Lessons learned: A look back at the performance of nine COVID-19 serologic assays and their proposed utility.

BACKGROUND: Serologic assays for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been proposed to assist with the acute diagnosis of infection, support epidemiological studies, identify convalescent plasma donors, and evaluate vaccine response. METHODS: We report an evaluation of nine serologic assays: Abbott (AB) and Epitope (EP) IgG and IgM, EUROIMMUN (EU) IgG and IgA, Roche anti-N (RN TOT) and anti-S (RS TOT) total antibody, and DiaSorin (DS) IgG. We evaluated 291 negative controls (NEG CTRL), 91 PCR positive (PCR POS) patients (179 samples), 126 convalescent plasma donors (CPD), 27 healthy vaccinated donors (VD), and 20 allogeneic hematopoietic stem cell transplant (HSCT) recipients (45 samples). RESULTS: We observed good agreement with the method performance claims for specificity (93-100%) in NEG CTRL but only 85% for EU IgA. The sensitivity claims in the first 2 weeks of symptom onset was lower (26-61%) than performance claims based on > 2 weeks since PCR positivity. We observed high sensitivities (94-100%) in CPD except for AB IgM (77%), EP IgM (0%). Significantly higher RS TOT was observed for Moderna vaccine recipients then Pfizer (p-values < 0.0001). A sustained RS TOT response was observed for the five months following vaccination. HSCT recipients demonstrated significantly lower RS TOT than healthy VD (p < 0.0001) at dose 2 and 4 weeks after. CONCLUSIONS: Our data suggests against the use of anti-SARS-CoV-2 assays to aid in acute diagnosis. RN TOT and RS TOT can readily identify past-resolved infection and vaccine response in the absence of native infection. We provide an estimate of expected antibody response in healthy VD over the time course of vaccination for which to compare antibody responses in immunosuppressed patients.

Atrial Fibrillation (AFIB) in the ICU: Incidence, Risk Factors, and Outcomes: The International AFIB-ICU Cohort Study.

OBJECTIVES: To assess the incidence, risk factors, and outcomes of atrial fibrillation (AF) in the ICU and to describe current practice in the management of AF. DESIGN: Multicenter, prospective, inception cohort study. SETTING: Forty-four ICUs in 12 countries in four geographical regions. SUBJECTS: Adult, acutely admitted ICU patients without a history of persistent/permanent AF or recent cardiac surgery were enrolled; inception periods were from October 2020 to June 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We included 1,423 ICU patients and analyzed 1,415 (99.4%), among whom 221 patients had 539 episodes of AF. Most (59%) episodes were diagnosed with continuous electrocardiogram monitoring. The incidence of AF was 15.6% (95% CI, 13.8-17.6), of which newly developed AF was 13.3% (11.5-15.1). A history of arterial hypertension, paroxysmal AF, sepsis, or high disease severity at ICU admission was associated with AF. Used interventions to manage AF were fluid bolus 19% (95% CI 16-23), magnesium 16% (13-20), potassium 15% (12-19), amiodarone 51% (47-55), beta-1 selective blockers 34% (30-38), calcium channel blockers 4% (2-6), digoxin 16% (12-19), and direct current cardioversion in 4% (2-6). Patients with AF had more ischemic, thromboembolic (13.6% vs 7.9%), and severe bleeding events (5.9% vs 2.1%), and higher mortality (41.2% vs 25.2%) than those without AF. The adjusted cause-specific hazard ratio for 90-day mortality by AF was 1.38 (95% CI, 0.95-1.99). CONCLUSIONS: In ICU patients, AF occurred in one of six and was associated with different conditions. AF was associated with worse outcomes while not statistically significantly associated with 90-day mortality in the adjusted analyses. We observed variations in the diagnostic and management strategies for AF.

Introduction and transmission of SARS-CoV-2 lineage B.1.1.7, Alpha variant, in Denmark.

BACKGROUND: In early 2021, the SARS-CoV-2 lineage B.1.1.7 (Alpha variant) became dominant across large parts of the world. In Denmark, comprehensive and real-time test, contact-tracing, and sequencing efforts were applied to sustain epidemic control. Here, we use these data to investigate the transmissibility, introduction, and onward transmission of B.1.1.7 in Denmark. METHODS: We analyzed a comprehensive set of 60,178 SARS-CoV-2 genomes generated from high-throughput sequencing by the Danish COVID-19 Genome Consortium, representing 34% of all positive cases in the period 14 November 2020 to 7 February 2021. We calculated the transmissibility of B.1.1.7 relative to other lineages using Poisson regression. Including all 1976 high-quality B.1.1.7 genomes collected in the study period, we constructed a time-scaled phylogeny, which was coupled with detailed travel history and register data to outline the introduction and onward transmission of B.1.1.7 in Denmark. RESULTS: In a period with unchanged restrictions, we estimated an increased B.1.1.7 transmissibility of 58% (95% CI: [56%, 60%]) relative to other lineages. Epidemiological and phylogenetic analyses revealed that 37% of B.1.1.7 cases were related to the initial introduction in November 2020. The relative number of cases directly linked to introductions varied between 10 and 50% throughout the study period. CONCLUSIONS: Our findings corroborate early estimates of increased transmissibility of B.1.1.7. Both substantial early expansion when B.1.1.7 was still unmonitored and continuous foreign introductions contributed considerably to case numbers. Finally, our study highlights the benefit of balanced travel restrictions and self-isolation procedures coupled with comprehensive surveillance efforts, to sustain epidemic control in the face of emerging variants.

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of the mask will be identified with a raspberry pi, a camera module, and the operations of DL and Machine Learning (ML). The detected information will be sent to the cloud server with the mechanism of IoT for real-time data monitoring. The proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security,monitoring, and storage fromintruders. This research further includes an IoT-based mask detection scheme with signal bulbs, alarms, and notifications in the smartphone. To find the efficacy of the proposed method, a set of experiments has been enumerated and interpreted. This research work finds the highest accuracy of 99.95% in the detection and classification of facial masks. Some related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data accordingly.ASystemUsability Scale (SUS) has been accomplished to check the satisfaction level of use and found the SUS score of 77%. Further, a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed scheme. However, the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification. © 2022 Tech Science Press. All rights reserved.

In COVID-19 Patients Who Suffer In-Hospital Cardiac Arrest, Cardiopulmonary Resuscitation Outcomes May Be Impacted by Arrest Etiology and Local Pandemic Conditions.

OBJECTIVES: The utility and risks to providers of performing cardiopulmonary resuscitation after in-hospital cardiac arrest in COVID-19 patients have been questioned. Additionally, there are discrepancies in reported COVID-19 in-hospital cardiac arrest survival rates. We describe outcomes after cardiopulmonary resuscitation for in-hospital cardiac arrest in two COVID-19 patient cohorts. DESIGN: Retrospective cohort study. SETTING: New York-Presbyterian Hospital/Columbia University Irving Medical Center in New York, NY. PATIENTS: Those admitted with COVID-19 between March 1, 2020, and May 31, 2020, as well as between March 1, 2021, and May 31, 2021, who received resuscitation after in-hospital cardiac arrest. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Among 103 patients with coronavirus disease 2019 who were resuscitated after in-hospital cardiac arrest in spring 2020, most self-identified as Hispanic/Latino or African American, 35 (34.0%) had return of spontaneous circulation for at least 20 minutes, and 15 (14.6%) survived to 30 days post-arrest. Compared with nonsurvivors, 30-day survivors experienced in-hospital cardiac arrest later (day 22 vs day 7; p = 0.008) and were more likely to have had an acute respiratory event preceding in-hospital cardiac arrest (93.3% vs 27.3%; p < 0.001). Among 30-day survivors, 11 (73.3%) survived to hospital discharge, at which point 8 (72.7%) had Cerebral Performance Category scores of 1 or 2. Among 26 COVID-19 patients resuscitated after in-hospital cardiac arrest in spring 2021, 15 (57.7%) had return of spontaneous circulation for at least 20 minutes, 3 (11.5%) survived to 30 days post in-hospital cardiac arrest, and 2 (7.7%) survived to hospital discharge, both with Cerebral Performance Category scores of 2 or less. Those who survived to 30 days post in-hospital cardiac arrest were younger (46.3 vs 67.8; p = 0.03), but otherwise there were no significant differences between groups. CONCLUSIONS: Patients with COVID-19 who received cardiopulmonary resuscitation after in-hospital cardiac arrest had low survival rates. Our findings additionally show return of spontaneous circulation rates in these patients may be impacted by hospital strain and that patients with in-hospital cardiac arrest preceded by acute respiratory events might be more likely to survive to 30 days, suggesting Advanced Cardiac Life Support efforts may be more successful in this subpopulation.