Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
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There has been a dramatic change in the pattern of patients being seen in hospitals and surgeries performed during the ongoing COVID-19 pandemic. The objective of this study is to study the change in the volume and spectrum of surgeries performed during the ongoing COVID-19 pandemic compared to pre-COVID-19 era. ⋯ The drastic decrease in the number of surgeries performed will result in large backlog of patients waiting for 'elective' surgery. There is a risk of these patients presenting at a later stage with progressed disease and the best way forward would be to resume work with necessary precautions and universal effective COVID-19 testing.
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The etiological agent of coronavirus disease-19 (COVID-19), SARS-coronavirus-2 (SARS-CoV-2), emerged in Wuhan, China, and quickly spread worldwide leading the World Health Organization (WHO) to recognize it not only as a pandemic but also as an important thread to public health. Beyond respiratory symptoms, new neurological manifestations are being identified such as headache, ageusia, anosmia, encephalitis or acute cerebrovascular disease. ⋯ Anti-herpes simplex virus (HSV) 1 and varicella-zoster IgM antibodies were not detected in serum samples and spinal and brain magnetic resonance imaging (MRI) showed no abnormal findings. This case remarks that COVID-19 nervous system damage could be caused by immune-mediated mechanisms.
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Neurological complications of coronavirus 2019 (COVID-19) are common, and novel manifestations are increasingly being recognized. Mild encephalopathy with reversible splenium lesion (MERS) is a syndrome that has been associated with viral infections, but not previously with COVID-19. ⋯ His symptoms resolved and the brain MRI findings completely normalized on repeat imaging, consistent with MERS. This case suggests that MERS may manifest as an autoimmune response to SARS-CoV-2 infection and should be considered in a patient with evidence of recent COVID-19 infection and the characteristic MERS clinico-radiological syndrome.
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Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data to predict the likely length of stay (LOS) for patients admitted with stroke. ⋯ Logistic regression was also the best performing model for predicting home vs non-home discharge destination (AUC 0.81). This study indicates that machine learning may aid in the prognostication of factors relevant to post-stroke discharge planning. Further prospective and external validation is required, as well as assessment of the impact of subsequent implementation.