Autoimmune and Chronic Inflammatory Disease Patients with COVID-19.

OBJECTIVE: There are limited data on the impact of coronavirus disease 2019 (COVID-19) on hospitalized patients with autoimmune and chronic inflammatory disease (AICID) compared with patients who do not have AICID. We sought to evaluate whether patients with AICID who have confirmed COVID-19 presenting to the hospital are at higher risk of adverse outcomes compared with those patients without AICID who are infected with COVID-19 and whether immunosuppressive medications impact this risk. METHODS: We performed a multicenter retrospective cohort study with patients presenting to five hospitals in a large academic health system with polymerase chain reaction-confirmed COVID-19 infection. We evaluated the impact of having an AICID and class of immunosuppressive medication being used to treat patients with AICID (biologics, nonbiologic immunosuppressives, or systemic corticosteroids) on the risk of developing severe COVID-19 defined as requiring mechanical ventilation (MV) and/or death. RESULTS: A total of 6792 patients with confirmed COVID-19 were included in the study, with 159 (2.3%) having at least one AICID. On multivariable analysis, AICIDs were not significantly associated with severe COVID-19 (adjusted odds ratio [aOR] 1.3, 95% confidence interval [CI]: 0.9-1.8). Among patients with AICID, use of biologics or nonbiologic immunosuppressives did not increase the risk of severe COVID-19. In contrast, systemic corticosteroid use was significantly associated with an increased risk of severe COVID-19 (aOR 6.8, 95% CI: 2.5-18.4). CONCLUSION: Patients with AICID are not at increased risk of severe COVID-19 with the exception of those on corticosteroids. These data suggest that patients with AICID should continue on biologic and nonbiologic immunosuppression but limit steroids during the COVID-19 pandemic.

COVID-19 is not associated with worse long-term inflammatory bowel disease outcomes: a multicenter case-control study.

Background: Inflammatory bowel disease (IBD) is not associated with worse coronavirus disease 2019 (COVID-19) outcomes. However, data are lacking regarding the long-term impact of severe acute respiratory syndrome coronavirus 2 infection on the disease course of IBD. Objectives: We aimed to investigate the effect of COVID-19 on long-term outcomes of IBD. Design: We performed a multicenter case-control study of patients with IBD and COVID-19 between February 2020 and December 2020. Methods: Cases and controls were individuals with IBD with presence or absence, respectively, of COVID-19-related symptoms and confirmatory testing. The primary composite outcome was IBD-related hospitalization or surgery. Results: We identified 251 cases [ulcerative colitis (n = 111, 45%), Crohn's disease (n = 139, 55%)] and 251 controls, with a median follow-up of 394 days. The primary composite outcome of IBD-related hospitalization or surgery occurred in 29 (12%) cases versus 38 (15%) controls (p = 0.24) and on multivariate Cox regression, COVID-19 was not associated with increased risk of adverse IBD outcomes [adjusted hazard ratio (aHR): 0.84, 95% confidence interval [CI]: 0.44-1.42]. When stratified by infection severity, severe COVID-19 was associated with a numerically increased risk of adverse IBD outcomes (aHR: 2.43, 95% CI: 1.00-5.86), whereas mild-to-moderate COVID-19 was not (aHR: 0.68, 95% CI: 0.38-1.23). Conclusion: In this case-control study, COVID-19 did not have a long-term impact on the disease course of IBD. However, severe COVID-19 was numerically associated with worse IBD outcomes, underscoring the continued importance of risk mitigation and prevention strategies for patients with IBD during the ongoing COVID-19 pandemic.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results: We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±âˆ¼4%) and specificity of 77% (CI ±âˆ¼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion: We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion: Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

The future of telemedicine and wearable technology in IBD.

PURPOSE OF REVIEW: Telemedicine has quickly become an essential part of modern healthcare, particularly in the management of chronic conditions like inflammatory bowel disease. The purpose of this review is to describe the current use of telehealth, mobile applications and wearable devices in inflammatory bowel disease and potential future applications. RECENT FINDINGS: Telemedicine was increasingly used during the Coronavirus Disease 2019 pandemic. Virtual consultations allowed clinical care to continue despite pandemic-related restrictions without compromising the quality of care for patients with inflammatory bowel disease (IBD). It also benefits patients who would not have access to care due to financial or geographical barriers. Mobile applications allow patients with IBD to record disease activity among other metrics, allowing for earlier healthcare provider intervention. Wearable devices are increasingly being explored to monitor physiological indicators of disease activity and flare. SUMMARY: Telehealth and remote patient monitoring has been successfully integrated into the care of IBD patients. The advantages of these modalities include better access to specialist care and remote noninvasive disease monitoring. Careful consideration must be given to patient privacy, data protection and equitable access. These modalities have enormous potential to improve patient care through accurate consistent data collection and even the prediction of disease activity.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

BACKGROUND: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data.

BACKGROUND: The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. OBJECTIVE: This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. METHODS: HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. RESULTS: A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City's COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial. CONCLUSIONS: High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.