severe impact on the survivor's ability to perform daily activities and influence their quality of life [1,2]. Stroke survivors often compensate for the loss of motor function by adapting their movement patterns to incorporate additional degrees of freedom at other joints and body segments.…”

Section: Introductionmentioning

confidence: 99%

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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Background: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. Methods: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-anddown reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. Results: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. Conclusions: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

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“…Several studies have examined the clinical and social factors predicting post-stroke discharge destination but have not reached consensus on the key factors [4][5][6] . Systematic reviews have been conducted to synthesise the evidence of factors 7,8 , however, meta-analysis of results has been limited. No systematic review has specifically examined post-stroke cognitive impairment (PSCI) and discharge destination; rather PSCI has been examined as one of several predictors.…”

Section: Introductionmentioning

confidence: 99%

Systematic review and meta-analysis of the effect of cognitive impairment on the risk of admission to long-term care after stroke

et al. 2020

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Introduction: Admission to long-term care (LTC) post-stroke can be a significant source of costs. Studies evaluating the effect of cognitive impairment (CI) and dementia on risk of LTC admission post-stroke have not been systematically reviewed. The aim of this paper was to conduct a systematic review and meta-analysis of studies of the association between post-stroke CI/dementia and admission to LTC. Patients and methods: PubMed, PsycInfo and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched for peer-review articles in English published January 2000-June 2018. Included studies were population-based or hospital-based studies assessing the relationship between CI or dementia, and admission to LTC post-stroke. Abstracts were screened, followed by full-text review of potentially relevant articles. Relevant data was extracted using a standard form and the Crowe Critical Appraisal Tool was used for quality appraisal. Results were pooled using random-effects meta-analysis and heterogeneity was assessed using the I² statistic. Results: 18 articles were included in the review and 12 in a meta-analysis. 14/18 studies adjusted for covariates including functional impairment. Increased odds of admission to LTC was associated with post-stroke CI [Odds Ratio (CI 95%): 2.36 (1.18, 4.71), I²=77%] and post-stroke dementia [Odds Ratio (CI 95%): 2.58 (1.38 to 4.82), I²=60%]. Discussion and conclusion: Post-stroke CI and dementia increase odds of admission to LTC post-stroke, independent of functional impairment. This indicates the potential for interventions that reduce post-stroke CI and dementia to also reduce risk of admission to LTC post-stroke, and ultimately costs.

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Predicting Discharge to Institutional Long‐Term Care After Stroke: A Systematic Review and Metaanalysis

Cited by 65 publications

References 41 publications

Factors associated with successful home discharge after inpatient rehabilitation in frail older stroke patients

Factors associated with successful home discharge after inpatient rehabilitation in frail older stroke patients

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Systematic review and meta-analysis of the effect of cognitive impairment on the risk of admission to long-term care after stroke

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