Introduction: The Southern California region of Kaiser Permanente developed a COVID-19 Home Monitoring program as an alternative to hospital admission to decrease hospital bed days and mitigate the adverse effects of a surge. To date, more than 15,000 patients have been enrolled and approximately 10% of enrolled patients have been escalated to hospital care for timely treatment. Our objective is to describe our COVID-19 Home Monitoring program and present early results.
Methods: We conducted an observational retrospective study of all patients enrolled in the COVID-19 Home Monitoring program between April 13, 2020 through February 12, 2021. Data analysis conducted includes patient demographics, enrollment, entry points, length of stay, mortality, additional treatment, utilization, adherence, satisfaction, and alert triggers.
Results: A total of 12,461 of 13,055 patients (95.5%) recovered and completed the program, 1387 patients (10.6%) were admitted to the hospital, and 20 patients (0.2%) died while they were being monitored at home. The mortality rate at 30 days from enrollment was 1.6%. Hospital length of stay for ambulatory patients receiving oxygen only was 5.4 days compared to 3.1 days for those ambulatory patients receiving oxygen, dexamethasone, and remdesivir.
Conclusion: COVID-19 home monitoring appears to be safe and effective. Initial data suggest it can serve as an alternative to hospitalization, decreasing hospital length of stay when patients receive therapies in the ambulatory setting otherwise reserved for the hospital. Initial results of this Home Monitoring program appear to be promising, and a longer term prospective study is warranted.
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http://dx.doi.org/10.7812/TPP/20.281 | DOI Listing |
Viruses
December 2024
Wadsworth Center, David Axelrod Institute, New York State Department of Health, Albany, NY 12208, USA.
A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.
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December 2024
I. Department of Internal Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
Background/objectives: The efficacy of monovalent BNT162b2 Omicron XBB.1.5 booster vaccination in liver transplant recipients (LTRs) has yet to be described, particularly regarding the immune response to emerging variants like JN.
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December 2024
World Health Organization, 1202 Geneva, Switzerland.
Setting up a global SARS-CoV-2 surveillance system requires an understanding of how virus isolation and propagation practices, use of animal or human sera, and different neutralisation assay platforms influence assessment of SARS-CoV-2 antigenicity. In this study, with the contribution of 15 independent laboratories across all WHO regions, we carried out a controlled analysis of neutralisation assay platforms using the first WHO International Standard for antibodies to SARS-CoV-2 variants of concern (source: NIBSC). Live virus isolates (source: WHO BioHub or individual labs) or spike plasmids (individual labs) for pseudovirus production were used to perform neutralisation assays using the same serum panels.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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November 2024
MRC/UVRI & LSHTM Uganda Research Unit, Entebbe 256, Uganda.
The emergence of SARS-CoV-2 variants has heightened concerns about vaccine efficacy, posing challenges in controlling the spread of COVID-19. As part of the COVID-19 Vaccine Effectiveness and Variants (COVVAR) study in Uganda, this study aimed to genotype and characterize SARS-CoV-2 variants in patients with COVID-19-like symptoms who tested positive on a real-time PCR. Amplicon deep sequencing was performed on 163 oropharyngeal/nasopharyngeal swabs collected from symptomatic patients.
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