Outcomes of non-hospitalized isolation service during COVID-19 pandemic
Tansawet, A.
Show abstract
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or COVID-19 affected more than 500 million patients worldwide and overwhelmed hospital resources. Rapid increase of new cases forced patient isolation to be conduct outside the hospital where many strategies have been implemented. This study aimed to compare outcomes among non-hospitalized isolation service. MethodsA retrospective cohort study was conducted in asymptomatic and mildly symptomatic adult patients who were allocated to home isolation, community isolation, and hospitel (i.e., hotel isolation) under service of Ramathibodi Hospital and Chakri Naruebodindra Medical Institute. Variables including patients characteristics, comorbidities, symptoms, and medication were retrieved for use in inverse-probability-weighted regression adjustment model. Risks and risk differences (RDs) of death, oxygen requirement, and hospitalization were estimated from the model afterward. ResultsA total of 3869 patients were included in the analysis. Mean age was 41.8 {+/-} 16.5 years. Cough was presented in 62.2% of patients, followed by hyposmia (43.7%), runny nose (43.5%), sore throat (42.2%), and fever (38.6%). Among the isolation strategies, hospitel yielded the lowest risks of death (0.3%), oxygen requirement (4.5%), and hospitalization (3.3%). Hospitel had significantly lower oxygen requirements and hospitalization rates compared with home isolation with the RDs (95% CI) of -0.016 (-0.029, -0.002) and -0.025 (-0.038, -0.012), respectively. Death rates did not differ among isolation strategies. ConclusionNon-hospitalized isolation is feasible and could ameliorate hospital demands. Given the lowest risks of death, hospitalization, and oxygen requirement, hospitel might be the best isolation strategy.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Assessing the impact of the Gamma variant on COVID-19 Patient admissions in a Southern Brazilian tertiary hospital - A comparison of dual pandemic phases 96%
- Clinical profile and factors associated with COVID-19 in Cameroon: a prospective cohort study 95%
- Prevalence, characteristics, and predictors of Long COVID among diagnosed cases of COVID-19 95%
Similar papers in this journal
- Risk factors for severity on admission and the disease progression during hospitalization in a large cohort of COVID-19 patients in Japan 95%
- Towards definitions of critical illness and critical care using concept analysis 95%
- Clinical Characteristics of Hospitalized Covid-19 Patients in New York City 94%
Similar papers in this journal
- Efficacy of pulmonary rehabilitation in severe and critical-ill COVID-19 patients: a controlled study 94%
- Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review 93%
- Are Geographic Factors Associated With Poorer Outcomes In Patients Diagnosed With COVID-19? 93%
Similar papers in this journal
- Application and Evaluation of Flipped Teaching Based on Video Conference in Standardized Training for Internal Medicine Residents 94%
- A qualitative study on factors influencing health workers’ uptake of a pilot surgical antibiotic prophylaxis stewardship programme in selected Georgian hospitals 94%
- Organizational models and patient-reported outcomes for palliative care across five tertiary hospitals in Nigeria: an Environmental Scan 93%
Similar papers in this journal
- Determinants of Developing Symptomatic Disease in Ethiopian COVID-19 Patients 94%
- Evaluation of the disease outcome in Covid-19 infected patients by disease symptoms: a retrospective cross-sectional study in Ilam Province, Iran 94%
- Explanation of Hand, Foot, and Mouth Disease Cases in Japan Using Google Trends Before and During the COVID-19: Infodemiology Study 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.