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

Abstract

Abstract 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.