Objective To evaluate the role of sleep cyclic alternating pattern (CAP) in patients with isolated REM sleep behavior disorder (IRBD) and ascertain whether CAP metrics might represent a marker of phenoconversion to a defined neurodegenerative condition. Methods Sixty-seven IRBD patients were included and classified into patients who phenoconverted to a neurodegenerative disease (RBD converters: converter REM sleep behavior disorder [cRBD]; n = 34) and remained disease-free (RBD non-converters: non-converter REM sleep behavior disorder [ncRBD]; n = 33) having a similar follow-up duration. Fourteen age- and gender-balanced healthy controls were included for comparisons. Results Compared to controls, CAP rate and CAP index were significantly decreased in IRBD mainly due to a decrease of A1 phase subtypes (A1 index) despite an increase in duration of both CAP A and B phases. The cRBD group had significantly lower values of CAP rate and CAP index when compared with the ncRBD group and controls. A1 index was significantly reduced in both ncRBD and cRBD groups compared to controls. When compared to the ncRBD group, A3 index was significantly decreased in the cRBD group. The Kaplan-Meier curve applied to cRBD estimated that a value of CAP rate below 32.9% was related to an average risk of conversion of 9.2 years after baseline polysomnography. Conclusion IRBD is not exclusively a rapid eye movement (REM) sleep parasomnia, as non-rapid eye movement (non-REM) sleep microstructure can also be affected by CAP changes. Further studies are necessary to confirm that a reduction of specific CAP metrics is a marker of neurodegeneration in IRBD.

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The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use.

Objective.MRI fails to reveal hippocampal pathology in 30-50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE.Methods.We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted as well as FLAIR/T1 (intensity) features. The classifier was trained on 60 TLE patients (mean age: 35.6; 58% female) with histologically-verified hippocampal sclerosis (HS). Images were deemed as MRI-negative in 42% of cases based on neuroradiological reading (40% based on hippocampal volumetry). The predictive model automatically labelled patients as left or right TLE. Lateralization accuracy was compared to electro-clinical data, including side of surgery. Accuracy of the classifier was further assessed in two independent TLE cohorts with similar demographics and electro-clinical characteristics (n=57; 58% MRI-negative).Results.The overall lateralization accuracy was 93% (95%; CI 92% - 94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy both in the training (84%, area-under-the-curve (AUC): 0.95±0.02) and the validation cohorts (Cohort 1: 90%, AUC: 0.99; Cohort 2: 76%, AUC: 0.94).Conclusion.This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiological assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation.