Impact of COVID-19 on the incidence of cardiac arrhythmias in implantable cardioverter defibrillator recipients followed by remote monitoring.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has been a fast-growing worldwide pandemic. AIMS: We aimed to investigate the incidence of cardiac arrhythmias among a large French cohort of implantable cardioverter defibrillator recipients over the first 5 months of 2020. METHODS: Five thousand nine hundred and fifty-four implantable cardioverter defibrillator recipients were followed by remote monitoring during the COVID-19 period (from 01 January to 31 May 2020). Data were obtained from automated remote follow-up of implantable cardioverter defibrillators utilizing the Implicity® platform. For all patients, the type of arrhythmia (atrial fibrillation, ventricular tachycardia or ventricular fibrillation), the number of ventricular arrhythmia episodes and the type of implantable cardioverter defibrillator-delivered therapy were recorded. RESULTS: A total of 472 (7.9%) patients presented 4917 ventricular arrhythmia events. An increase in ventricular arrhythmia incidence was observed after the first COVID-19 case in France, and especially during weeks #10 and #11, at the time of major governmental measures, with an increase in the incidence of antitachycardia pacing delivered therapy. During the 11 weeks before the lockdown order, the curve of the percentage of live-stream television coverage of COVID-19 information matched the ventricular arrhythmia incidence. During the lockdown, the incidence of ventricular arrhythmia decreased significantly compared with baseline (0.05±0.7 vs. 0.09±1.2 episodes per patient per week, respectively; P<0.001). Importantly, no correlation was observed between ventricular arrhythmia incidence and the curve of COVID-19 incidence. No changes were observed regarding atrial fibrillation/atrial tachycardia episodes over time. CONCLUSIONS: An increase in ventricular arrhythmia incidence was observed in the 2 weeks before the lockdown order, at the time of major governmental measures. Ventricular arrhythmia incidence decreased dramatically during the lockdown.

The Promise of Remote Patient Monitoring: Lessons Learned During the COVID-19 Surge in New York City.

The coronavirus pandemic catalyzed a digital health transformation, placing renewed focus on using remote monitoring technologies to care for patients outside of hospitals. At NewYork-Presbyterian, the authors expanded remote monitoring infrastructure and developed a COVID-19 Hypoxia Monitoring program-a critical means through which discharged COVID-19 patients were followed and assessed, enabling the organization to maximize inpatient capacity at a time of acute bed shortage. The pandemic tested existing remote monitoring efforts, revealing numerous operating challenges including device management, centralized escalation protocols, and health equity concerns. The continuation of these programs required addressing these concerns while expanding monitoring efforts in ambulatory and transitions of care settings. Building on these experiences, this article offers insights and strategies for implementing remote monitoring programs at scale and improving the sustainability of these efforts. As virtual care becomes a patient expectation, the authors hope hospitals recognize the promise that remote monitoring holds in reenvisioning health care delivery.

Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score.

INTRODUCTION: Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking. METHODS: Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290). RESULTS: 983 patients (median age 70, IQR 53-83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0-1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%). CONCLUSION: The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.