Impact of COVID-19 on U.S. and Canadian neurologists’ therapeutic approach to multiple sclerosis: a survey of knowledge,attitudes, and practices

Journal of Neurology - To report the understanding and decision-making of neuroimmunologists and their treatment of patients with multiple sclerosis (MS) during the early stages of the SARS-CoV-2...     

Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study

Background and Study AimThe study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predicts the extent of liver fibrosis.Patients and MethodsThis retrospective multicenter study included 69,106 patients with chronic hepatitis C planned for antiviral therapy from January 2010–December 2014 with liver biopsy results. FIB-4 and APRI scores were calculated and their performance for predicting significant liver fibrosis (F3–F4) assessed against the Metavir scoring system. ML was used for feature selection and reduction to identify the most relevant attributes (CfsSubseteval/best first) for prediction.ResultsIn this study, 57,492 (83.2%) patients were F0–F2, and 11,615 (16.8%) patients were F3–F4. The revalidation of FIB-4 and APRI showed lower accuracy and higher disagreement with the biopsy results, with AUCs of 0.68 and 0.58, respectively. FIB-4 diagnosed fewer (14%) F3–F4 patients, and the high specificity and negative predictive values of FIB-4 and APRI reflected the low prevalence of F3–F4 in the study population. Out of 15 attributes, age (>35 years), AFP (>6.5 ng/ml), and platelet count (<150,000/mm³) were the most relevant risk attributes, and patients with one or more of these risk factors were likely to be F3–F4, with a classification accuracy of ≤ 92% and receiver operating characteristics area of 0.74.ConclusionFIB-4 and APRI scores were not very accurate and missed diagnosing most of the F3–F4 patients. ML implementation improved medical decisions and minimized the required clinical data to three risk factors.     

Efficacy of UVC-treated,pathogen-reduced platelets versus untreated platelets: a randomized controlled non-inferiority trial

Pathogen reduction (PR) technologies for blood components have been established to reduce the residual risk of known and emerging infectious agents. THERAFLEX UV-Platelets, a novel ultraviolet C (UVC) light-based PR technology for platelet concentrates, works without photoactive substances. This randomized, controlled, double-blind, multicenter, non-inferiority trial was designed to compare the efficacy and safety of UVC-treated platelets to that of untreated platelets in thrombocytopenic patients with hematologic-oncologic diseases. The primary objective was to determine non-inferiority of UVC-treated platelets, assessed by the 1-hour corrected count increment (CCI) in up to eight per-protocol platelet transfusion episodes. Analysis of the 171 eligible patients showed that the defined non-inferiority margin of 30% of UVC-treated platelets was narrowly missed as the mean differences in 1-hour CCI between standard platelets versus UVC-treated platelets for intention-to-treat and per-protocol analyses were 18.2% (95% Confidence Interval [CI]: 6.4-30.1) and 18.7% (95% CI: 6.3-31.1), respectively. In comparison to the control, the UVC group had a 19.2% lower mean 24-hour CCI and was treated with an about 25% higher number of platelet units, but the average number of days to the next platelet transfusion did not differ significantly between both treatment groups. The frequency of low-grade adverse events was slightly higher in the UVC group and the frequencies of refractoriness to platelet transfusion, platelet alloimmunization, severe bleeding events, and red blood cell transfusions were comparable between groups. Our study suggests that transfusion of pathogen-reduced platelets produced with the UVC technology is safe but non-inferiority was not demonstrated. (clinicaltrials gov. Identifier: DRKS00011156).