Test-Retest Reliability and Responsiveness of the Computerized Adaptive Test of Social Functioning in Persons With Stroke

Behavioral disinhibition in stroke

Background

Post-stroke behavioral disinhibition (PSBD) is common in stroke survivors and often presents as impulsive, tactless or vulgar behavior. However, it often remains undiagnosed and thus untreated, even though it can lead to a longer length of stay in a rehabilitation facility. The proposed study will aim to evaluate the clinical, neuropsychological and magnetic resonance imaging (MRI) correlates of PSBD in a cohort of stroke survivors and describe its 12-month course.

Methods

This prospective cohort study will recruit 237 patients and will be conducted at the Neurology Unit of the Prince of Wales Hospital. The project duration will be 24 months. The patients will be examined by multiple MRI methods, including diffusion-weighted imaging, within 1 week after stroke onset. The patients and their caregivers will receive a detailed assessment at a research clinic at 3, 9 and 15 months after stroke onset (T1, T2 and T3, respectively). The disinhibition subscale of the Frontal Systems Behavior Scale (FrSBe) will be completed by each subject and caregiver, and scores ≥65 will be considered to indicate PSBD.A stepwise logistic regression will be performed to assess the importance of lesions in the regions of interest (ROIs), together with other significant variables identified in the univariate analyses. For patients with PSBD at T1, the FrSBe disinhibition scores will be compared between the groups of patients with and without ROI infarcts, using covariance analysis. The demographic, clinical and MRI variables of remitters and non-remitters will be examined again at T2 and T3 by logistic regression.

Discussion

This project will be the first MRI study on PSBD in stroke survivors. The results will shed light on the associations of lesions in the orbitofrontal cortex, anterior temporal lobe and subcortical brain structures with the risk of PSBD. The obtained data will advance our understanding of the pathogenesis and clinical course of PSBD in stroke, as well as other neurological conditions. The findings are thus likely to be applicable to the large population of patients with neurological disorders at risk of PSBD and are expected to stimulate further research in this field.

Validation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Sample

IMPORTANCE

The machine learning-based Stroke Impact Scale (ML-SIS) is an efficient short-form measure that uses 28 items to provide domain scores comparable to those of the original 59-item Stroke Impact Scale-Third Edition (SIS 3.0). However, its utility is largely unknown because it has not been cross-validated with an independent sample.

OBJECTIVE

To examine the ML-SIS's comparability and test-retest reliability with that of the original SIS 3.0 in an independent sample of people with stroke.

DESIGN

Comparability was examined with the coefficient of determination (R2), mean absolute error, and root-mean-square error (RMSE). Test-retest reliability was examined using the intraclass correlation coefficient (ICC).

SETTING

Five hospitals in Taiwan.

PARTICIPANTS

Data of 263 persons with stroke were extracted from a previous study; 144 completed repeated assessments after a 2-wk interval.

RESULTS

High R2 (.87-.95) and low mean absolute error or RMSE (about 2.4 and 3.3) of the domain scores, except for the Emotion scores (R2 = .08), supported the comparability of the two measures. Similar ICC values (.39-.87 vs. .46-.87) were found between the two measures, suggesting that the ML-SIS is as reliable as the SIS 3.0.

CONCLUSIONS AND RELEVANCE

The ML-SIS provides scores mostly identical to those of the original measure, with similar test-retest reliability, except for the Emotion domain. Thus, it is a promising alternative that can be used to lessen the burden of routine assessments and provide scores comparable to those of the original SIS 3.0. Plain-Language Summary: The machine learning-based Stroke Impact Scale (ML-SIS) is as reliable as the original Stroke Impact Scale-Third Edition, except for the Emotion domain. Thus, the ML-SIS can be used to improve the efficiency of clinical assessments and also relieve the burden on people with stroke who are completing the assessments.