Screening tests for aphasia in patients with stroke: a systematic review

Hachioui, Hanane El; Visch-Brink, Evy; Lau, Lonneke M. L. de; Sandt-Koenderman, Mieke W. M. E. van de; Nouwens, Femke; Koudstaal, Peter J.; Dippel, Diederik W.J.

doi:10.1007/s00415-016-8170-8

Cited by 83 publications

(89 citation statements)

References 34 publications

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“…This scale is used for rating communicative ability in spontaneous speech. The ScreeLing, an aphasia screening test designed to assess the core linguistic components semantics, phonology and syntax in the acute phase after onset, was also included in the model [ 18 – 20 ]. For detailed methods, results and discussion we refer to the original paper [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

Validation of a prediction model for long-term outcome of aphasia after stroke

et al. 2018

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BackgroundAbout 30% of stroke patients suffer from aphasia. As aphasia strongly affects daily life, most patients request a prediction of outcome of their language function. Prognostic models provide predictions of outcome, but external validation is essential before models can be used in clinical practice. We aim to externally validate the prognostic model from the Sequential Prognostic Evaluation of Aphasia after stroKe (SPEAK-model) for predicting the long-term outcome of aphasia caused by stroke.MethodsWe used data from the Rotterdam Aphasia Therapy Study – 3 (RATS-3), a multicenter RCT with inclusion criteria similar to SPEAK, an observational prospective study. Baseline assessment in SPEAK was four days after stroke and in RATS-3 eight days. Outcome of the SPEAK-model was the Aphasia Severity Rating Scale (ASRS) at 1 year, dichotomized into good (ASRS-score of 4 or 5) and poor outcome (ASRS-score < 4). In RATS-3, ASRS-scores at one year were not available, but we could use six month ASRS-scores as outcome. Model performance was assessed with calibration and discrimination.ResultsWe included 131 stroke patients with first-ever aphasia. At six months, 86 of 124 (68%) had a good outcome, whereas the model predicted 88%. Discrimination of the model was good with an area under the receiver operation characteristic curve of 0.87 (95%CI: 0.81–0.94), but calibration was unsatisfactory. The model overestimated the probability of good outcome (calibration-in-the-large α = − 1.98) and the effect of the predictors was weaker in the validation data than in the derivation data (calibration slope β = 0.88). We therefore recalibrated the model to predict good outcome at six months.ConclusionThe original model, renamed SPEAK-12, has good discriminative properties, but needs further external validation. After additional external validation, the updated SPEAK-model, SPEAK-6, may be used in daily practice to discriminate between patients with good and patients with poor outcome of aphasia at six months after stroke.Trial registrationRATS-3 was registered on January 13th 2012 in the Netherlands Trial Register: NTR3271. SPEAK was not listed in a trial registry.Electronic supplementary materialThe online version of this article (10.1186/s12883-018-1174-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Validation of a prediction model for long-term outcome of aphasia after stroke

et al. 2018

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“…Stroke prevalence among the older adults was reported 46 -73 per 1000 people (5). This rate increased due to prevalence of aging (6,7).…”

Section: Introductionmentioning

confidence: 99%

Lifestyle of the Elderly with Stroke: A Cross Sectional Study

et al. 2018

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Background: Nowadays, stroke is one of the most important and commonly diagnosed neurological diseases throughout the world. This disease annually causes numerous moralities and disabilities; its high prevalence is reported among the elderly. Objectives: The current study aimed at determining the lifestyle of the elderly with stroke in Ilam, Western Iran. Methods: The current descriptive, cross sectional study was conducted on 200 elderly people with stroke in Ilam, Western Iran by convenience sampling method. The study was conducted by the application of healthy lifestyle questionnaire with confirmed validity and reliability. After data collection, the data were analyzed with SPSS version 16 using descriptive statistics (mean and standard deviation) and inferential statistics (independent t test and ANOVA). Results: Findings of the current study showed that the majority of the studied elderly were male (n = 106, 53%) aged 65 -70 years (n = 93, 46.5%), educated up to middle school level (n = 112, 56%), with annual income range of $US72,000 to $US144,000 (n = 117, 58.5%), had no spouse (n = 111, 5.55%) and lived in the city (n = 115, 57.5%). Mean (± standard deviation; SD) of age was 75.56 ± 9.87 years, mean lifestyle score was 37.54 ± 3.91, 9.55 ± 1.79, 24.30 ± 3.36, 18.76 ± 1.79, and 28.04 ± 2.41 in the dimensions of prevention, physical activity and recreational activities and sport, healthy nutrition, stress management, and inter-personal relationships, respectively.Total score of the questionnaire was 118.20 ± 6.00, which indicated a medium score of healthy lifestyle in the elderly. Conclusions:Findings of the current study revealed that the lifestyle condition of the elderly with stroke was in medium level. It was suggested that the therapeutic and health teams, in particular nurses, intervene properly and use nursing care models to enhance the lifestyle of the elderly.

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“…One third of stroke patients suffer from aphasia (Brady, Kelly, Godwin, Enderby, & Campbell, 2016;Croquelois & Bogousslavsky, 2011). Since the disabling language problems in aphasia have significant impacts on the patient's quality of life, communicative and social functions as well as adding to the costs of stroke care, aphasia is essential to be recognized as soon as possible among stroke patients (Hachioui, Visch-Brink, Lau, Sandt-Koenderman, Nouwens, Koudstaal, & Dippel, 2017;Papanathanasiou, Coppens, & Davidson, 2017).…”

Section: Introductionmentioning

confidence: 99%

Adaptation of the Token Test in Standard Indonesian

Jap¹,

Arumsari²

2017

Makara Hubs-Asia

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Aphasia is a language disorder caused by focal brain injury. The Token Test is a tool to detect aphasic symptoms and measure aphasic severity in individuals who suffer brain damage causing language impairment. While Indonesia has a diagnostic test battery for aphasia (TADIR), it has yet to be able to quantify aphasic severity. In this study, we tested 49 individuals: 26 healthy adults, 7 non-aphasic post-stroke individuals, and 16 aphasic individuals. A series of tests were administered: the TADIR, Token Test, and the Verb and Sentence Test. The Token Test was sensitive enough to distinguish between the three groups and was also correlated with all other language tests including the TADIR.

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Screening tests for aphasia in patients with stroke: a systematic review

Cited by 83 publications

References 34 publications

Validation of a prediction model for long-term outcome of aphasia after stroke

Validation of a prediction model for long-term outcome of aphasia after stroke

Lifestyle of the Elderly with Stroke: A Cross Sectional Study

Adaptation of the Token Test in Standard Indonesian

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