Oxfordshire Community Stroke Project Classification: A proposed automated algorithm. (June 2021)

• Record Type: Journal Article
• Authors: Andrade, Joao Brainer Clares de; Mohr, Jay P; Timbó, Felipe Brito; Nepomuceno, Camila Rodrigues; Moreira, João Vitor da Silva; Timbó, Isabelle da Costa Goes; Lima, Fabricio Oliveira; Silva, Gisele Sampaio; Bamford, John
• Is Part Of: European stroke journal. Volume 6:Number 2(2021)
• Page Start: 160
• Page End: 167
• Publication Date: 2021-06
• Subjects: Stroke -- Oxfordshire Community Stroke Project classification -- algorithm -- scale; Cerebrovascular disease -- Periodicals; 616.8005
• DOI: 10.1177/23969873211012136
• Languages: English
• ISSNs: 2396-9873

Abstract

Introduction: The Oxfordshire Community Stroke Project (OCSP) proposed a clinical classification for Stroke patients. This classification has proved helpful to predict the risk of neurological complications. However, the OCSP was initially based on findings on the neurological assesment, which can pose difficulties for classifying patients. We aimed to describe the development and the validation step of a computer-based algorithm based on the OCSP classification. Materials and methods: A flow-chart was created which was reviewed by five board-certified vascular neurologists from which a computer-based algorithm (COMPACT) was developed. Neurology residents from 12 centers were invited to participate in a randomized trial to assess the effect of using COMPACT. They answered a 20-item questionnaire for classifying the vignettes according to the OCSP classification. Each correct answer has been attributed to 1-point for calculating the final score. Results: Six-two participants agreed to participate and answered the questionnaire. Thirty-two were randomly allocated to use our algorithm, and thirty were allocated to adopt a list of symptoms alone. The group who adopted our algorithm had a median score of correct answers of 16.5[14.5, 17]/20 versus 15[13, 16]/20 points, p = 0.014. The use of our algorithm was associated with the overall rate of correct scores (p = 0.03). Discussion: Our algorithm seemed a useful tool for any postgraduate year Neurology resident. A computer-based algorithm may save time and improve the accuracy to classify these patients. Conclusion: An easy-to-use computer-based algorithm improved the accuracy of the OCSP classification, with the possible benefit of further improvement of the prediction of neurological complications and prognostication.