Evaluating an Advanced Practice Provider-Managed Coronavirus Disease 2019 Deterioration Program.

Background: COVID-19 changed how healthcare systems could provide quality healthcare to patients, safely. An urban healthcare system created an advanced practice provider (APP)-managed continuous remote patient monitoring (cRPM) program. Methods: A mixed-method study design focusing on the usable and feasible nature of the cRPM program. Both APP-guided interviews and online questionnaires were analyzed. Results: There was overwhelmingly positive APP feedback utilizing the remote monitoring solution including providing quality healthcare, detecting early clinical deterioration, and desiring to adapt the solution to other acute or chronic diseases. Implications: Understanding the clinical users' feedback on usability and feasibility of cRPM highlights the significance of rapid clinical assessment, urgent care escalation and provider accessibility.

Exploring physical and biological manifestations of burnout and post-traumatic stress disorder symptoms in healthcare workers: a scoping review protocol.

INTRODUCTION: The COVID-19 pandemic has strained the mental and physical well-being of healthcare workers (HCW). Increased work-related stress and limited resources have increased symptoms of anxiety, depression, insomnia and post-traumatic stress disorder (PTSD) in this population. Stress-related disorders have been strongly associated with long-term consequences, including cardiometabolic disorders, endocrine disorders and premature mortality. This scoping review aims to explore available literature on burnout, PTSD, and other mental health-associated symptoms in HCW to synthesise relationships with physiological and biological biomarkers that may be associated with increased risk of disease, creating an opportunity to summarise current biomarker knowledge and identify gaps in this literature. METHODS AND ANALYSIS: This scoping review uses the Arksey and O'Malley six-step scoping review methodology framework. The research team will select appropriate primary sources using a search strategy developed in collaboration with a health sciences librarian. Three reviewers will initially screen the title and abstracts obtained from the literature searches, and two reviewers will conduct independent reviews of full-text studies for inclusion. The research team will be reviewing literature focusing on which burnout and/or PTSD-associated physiological and biological biomarkers have been studied, the methodologies used to study them and the correlations between the biomarkers and HCW experiencing burnout/PTSD. Data extraction forms will be completed by two reviewers for included studies and will guide literature synthesis and analysis to determine common themes. ETHICS AND DISSEMINATION: This review does not require ethical approval. We expect results from this scoping review to identify gaps in the literature and encourage future research regarding improving biological and physiological biomarker research in HCW. Preliminary results and general themes will be communicated back to stakeholders. Results will be disseminated through peer-reviewed publications, policy briefs and conferences as well as presented to stakeholders to an effort to invest in HCW mental and physical health.

Exploring Physical and Biological Manifestations of Burnout and Post-Traumatic Stress Disorder Symptoms in Healthcare Workers: A Scoping Review Protocol.

Introduction: The COVID-19 pandemic has strained the mental and physical well-being of healthcare workers (HCW). Increased work-related stress and limited resources has increased symptoms of anxiety, depression, insomnia, and post-traumatic stress disorder (PTSD) in this population. Stress-related disorders have been strongly associated with long-term consequences including cardiometabolic disorders, endocrine disorders and premature mortality. This scoping review aims to explore available literature on burnout, PTSD, and other mental health-associated symptoms in HCW to synthesize relationships with physiological and biological biomarkers that may be associated with increased risk of disease, creating an opportunity to summarize current biomarker knowledge and identify gaps in this literature. Methods and Analysis: This scoping review uses the Arksey and O'Malley six-step scoping review methodology framework. The research team will select appropriate primary sources using a search strategy developed in collaboration with a health sciences librarian. Three reviewers will initially screen the title and abstracts obtained from the literature searches, and two reviewers will conduct independent reviews of full-text studies for inclusion. The research team will be reviewing literature focusing on which burnout and/or PTSD-associated physiological and biological biomarkers have been studied, the methodologies used to study them and the correlations between the biomarkers and HCW experiencing burnout/PTSD. Data extraction forms will be completed by two reviewers for included studies and will guide literature synthesis and analysis to determine common themes. Ethics and Dissemination: This review does not require ethical approval. We expect results from this scoping review to identify gaps in the literature and encourage future research regarding improving biologic and physiologic biomarker research in HCW. Preliminary results and general themes will be communicated back to stakeholders. Results will be disseminated through peer-reviewed publications, policy briefs, and conferences, as well as presented to stakeholders to an effort to invest in HCW mental and physical health. Strengths and Limitations of This Study: This will be the first scoping review to assess the current understanding of the biologic and physiological impact of burnout on healthcare workers. The target population is restricted to healthcare workers; however, identified research gaps may be used to guide future studies in other high-burnout occupations and industries.This scoping review will be guided by the Arksey and O'Malley six-step methodological framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review checklist.Both peer reviewed manuscript and pre-prints/abstracts will be evaluated, but studies that have not been peer reviewed will be notated in the summary table. Conference abstracts are excluded.Preliminary and final themes and results identified by this scoping review will be communicated to stakeholders, including hospital staff and HCW, to ensure agreement with our interpretation and to convey knowledge gained with our population of interest.This review will advance the field's current understanding of mechanisms connecting the burnout and pathogenic stress to biologic and physiologic outcomes in healthcare workers and provide researchers with gaps in the literature to inform opportunities for future research.

Researching COVID to Enhance Recovery (RECOVER) adult study protocol: Rationale, objectives, and design.

IMPORTANCE: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. METHODS: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. DISCUSSION: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options. REGISTRATION: NCT05172024.

Estimation of Respiratory Rate from Breathing Audio.

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods. Clinical Relevance- We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index.

BACKGROUND: During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets. OBJECTIVE: To this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ's end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed. METHODS: This is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on the development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. RESULTS: Our target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1-ß]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time-time to predict decompensation-and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale. CONCLUSIONS: Using machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19. TRIAL REGISTRATION: ClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27271.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

Effect of number of open charts on intercepted wrong-patient medication orders in an emergency department.

To reduce the risk of wrong-patient errors, safety experts recommend allowing only one patient chart to be open at a time. Due to the lack of empirical evidence, the number of allowable open charts is often based on anecdotal evidence or institutional preference, and hence varies across institutions. Using an interrupted time series analysis of intercepted wrong-patient medication orders in an emergency department during 2010-2016 (83.6 intercepted wrong-patient events per 100 000 orders), we found no significant decrease in the number of intercepted wrong-patient medication orders during the transition from a maximum of 4 open charts to a maximum of 2 (b = -0.19, P = .33) and no significant increase during the transition from a maximum of 2 open charts to a maximum of 4 (b = 0.08, P = .67). These results have implications regarding decisions about allowable open charts in the emergency department in relation to the impact on workflow and efficiency.

The kinematics of multifunctionality: comparisons of biting and swallowing in Aplysia californica.

What are the mechanisms of multifunctionality, i.e. the use of the same peripheral structures for multiple behaviors? We studied this question using the multifunctional feeding apparatus of the marine mollusk Aplysia californica, in which the same muscles mediate biting (an attempt to grasp food) and swallowing (ingestion of food). Biting and swallowing responses were compared using magnetic resonance imaging of intact, behaving animals and a three-dimensional kinematic model. Biting is associated with larger amplitude protractions of the grasper (radula/odontophore) than swallowing, and smaller retractions. Larger biting protractions than in swallowing appear to be due to a more anterior position of the grasper as the behavior begins, a larger amplitude contraction of protractor muscle I2, and contraction of the posterior portion of the I1/I3/jaw complex. The posterior I1/I3/jaw complex may be context-dependent, i.e. its mechanical context changes the direction of the force it exerts. Thus, the posterior of I1/I3 may aid protraction near the peak of biting, whereas the entire I1/I3/jaw complex acts as a retractor during swallowing. In addition, larger amplitude closure of the grasper during swallowing allows an animal to exert more force as it ingests food. These results demonstrate that differential deployment of the periphery can mediate multifunctionality.