Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

Efficacy and safety of a single dose of casirivimab and imdevimab for the prevention of COVID-19 over an 8-month period: a randomised, double-blind, placebo-controlled trial

BACKGROUND

There is an unmet need for COVID-19 prevention in patient populations who have not mounted or are not expected to mount an adequate immune response to complete COVID-19 vaccination. We previously reported that a single subcutaneous 1200 mg dose of the monoclonal antibody combination casirivimab and imdevimab (CAS + IMD) prevented symptomatic SARS-CoV-2 infections by 81·4% in generally healthy household contacts of SARS-CoV-2-infected individuals over a 1-month efficacy assessment period. Here we present additional results, including the 7-month follow-up period (months 2-8), providing additional insights about the potential for efficacy in pre-exposure prophylaxis settings.

METHODS

This was a randomised, double-blind, placebo-controlled trial done in the USA, Romania, and Moldova in 2020-2021, before the emergence of omicron (B.1.1.529) and omicron-lineage variants. Uninfected and unvaccinated household contacts of infected individuals, judged by the investigator to be in good health, were randomly assigned (1:1) to receive 1200 mg CAS + IMD or placebo by subcutaneous injection according to a central randomisation scheme provided by an interactive web response system; randomisation was stratified per site by the test results of a local diagnostic assay for SARS-CoV-2 and age group at baseline. COVID-19 vaccines were prohibited before randomisation, but participants were allowed to receive COVID-19 vaccination during the follow-up period. Participants who developed COVID-19 symptoms during the follow-up period underwent RT-PCR testing. Prespecified endpoints included the proportion of previously uninfected and baseline-seronegative participants (seronegative-modified full analysis set) who had RT-PCR-confirmed COVID-19 in the follow-up period (post-hoc for the timepoints of months 2-5 and 6-8 only) and underwent seroconversion (ie, became seropositive, considered a proxy for any SARS-CoV-2 infections [symptomatic and asymptomatic]; prespecified up to day 57, post-hoc for all timepoints thereafter). We also assessed the incidence of treatment-emergent adverse events. This study is registered with ClinicalTrials.gov, NCT04452318.

FINDINGS

From July 13, 2020, to Oct 4, 2021, 2317 participants who were RT-PCR-negative for SARS-CoV-2 were randomly assigned, of whom 1683 (841 assigned to CAS + IMD and 842 assigned to placebo) were seronegative at baseline. During the entirety of the 8-month study, CAS + IMD reduced the risk of COVID-19 by 81·2% (nominal p<0·0001) versus placebo (prespecified analysis). During the 7-month follow-up period, protection was greatest during months 2-5, with a 100% relative risk reduction in COVID-19 (nominal p<0·0001; post-hoc analysis). Efficacy waned during months 6-8 (post-hoc analysis). Seroconversion occurred in 38 (4·5%) of 841 participants in the CAS + IMD group and in 181 (21·5%) of 842 in the placebo group during the 8-month study (79·0% relative risk reduction vs placebo; nominal p<0·0001). Six participants in the placebo group were hospitalised due to COVID-19 versus none who received CAS + IMD. Serious treatment-emergent adverse events (including COVID-19) were reported in 24 (1·7%) of 1439 participants receiving CAS + IMD and in 23 (1·6%) of 1428 receiving placebo. Five deaths were reported, none of which were due to COVID-19 or related to the study drugs.

INTERPRETATION

CAS + IMD is not authorised in any US region as of Jan 24, 2022, because data show that CAS + IMD is not active against omicron-lineage variants. In this study, done before the emergence of omicron-lineage variants, a single subcutaneous 1200 mg dose of CAS + IMD protected against COVID-19 for up to 5 months of community exposure to susceptible strains of SARS-CoV-2 in the pre-exposure prophylaxis setting, in addition to the post-exposure prophylaxis setting that was previously shown.

FUNDING

Regeneron Pharmaceuticals, F Hoffmann-La Roche, US National Institute of Allergy and Infectious Diseases, US National Institutes of Health.

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers

The Earable device is a behind-the-ear wearable originally developed to measure cognitive function. Since Earable measures electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG), it may also have the potential to objectively quantify facial muscle and eye movement activities relevant in the assessment of neuromuscular disorders. As an initial step to developing a digital assessment in neuromuscular disorders, a pilot study was conducted to determine whether the Earable device could be utilized to objectively measure facial muscle and eye movements intended to be representative of Performance Outcome Assessments, (PerfOs) with tasks designed to model clinical PerfOs, referred to as mock-PerfO activities. The specific aims of this study were: To determine whether the Earable raw EMG, EOG, and EEG signals could be processed to extract features describing these waveforms; To determine Earable feature data quality, test re-test reliability, and statistical properties; To determine whether features derived from Earable could be used to determine the difference between various facial muscle and eye movement activities; and, To determine what features and feature types are important for mock-PerfO activity level classification. A total of N = 10 healthy volunteers participated in the study. Each study participant performed 16 mock-PerfOs activities, including talking, chewing, swallowing, eye closure, gazing in different directions, puffing cheeks, chewing an apple, and making various facial expressions. Each activity was repeated four times in the morning and four times at night. A total of 161 summary features were extracted from the EEG, EMG, and EOG bio-sensor data. Feature vectors were used as input to machine learning models to classify the mock-PerfO activities, and model performance was evaluated on a held-out test set. Additionally, a convolutional neural network (CNN) was used to classify low-level representations of the raw bio-sensor data for each task, and model performance was correspondingly evaluated and compared directly to feature classification performance. The model's prediction accuracy on the Earable device's classification ability was quantitatively assessed. Study results indicate that Earable can potentially quantify different aspects of facial and eye movements and may be used to differentiate mock-PerfO activities. Specially, Earable was found to differentiate talking, chewing, and swallowing tasks from other tasks with observed F1 scores >0.9. While EMG features contribute to classification accuracy for all tasks, EOG features are important for classifying gaze tasks. Finally, we found that analysis with summary features outperformed a CNN for activity classification. We believe Earable may be used to measure cranial muscle activity relevant for neuromuscular disorder assessment. Classification performance of mock-PerfO activities with summary features enables a strategy for detecting disease-specific signals relative to controls, as well as the monitoring of intra-subject treatment responses. Further testing is needed to evaluate the Earable device in clinical populations and clinical development settings.

Developing a Novel Measurement of Sleep in Rheumatoid Arthritis: Study Proposal for Approach and Considerations

The development of novel digital endpoints (NDEs) using digital health technologies (DHTs) may provide opportunities to transform drug development. It requires a multidisciplinary, multi-study approach with strategic planning and a regulatory-guided pathway to achieve regulatory and clinical acceptance. Many NDEs have been explored; however, success has been limited. To advance industry use of NDEs to support drug development, we outline a theoretical, methodological study as a use-case proposal to describe the process and considerations when developing and obtaining regulatory acceptance for an NDE to assess sleep in patients with rheumatoid arthritis (RA). RA patients often suffer joint pain, fatigue, and sleep disturbances (SDs). Although many researchers have investigated the mobility of joint functions using wearable technologies, the research of SD in RA has been limited due to the availability of suitable technologies. We proposed measuring the improvement of sleep as the novel endpoint for an anti-TNF therapy and described the meaningfulness of the measure, considerations of tool selection, and the design of clinical validation. The recommendations from the FDA patient-focused drug development guidance, the Clinical Trials Transformation Initiative (CTTI) pathway for developing novel endpoints from DHTs, and the V3 framework developed by the Digital Medicine Society (DiMe) have been incorporated in the proposal. Regulatory strategy and engagement pathways are also discussed.