Trajectories of Key Physiological Parameters in COVID-19 Patients Using Continuous Remote Monitoring and Health AI

Eisenkraft, Arik; Maor, Yasmin; Constantini, Keren; Goldstein, Nir; Nachman, Dean; Levy, Ran; Halberthal, Michael; Horowitz, Netanel A.; Golan, Ron; Rosenberg, Elli; Lavon, Eitan; Cohen, Ornit; Shapira, Guy; Shomron, Noam; Ishay, Arik Ben; Sand, Efrat; Merin, Roei; Fons, Meir; Littman, Romi; Gepner, Yftach

doi:10.21203/rs.3.rs-122775/v1

Cited by 4 publications

(4 citation statements)

References 45 publications

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“…Planned tests include HUTT (or supine/standing vital signs for those unable to complete HUTT), ambulatory EKG monitoring, and the COMPASS-31 survey for autonomic dysfunction. Volunteers will be monitored using an advanced automated ambulatory vital signs monitor recently approved by FDA in order to aid in diagnosis (60).…”

Section: Discussionmentioning

confidence: 99%

Comparative Cohort Study of Post-Acute Covid-19 Infection with a Nested, Randomized Controlled Trial of Ivabradine for Those With Postural Orthostatic Tachycardia Syndrome (The COVIVA Study)

Saunders

Arnold

Lavender

et al. 2023

Preprint

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Background: Significant clinical similarities have been observed between the recently described Long-Haul COVID-19 (LHC) syndrome, Postural Orthostatic Tachycardia Syndrome (POTS) and Inappropriate Sinus Tachycardia (IST). Shared symptoms include light-headedness, palpitations, tremulousness, generalized weakness, blurred vision, chest pain, dyspnea, brain-fog, and fatigue. Ivabradine is a selective sinoatrial node blocker FDA-approved for management of tachycardia associated with stable angina and heart failure not fully managed by beta blockers. In our study we aim to identify risk factors underlying LHC, as well as the effectiveness of ivabradine in controlling heart rate dysregulations and POTS/IST related symptoms. Methods/Design: A detailed prospective phenotypic evaluation combined with multi-omic analysis of 200 LHC volunteers will be conducted to identify risk factors for autonomic dysfunction. A comparator group of 50 volunteers with documented COVID-19 but without LHC will be enrolled to better understand the risk factors for LHC and autonomic dysfunction. Those in the cohort who meet diagnostic criteria for POTS or IST will be included in a nested prospective, randomized, placebo-controlled trial to assess the impact of ivabradine on symptoms and heart rate, assessed non-invasively based on physiologic response and ambulatory electrocardiogram. Additionally, studies on catecholamine production, mast cell and basophil degranulation, inflammatory biomarkers, and indicators of metabolic dysfunction will be measured to potentially provide molecular classification and mechanistic insights. Discussion: Optimal therapies for dysautonomia, particularly associated with LHC, have yet to be defined. In the present study, ivabradine, one of numerous proposed interventions, will be systematically evaluated for therapeutic potential in LHC-associated POTS and IST. Additionally, this study will further refine the characteristics of the LHC-associated POTS/IST phenotype, genotype and transcriptional profile, including immunologic and multi-omic analysis of persistent immune activation and dysregulation. The study will also explore and identify potential endotheliopathy and abnormalities of the clotting cascade.

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Section: Discussionmentioning

confidence: 99%

Comparative Cohort Study of Post-Acute Covid-19 Infection with a Nested, Randomized Controlled Trial of Ivabradine for Those With Postural Orthostatic Tachycardia Syndrome (The COVIVA Study)

Saunders

Arnold

Lavender

et al. 2023

Preprint

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“…The medical-grade monitoring devices used in the study employ reflective photoplethysmography (PPG) technology, capturing changes in tissue reflectance and measuring numerous vital signs including advanced hemodynamic parameters [ 19 , 20 , 21 , 22 , 23 ]. More specifically, the waveform is related to the reflection caused by the blood vessels when they change diameter.…”

Section: Methodsmentioning

confidence: 99%

Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial

Goldstein

Eisenkraft²,

Arguello

et al. 2021

JCM

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Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.

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“…Digital sensing products enable rapid assessment of health outcomes and support remote and longitudinal monitoring of patients with chronic diseases under daily living conditions [ 2 - 5 ]. During the COVID-19 pandemic, the utility of digital sensor technologies in clinical research [ 6 ], clinical care [ 7 ], and public health [ 8 , 9 ] have become even more apparent.…”

Section: Introductionmentioning

confidence: 99%

Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Shandhi¹,

Goldsack²,

Ryan³

et al. 2021

J Med Internet Res

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Background Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized. Objective We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research. Methods We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types. Results The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded. Conclusions Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.

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Trajectories of Key Physiological Parameters in COVID-19 Patients Using Continuous Remote Monitoring and Health AI

Cited by 4 publications

References 45 publications

Comparative Cohort Study of Post-Acute Covid-19 Infection with a Nested, Randomized Controlled Trial of Ivabradine for Those With Postural Orthostatic Tachycardia Syndrome (The COVIVA Study)

Comparative Cohort Study of Post-Acute Covid-19 Infection with a Nested, Randomized Controlled Trial of Ivabradine for Those With Postural Orthostatic Tachycardia Syndrome (The COVIVA Study)

Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial

Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

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