Digital Biomarkers最新文献

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones.

Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset.

Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images.

Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

Introduction: We aimed to assess the validity and reproducibility of a wearable hydration device in a cohort of maintenance dialysis patients.

Methods: We conducted a prospective, single-arm observational study on 20 haemodialysis patients between January and June 2021 in a single centre. A prototype wearable infrared spectroscopy device, termed the Sixty device, was worn on the forearm during dialysis sessions and nocturnally. Bioimpedance measurements were performed 4 times using the body composition monitor (BCM) over 3 weeks. Measurements from the Sixty device were compared with the BCM overhydration index (litres) pre- and post-dialysis and with standard haemodialysis parameters.

Results: 12 out of 20 patients had useable data. Mean age was 52 ± 12.4 years. The overall accuracy for predicting pre-dialysis categories of fluid status using Sixty device was 0.55 [K = 0.00; 95% CI: -0.39-0.42]. The accuracy for the prediction of post-dialysis categories of volume status was low [accuracy = 0.34, K = 0.08; 95% CI: -0.13-0.3]. Sixty outputs at the start and end of dialysis were weakly correlated with pre- and post-dialysis weights (r = 0.27 and r = 0.27, respectively), as well as weight loss during dialysis (r = 0.31), but not ultrafiltration volume (r = 0.12). There was no difference between the change in Sixty readings overnight and the change in Sixty readings during dialysis (mean difference 0.09 ± 1.5 kg), [t(39) = 0.38, p = 0.71].

Conclusion: A prototype wearable infrared spectroscopy device was unable to accurately assess changes in fluid status during or between dialysis sessions. In the future, hardware development and advances in photonics may enable the tracking of interdialytic fluid status.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Key messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Introduction: Kinematic analyses have recently revealed a strong potential to contribute to the assessment of neurological diseases. However, the validation of home-based kinematic assessments using consumer-grade video technology has yet to be performed. In line with best practices for digital biomarker development, we sought to validate webcam-based kinematic assessment against established, laboratory-based recording gold standards. We hypothesized that webcam-based kinematics would possess psychometric properties comparable to those obtained using the laboratory-based gold standards.

Methods: We collected data from 21 healthy participants who repeated the phrase "buy Bobby a puppy" (BBP) at four different combinations of speaking rate and volume: Slow, Normal, Loud, and Fast. We recorded these samples twice back-to-back, simultaneously using (1) an electromagnetic articulography ("EMA"; NDI Wave) system, (2) a 3D camera (Intel RealSense), and (3) a 2D webcam for video recording via an in-house developed app. We focused on the extraction of kinematic features in this study, given their demonstrated value in detecting neurological impairments. We specifically extracted measures of speed/acceleration, range of motion (ROM), variability, and symmetry using the movements of the center of the lower lip during these tasks. Using these kinematic features, we derived measures of (1) agreement between recording methods, (2) test-retest reliability of each method, and (3) the validity of webcam recordings to capture expected changes in kinematics as a result of different speech conditions.

Results: Kinematics measured using the webcam demonstrated good agreement with both the RealSense and EMA (ICC-A values often ≥0.70). Test-retest reliability, measured using the absolute agreement (2,1) formulation of the intraclass correlation coefficient (i.e., ICC-A), was often "moderate" to "strong" (i.e., ≥0.70) and similar between the webcam and EMA-based kinematic features. Finally, the webcam kinematics were typically as sensitive to differences in speech tasks as EMA and the 3D camera gold standards.

Discussion and conclusions: Our results suggested that webcam recordings display good psychometric properties, comparable to laboratory-based gold standards. This work paves the way for a large-scale clinical validation to continue the development of these promising technologies for the assessment of neurological diseases via home-based methods.

Continuous monitoring using commercial-grade wearable technology was used to quantify the physiological response to reported COVID-19 infections and vaccinations in five biometric measurements. Larger responses were observed following confirmed COVID-19 infection reported by unvaccinated versus vaccinated individuals. Responses following reported vaccination were smaller in both magnitude and duration compared to infection and mediated by both dose number and age. Our results suggest commercial-grade wearable technology as a potential platform on which to build screening tools for early detection of illness, including COVID-19 breakthrough cases.

Introduction: PRESENCE was a phase 2 clinical trial assessing the efficacy of mevidalen, a D1 receptor positive allosteric modulator, for symptomatic treatment of Lewy body dementia (LBD). Mevidalen demonstrated improvements in motor and non-motor features of LBD, global functioning, and actigraphy-measured activity and daytime sleep. Adverse events (AEs) of fall were numerically increased in mevidalen-treated participants.

Methods: A subset of PRESENCE participants wore a wrist actigraphy device for 2-week periods pre-, during, and posttreatment. Actigraphy sleep and activity measures were derived per period and analyzed to assess for their association with participants' reports of an AE of fall. Prespecified baseline and treatment-emergent clinical characteristics were also included in the retrospective analysis of falls. Independent-samples t test and χ² test were performed to compare the means and proportions between individuals with/without falls.

Results: A trend toward more falls was observed with mevidalen treatment (31/258 mevidalen-treated vs. 4/86 in placebo-treated participants: p = 0.12). Higher body mass index (BMI) (p < 0.05), more severe disease measured by baseline Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part II (p < 0.05), and a trend toward improved Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog₁₃) (p = 0.06) were associated with individuals with falls. No statistically significant associations with falls and treatment-emergent changes were observed.

Conclusion: The association of falls with worse baseline disease severity and higher BMI and overall trend toward improvements on cognitive and motor scales suggest that falls in PRESENCE may be related to increased activity in mevidalen-treated participants at greater risk for falling. Future studies to confirm this hypothesis using fall diaries and digital assessments are necessary.

Introduction: Digital health technologies (DHTs) provide opportunities for real-time data collection and assessment of patient function. However, use of DHT-derived endpoints in clinical trials to support medical product labelling claims is limited.

Methods: From November 2020 through March 2021, the Clinical Trials Transformation Initiative (CTTI) conducted a qualitative descriptive study using semi-structured interviews with sponsors of clinical trials that used DHT-derived endpoints. We aimed to learn about their experiences, including their interactions with regulators and the challenges they encountered. Using applied thematic analysis, we identified barriers to and recommendations for using DHT-derived endpoints in pivotal trials.

Results: Sponsors identified five key challenges to incorporating DHT-derived endpoints in clinical trials. These included (1) a need for additional regulatory clarity specific to DHT-derived endpoints, (2) the official clinical outcome assessment qualification process being impractical for the biopharmaceutical industry, (3) a lack of comparator clinical endpoints, (4) a lack of validated DHTs and algorithms for concepts of interest, and (5) a lack of operational support from DHT vendors.

Discussion/conclusion: CTTI shared the interview findings with the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and during a multi-stakeholder expert meeting. Based on these discussions, we provide several new and revised tools to aid sponsors in using DHT-derived endpoints in pivotal trials to support labelling claims.

Introduction: Little is known if, and to what extent, outpatient red blood cell (RBC) transfusions benefit chronic transfusion-dependent patients. Costs, labour, and potential side effects of RBC transfusions cause a restrictive transfusion strategy to be the standard of care. However, effects on the actual performance and quality of life of patients who require RBCs on a regular basis are hardly studied. The aim of this study was to assess if new technologies and techniques like wearable biosensor devices and web-based testing can be used to measure physiological changes, functional activity, and hence eventually better assess quality of life in a cohort of transfusion-dependent patients.

Methods: We monitored 5 patients who regularly receive transfusions during one transfusion cycle with the accelerateIQ biosensor platform, the Withings Steel HR, and web-based cognitive and quality of life testing.

Results: Data collection by the deployed devices was shown to be feasible; the AccelerateIQ platform rendered data of which 97.8% was of high quality and usable; of the data the Withings Steel HR rendered, 98.9% was of high quality and usable. Furthermore, heart rate decreased and cognition improved significantly following RBC transfusions. Activity and quality of life measures did not show transfusion-induced changes.

Conclusion: In a 5-patient cohort of transfusion-dependent patients, we found that the accelerateIQ, Withings Steel HR, and CANTAB platforms enable acquisition of high-quality data. The collected data suggest that RBC transfusions significantly and reversibly decrease heart rate and increase sustained attention in this cohort. This feasibility study justifies larger validation trials to confirm that these wearables can indeed help to determine personalized RBC transfusion strategies and thus optimization of each patient's quality of life.

Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation.

Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale.

Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline.

Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.