4 research outputs found

    Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

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    OBJECTIVES We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results

    Hemodynamic Features of Symptomatic Vertebrobasilar Disease

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    Background and purposeAtherosclerotic vertebrobasilar disease is an important cause of posterior circulation stroke. To examine the role of hemodynamic compromise, a prospective multicenter study, Vertebrobasilar Flow Evaluation and Risk of Transient Ischemic Attack and Stroke (VERiTAS), was conducted. Here, we report clinical features and vessel flow measurements from the study cohort.MethodsPatients with recent vertebrobasilar transient ischemic attack or stroke and ≥50% atherosclerotic stenosis or occlusion in vertebral or basilar arteries (BA) were enrolled. Large-vessel flow in the vertebrobasilar territory was assessed using quantitative MRA.ResultsThe cohort (n=72; 44% women) had a mean age of 65.6 years; 72% presented with ischemic stroke. Hypertension (93%) and hyperlipidemia (81%) were the most prevalent vascular risk factors. BA flows correlated negatively with percentage stenosis in the affected vessel and positively to the minimal diameter at the stenosis site (P<0.01). A relative threshold effect was evident, with flows dropping most significantly with ≥80% stenosis/occlusion (P<0.05). Tandem disease involving the BA and either/both vertebral arteries had the greatest negative impact on immediate downstream flow in the BA (43 mL/min versus 71 mL/min; P=0.01). Distal flow status assessment, based on an algorithm incorporating collateral flow by examining distal vessels (BA and posterior cerebral arteries), correlated neither with multifocality of disease nor with severity of the maximal stenosis.ConclusionsFlow in stenotic posterior circulation vessels correlates with residual diameter and drops significantly with tandem disease. However, distal flow status, incorporating collateral capacity, is not well predicted by the severity or location of the disease
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