A model to aid in the prediction of discharge location for stroke rehabilitation patients11No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated.

Agarwal, Vikas; McRae, Marc P.; Bhardwaj, Asha; Teasell, Robert

doi:10.1053/s0003-9993(03)00362-9

Cited by 49 publications

(16 citation statements)

References 20 publications

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“…In this regard, our results were comparable to those of other studies of predictors of functional outcome. Although there is controversy in the field of stroke research regarding predictors of stroke outcome, in most studies age and disability have a stronger association with negative outcome than neuropsychiatric symptoms and depressive complaints [8,10,31]. Interestingly, in our sample, neuropsychiatric symptoms and depressive complaints were significant factors to separate patients into the poor and good cluster, and age was not a significant factor.…”

Section: Discussioncontrasting

confidence: 51%

Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis

Buijck

Zuidema

Eijk

et al. 2012

BMC Health Serv Res

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BackgroundGeriatric stroke patients are generally frail, have an advanced age and co-morbidity. It is yet unclear whether specific groups of patients might benefit differently from structured multidisciplinary rehabilitation programs. Therefore, the aims of our study are 1) to determine relevant patient characteristics to distinguish groups of patients based on their admission scores in skilled nursing facilities (SNFs), and (2) to study the course of these particular patient-groups in relation to their discharge destination.MethodsThis is a longitudinal, multicenter, observational study. We collected data on patient characteristics, balance, walking ability, arm function, co-morbidity, activities of daily living (ADL), neuropsychiatric symptoms, and depressive complaints of 127 geriatric stroke patients admitted to skilled nursing facilities with specific units for geriatric rehabilitation after stroke.ResultsCluster analyses revealed two groups: cluster 1 included patients in poor condition upon admission (n = 52), and cluster 2 included patients in fair/good condition upon admission (n = 75). Patients in both groups improved in balance, walking abilities, and arm function. Patients in cluster 1 also improved in ADL. Depressive complaints decreased significantly in patients in cluster 1 who were discharged to an independent- or assisted-living situation. Compared to 80% of the patients in cluster 2, a lower proportion (46%) of the patients in cluster 1 were discharged to an independent- or assisted-living situation.ConclusionStroke patients referred for rehabilitation to SNFs could be clustered on the basis of their condition upon admission. Although patients in poor condition on admission were more likely to be referred to a facility for long-term care, this was certainly not the case in all patients. Almost half of them could be discharged to an independent or assisted living situation, which implied that also in patients in poor condition on admission, discharge to an independent or assisted living situation was an attainable goal. It is important to put substantial effort into the rehabilitation of patients in poor condition at admission.

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Section: Discussioncontrasting

confidence: 51%

Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis

Buijck

Zuidema

Eijk

et al. 2012

BMC Health Serv Res

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“…The presence or absence of specific patient-related factors such as advanced age, disability status, and pre-existing comorbid conditions may contribute to whether a patient will have a good or poor outcome following stroke. 19 20 21 22 Although there are currently no robust TACS data with which we can compare the present findings, they do share similarities with those from some previous research studies examining all stroke types. Langhorne et al 23 undertook a prospective, multicenter study and reported that most acute strokes are ischemic in nature ( n =277, 89%), and are suffered by those with advanced age (mean=76 years, IQR=70–82 years) and who are independent (prestroke mRS score=0–2; n =229, 74%).…”

Section: Discussionsupporting

confidence: 86%

A 6-Point TACS Score Predicts In-Hospital Mortality Following Total Anterior Circulation Stroke

et al. 2016

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Background and PurposeLittle is known about the factors associated with in-hospital mortality following total anterior circulation stroke (TACS). We examined the characteristics and comorbidity data for TACS patients in relation to in-hospital mortality with the aim of developing a simple clinical rule for predicting the acute mortality outcome in TACS.MethodsA routine data registry of one regional hospital in the UK was analyzed. The subjects were 2,971 stroke patients with TACS (82% ischemic; median age=81 years, interquartile age range=74–86 years) admitted between 1996 and 2012. Uni- and multivariate regression models were used to estimate in-hospital mortality odds ratios for the study covariates. A 6-point TACS scoring system was developed from regression analyses to predict in-hospital mortality as the outcome.ResultsFactors associated with in-hospital mortality of TACS were male sex [adjusted odds ratio (AOR)=1.19], age (AOR=4.96 for ≥85 years vs. <65 years), hemorrhagic subtype (AOR=1.70), nonlateralization (AOR=1.75), prestroke disability (AOR=1.73 for moderate disability vs. no symptoms), and congestive heart failure (CHF) (AOR=1.61). Risk stratification using the 6-point TACS Score [T=type (hemorrhage=1 point) and territory (nonlateralization=1 point), A=age (65–84 years=1 point, ≥85 years=2 points), C=CHF (if present=1 point), S=status before stroke (prestroke modified Rankin Scale score of 4 or 5=1 point)] reliably predicted a mortality outcome: score=0, 29.4% mortality; score=1, 46.2% mortality [negative predictive value (NPV)=70.6%, positive predictive value (PPV)=46.2%]; score=2, 64.1% mortality (NPV=70.6, PPV=64.1%); score=3, 73.7% mortality (NPV=70.6%, PPV=73.7%); and score=4 or 5, 81.2% mortality (NPV=70.6%, PPV=81.2%).ConclusionsWe have identified the key determinants of in-hospital mortality following TACS and derived a 6-point TACS Score that can be used to predict the prognosis of particular patients.

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“…The scarcity of allied health staff at the research sites over weekends suggested that day of admission may alter care. Patient outcomes following stroke have been associated with previous levels of independence and accommodation,22,23 comorbidity levels,24,25 and length of stay in the acute hospital 26,27. These factors may influence allied health staff decisions regarding care, for example, the priority given to early rehabilitation interventions.…”

Section: Methodsmentioning

confidence: 99%

Demographic and stroke-related factors as predictors of quality of acute stroke care provided by allied health professionals

Bernhardt¹,

Grimmer²

2011

JMDH

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Background:We recently indicated that patient age on its own is not a determinant of quality of allied health care received after an acute stroke. It has not been tested whether other non-age variables influence care decisions made by allied health professionals. This paper explores demographic and stroke-related variables that are putatively associated with the quality of care provided to acute stroke patients by allied health professionals.Methods:Data were retrospectively audited from 300 acute stroke patient records regarding allied health care. Compliance with each of 20 indicators of allied health care quality was established. The influence of various demographic and stroke-related variables on each performance indicator was examined. We undertook a series of analyses using univariate logistic regression models to establish the influence of these variables on care quality.Results:Patient age had a significant correlation with only one process indicator (early mobilization). Seven variables, including stroke severity and level of dependence, were associated with patient age. The majority of these age proxies had significant associations with process indicator compliance. Correlations between non-age variables, in particular stroke severity and comorbidity, suggest the potential for complex confounding relationships between non-age variables and quality of allied health care.Conclusion:Compliance with individual indicators of allied health care was significantly associated with variables other than patient age, and included stroke severity, previous independence, comorbidities, day of admission, stroke unit admission, and length of stay. The inter-relationships between these non-age variables suggest that their influence on quality of care is complex.

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Cited by 49 publications

References 20 publications

Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis

Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis

A 6-Point TACS Score Predicts In-Hospital Mortality Following Total Anterior Circulation Stroke

Demographic and stroke-related factors as predictors of quality of acute stroke care provided by allied health professionals

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