Oxfordshire Community Stroke Project Classification: A proposed automated algorithm.

Autor:	de Andrade JBC; Department of Neurology, Universidade Federal de Sao Paulo, Sao Paulo, Brazil.; Columbia University, Doris and Stanley Tananbaum Stroke Center, New York, NY, USA., Mohr JP; Columbia University, Doris and Stanley Tananbaum Stroke Center, New York, NY, USA., Timbó FB; Department of Computational Science, Universidade Federal do Ceara, Fortaleza, Brazil., Nepomuceno CR; Department of Medicine, Universidade Estadual do Ceara, Fortaleza, Brazil., Moreira JVDS; Department of Neuroscience, SUNY Dowstate Medical Center, New York, NY; USA., Timbó IDCG; Department of Computational Science, Universidade Federal do Ceara, Fortaleza, Brazil., Lima FO; Department of Neurology, Hospital Geral de Fortaleza, Fortaleza, Brazil., Silva GS; Department of Neurology, Universidade Federal de Sao Paulo, Sao Paulo, Brazil., Bamford J; Department of Neurology, University of Leeds, Leeds, UK.
Jazyk:	angličtina
Zdroj:	European stroke journal [Eur Stroke J] 2021 Jun; Vol. 6 (2), pp. 160-167. Date of Electronic Publication: 2021 Jun 18.
DOI:	10.1177/23969873211012136
Abstrakt:	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. Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© European Stroke Organisation 2021.)
Databáze:	MEDLINE
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