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Background: Hemorrhagic transformation (HT) is a common complication after ischemic stroke. It may be associated to poor outcomes. Some predictors of HT have been previously identified, but there remain controversies. Objective: To describe the risk factors for HT more frequently reported by a panel of experts surveyed for this project. Methods: We sent a standard questionnaire by e-mail to specialists in Vascular Neurology from 2014 to 2018. Forty-five specialists were contacted and 20 of them responded to the invitation. Predictors cited by three or more specialists were included in a table and ranked by the frequency in which they appeared. A review of the literature looking for published predictive scores of HT was performed, comparing to the answers received. Results: The 20 responding specialists cited 23 different risk factors for HT. The most frequent factors in the order of citation were the volume of ischemia, previous use of antithrombotic medication, neurological severity, age, hyperglycemia at presentation, hypertension on admission, and cardioembolism. Most variables were also found in previously published predictive scores, but they were reported by the authors with divergences of frequency. Conclusion: Although many studies have evaluated HT in patients with acute ischemic stroke, the published risk factors were neither uniform nor in agreement with those cited by the stroke specialists. These findings may be helpful to build a score that can be tested with the goal of improving the prediction of HT.

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.

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Introduction: Charcot-Marie-Tooth disease (CMT) is the most common hereditary neuropathy, with a diverse phenotypic and genotypic spectrum. The main clinical features are onset during infancy, slowly progressing symptoms and foot deformities, especially if there is a positive family history, although the lack of family awareness can be present. Over 70 distinct genes have been associated, however, their genetic diagnosis can be challenging, especially if we consider the fact that the same gene can transmit disease either dominantly or recessively. The aim to describe a case of CMT1F as a rare case of recessive demyelinating hereditary neuropathy. Clinical case: A 25-year-old woman, born of consanguineous parents, had history of distal weakness and burning sensation in lower limbs, with onset in infancy. Her childhood was marked for abnormal gait and falls, and evolved with foot deformities, requiring surgical corrections. The symptoms progressed slowly and reached upper limbs in few years. On physical evaluation was noted: muscle weakness of upper and lower limbs, predominantly distal, associated with atrophy, foot drop and absent reflexes. Electroneuromyography demonstrated signs of chronic demyelinating polyneuropathy. An initial sequencing analysis of the PMP22 gene was indicated, with normal results. A panel for neuropathies was performed, showing a homozygous frameshift mutation in NEFL (p.Lys362Glufs*2; c.1084_1085delAA), classified as probably pathogenic variant. Conclusion: CMT due to bi-allelic NEFL mutations is a rare condition that should be considered in hereditary demyelinating neuropathy, especially when recessive inheritance is suspected. Our study illustrates this condition and brings attention to the importance of the disponibility of high throughput genetic tests.