Comparison of the Characteristics and Outcomes of COVID-19 Patients Treated by a Hospital-at-Home Service in Japan during the Alpha and Delta Waves

The differences in virus shedding time between the Delta variant and original SARS-CoV-2 infected patients

Background

The worldwide epidemic of Coronavirus Disease 2019 (COVID-19) has evolved into multiple variants. The Delta variant is known for its ability to spread and replicate, while data are limited about the virus shedding time in patients infected by the Delta variant.

Methods

56 Delta variant and 56 original SARS-CoV-2 infected patients from Hunan, China, matched according to age and gender divided into two groups and compared the baseline characteristics and laboratory findings with appropriate statistical methods.

Results

Patients infected with the Delta variant had significantly fewer symptoms of fever (p < 0.001), fatigue (p = 0.004), anorexia (p < 0.001), shortness of breath (p = 0.004), diarrhea (p = 0.006), positive pneumonia rate of chest CT (p = 0.019) and chest CT ground glass opacities (p = 0.004) than those of patients with the original SARS-CoV-2. Patients of the Delta variant group had a significantly longer virus shedding time [41.5 (31.5, 46.75) vs. 18.5 (13, 25.75), p < 0.001] compared with the original SARS-CoV-2 group. The correlation analyses between the virus shedding time and clinical or laboratory parameters showed that the virus shedding time was positively related to the viral strain, serum creatinine and creatine kinase isoenzyme, while negatively correlated with lymphocyte count, total bilirubin and low-density lipoprotein. Finally, the viral strain and lymphocyte count were thought of as the independent risk factors of the virus shedding time demonstrated by multiple linear regression.

Conclusion

COVID-19 patients infected with the Delta variant exhibited fewer gastrointestinal symptoms and prolonged virus shedding time than those infected with the original SARS-CoV-2. Delta variant and fewer lymphocyte were correlated with prolonged virus shedding time.

Characteristics and Outcomes of 360 Consecutive COVID-19 Patients Discharged From the Emergency Department With Supplemental Oxygen

STUDY OBJECTIVE

To describe characteristics and outcomes of coronavirus disease (COVID-19) patients with new supplemental oxygen requirements discharged from a large public urban emergency department (ED) with supplemental oxygen.

METHODS

This observational case series describes the characteristics and outcomes of 360 consecutive COVID-19 patients with new supplemental oxygen requirements discharged from a large urban public ED between April 2020 and March 2021 with supplemental oxygen. Primary outcomes included 30-day survival and 30-day survival without unscheduled inpatient admission. Demographic and clinical data were collected through a structured chart review.

RESULTS

Among 360 patients with COVID-19 discharged from the ED with supplemental oxygen, 30-day survival was 97.5% (95% confidence interval (CI) 95.3 to 98.9%; n=351), and 30-day survival without unscheduled admission was 81.1% (95% CI 76.7 to 85.0%; n=292). A sensitivity analysis incorporating worst-case-scenario for 12 patients without complete follow-up 30 days after index visit yields 30-day survival of 95.5% (95% CI 92.5 to 97.2%; n=343), and 30-day survival without unscheduled admission of 78.9% (95% CI 74.3 to 83.0%; n=284). Among study patients, 32.2% (n=116) had a nadir ED oxygen saturation of <90%, among these 30-day survival was 97.4% (95% CI 92.6 to 99.4%; n=113), and 30-day survival without unscheduled admission was 76.7% (95% CI 68.8 to 84.1%; n=89).

CONCLUSION

COVID-19 patients with new supplemental oxygen requirements discharged from the ED had survival comparable to COVID-19 ED patients with mild exertional hypoxia treated with supplemental oxygen in other settings, and this held true when the analysis was restricted to patients with nadir ED index visit oxygen saturations <90%. Discharge of select COVID-19 patients with supplemental oxygen from the ED may provide a viable alternative to hospitalization, particularly when inpatient capacity is limited.

Digital health as an enabler for hospital@home: A rising trend or just a vision?

Background

Hospital@home is a model of healthcare, where healthcare professionals actively treat patients in their homes for conditions that may otherwise require hospitalization. Similar models of care have been implemented in jurisdictions around the world over the past few years. However, there are new developments in health informatics including digital health and participatory health informatics that may have an impact on hospital@home approaches.

Objectives

This study aims to identify the current state of implementation of emerging concepts into the hospital@home research and models of care; to identify strengths and weaknesses, opportunities, and threats associated with the models of care; and to suggest a research agenda.

Methods

We employed two research methodologies, namely, a literature review and a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The literature from the last 10 years was collected from PubMed using the search string "hospital at home" OR "care at home" OR "patient at home." Relevant information was extracted from the included articles.

Results

Title and abstract review were conducted on 1,371 articles. The full-text review was conducted on 82 articles. Data were extracted from 42 articles that met our review criteria. Most of the studies originated from the United States and Spain. Several medical conditions were considered. The use of digital tools and technologies was rarely reported. In particular, innovative approaches such as wearables or sensor technologies were rarely used. The current landscape of hospital@home models of care simply delivers hospital care in the patient's home. Tools or approaches from taking a participatory health informatics design approach involving a range of stakeholders (such as patients and their caregivers) were not reported in the literature reviewed. In addition, emerging technologies supporting mobile health applications, wearable technologies, and remote monitoring were rarely discussed.

Conclusion

There are multiple benefits and opportunities associated with hospital@home implementations. There are also threats and weaknesses associated with the use of this model of care. Some weaknesses could be addressed by using digital health and wearable technologies to support patient monitoring and treatment at home. Employing a participatory health informatics approach to design and implementation could help to ensure the acceptance of such care models.