A retrospective single-center clinical assessment was performed for patients admitted to the Metro North Virtual Ward from March 25 to May 15, 2020. This study was deemed at low/negligible risk by the Royal Brisbane and Women’s Hospital Human Research Ethics Committee. No formal power calculations were performed, owing to the inclusion of all patients meeting the study criteria.

All patients admitted to the virtual ward during the specified period were assessed in accordance with the following inclusion criterion: a laboratory-confirmed diagnosis of COVID-19 through polymerase chain reaction (PCR) detection of SARS-CoV-2 RNA on a diagnostic nasopharyngeal swab (NPS). Patients were excluded if they had a preliminary positive or inconclusive PCR result but a negative result on subsequent confirmatory PCR testing. Patients were admitted to the virtual ward from the community after notification of a positive PCR result by the Metro North Public Health Unit, Herston, Australia, or following hospital discharge, in cases of confirmed disease.

Patients remained in out-of-hospital isolation during their virtual ward admission with nursing observations obtained through telephonic consultations. Virtual ward staff were located in a secure dedicated hospital workspace with medical records maintained in accordance with local hospital procedures and protocols. Patients were risk-stratified by age, comorbidities, and symptom burden to determine the frequency of telephonic consultations: low-risk patients, once daily; high-risk patients, twice daily. Observations were structured to monitor patient symptoms and identify potential deterioration. During each consultation, patients were asked to rate (on a scale of none, mild, moderate, or severe) the following symptoms: shortness of breath, cough, fatigue, sputum production, nausea/vomiting, headache, myalgia, and sore throat. These symptoms were numerically scored at each review. Patients’ general well-being, social situation, and adherence to isolation were also assessed.

Clinical reviews were conducted by medical officers when the following prespecified escalation criteria were met: (1) the patient reported severe symptoms related to shortness of breath, cough, or fatigue; (2) symptoms became more severe either on 1 observation of patients aged >65 years and having comorbidities or over 2 observations in those without comorbidities; or (3) any staff or patient concerns regarding disease exacerbation. If required, hospital referral was arranged for further assessment. All patients were reviewed by a medical officer prior to discharge. Multidisciplinary care was provided, with pharmacists ensuring patient access to medications and social workers offering psychosocial support.

In accordance with the Communicable Diseases Network Australia (CDNA) COVID-19 guidelines [24], patients were discharged and released from self-isolation on meeting the following recovery criteria: (1) 10 days since symptom onset and resolution of all symptoms of acute illness in the past 72 hours and (2) 10 days since hospital discharge and resolution of all symptoms of acute illness in the past 72 hours, if hospitalized for severe COVID-19.

Data on patient demographics, epidemiological history, comorbidities, medication history, COVID-19 symptoms, clinical reviews, pathology results, hospital assessment, and treatment outcomes were collected from existing medical records.

We expressed descriptive statistics as number (%) values for categorical data and median or mean (range) values for continuous variables. We performed Pearson χ² tests to explore the risk factors among patients requiring hospital referral and those with a virtual ward stay >7 days. We used Fisher exact tests when event counts were <5. Missing data were not imputed in the analyses. Furthermore, we calculated odds ratio (OR) and corresponding 95% CI values. All tests were two-sided, with a P value <.05 considered significant. Data were not adjusted for multiple testing; hence, findings should be considered to be descriptive and should not be used to infer definitive effects. SPSS (version 26.0, IBM Corp) was used for the analyses.

A total of 223 patients with a median age of 45 (range 14-78) years (female n=118, 52.9%) were assessed in this study (Table 1). This included 2 patients aged <18 years. Almost half (n=100, 44.8%) of the patients had a comorbidity, with hypertension (n=38, 17%) and asthma (n=24, 10.8%) being the most common manifestations. A total of 178 (79.8%) cases were epidemiologically linked to overseas travel (Figure 1), the most common destinations being the United Kingdom (n=68, 38%) and the United States (n=30, 17%). Furthermore, 16 (7.2%) patients had traveled on cruise ships. The most common COVID-19 symptoms upon presentation were cough (n=163, 73.1%), fever (n=117, 52.5%), and headache (n=103, 46.2%). Prior to virtual ward admission, 100 (44.8%) patients were assessed by a medical practitioner, either in person or through telemedicine, 21 (9.4%) had undergone chest radiography, and 22 (9.9%) had received laboratory blood tests. Initial diagnostic PCR detected SARS-CoV-2 RNA at a median cycle threshold (Ct) of 23.88.

Of the 223 patients in the virtual ward, 205 (92%) were discharged after clinical recovery without escalation to hospital care (Table 2). The median virtual ward admission length was 8 days (range 1-44 days). The median time to clinical recovery was 16 days (range 10-52 days). A total of 18 (8.1%) patients were referred for in-person hospital assessment (Table 3), of which 6 (33.3%) were assessed at the emergency department and discharged back to the virtual ward after review. The remaining 12 (66.7%) patients were admitted to an in-patient ward, of which 2 (16.7%) were admitted to the ICU and required mechanical ventilation (Table 3). The average length of hospital stay was 3.5 days (range 1-15 days) when admission to the ICU was not required. We recorded no mortality upon discharging the 223 patients assessed in this study.

Several factors were associated with a length of virtual ward stay >7 days. These included having any comorbidity (OR 2.0, 95% CI 1.15-3.40; P=.01), being classified as high risk on admission (OR 2.2, 95% CI 1.16-4.00; P=.02), or being hospitalized prior to virtual ward admission (OR 2.6, 95% CI 1.10-5.99; P=.03). Initial COVID-19 symptoms of cough (OR 2.2, 95% CI 1.22-4.10; P=.008), fevers or night sweats (OR 2.2, 95% CI 1.26-3.70; P=.005), and diarrhea (OR 2.3, 95% CI 1.06-5.07; P=.03) were also associated with a length of virtual ward stay >7 days.

In total, 18 patients with a median age of 54 (range 23-71) years (females: n=10 [55.6%]; high-risk: n=9 [50%]) were assessed in hospital upon virtual ward admission (Table 3). Referral to hospital occurred at a median of 8.5 days (range 3-20 days) since COVID-19 onset. Furthermore, 8 (43.9%) patients were reviewed by a medical officer prior to their virtual ward admission. Among 10 (55%) patients, the most common reason for care escalation was worsening, ongoing, or new-onset dyspnea. In addition, 4 (22%) patients were referred to hospital owing to new or ongoing fever, 3 (17%) for further clinical assessment of chest pain or chest tightness (Table 3), and the remaining 4 (23%) owing to a high symptom burden, functional decline with worsening fatigue, presyncopal symptoms, or suspected bacterial superinfection.

On hospital presentation, 4 (22%) patients had fever, and 4 (22%) had hypoxia and required supplemental oxygen on or shortly after presentation. Of the 18 patients presenting to hospital, 16 (88.9%) most commonly had an elevated lactate dehydrogenase level, liver function derangement, an elevated C-reactive protein level, or lymphopenia on blood tests. Furthermore, 15 (83.3%) patients underwent chest radiography, of which 4 (26.7%) had features of consolidation. Clinically, bacterial pneumonia was suspected in 7 (38.9%) hospitalized patients, and 8 (44.4%) patients were treated with antibiotics. Complications identified during hospitalization included respiratory failure in 3 (16.7%) patients, acute kidney injury in 3 (23.1%) patients, and liver function derangement in 6 (37.5%) patients. Moreover, 2 (11.1%) patients required admission to the ICU for mechanical ventilation.

Several possible predictors of deterioration associated with escalation of care were identified, including the presence of hypertension (OR 3.6, 95% CI 1.28-9.87; P=.01), sputum production at symptom onset (OR 5.2, 95% CI 1.74-15.49; P=.001), arthralgia at onset (OR 3.8, 95% CI 1.21-11.71; P=.02), and an PCR Ct for SARS-CoV-2 RNA of ≤20 on diagnostic NPS (OR 5.0, 95% CI 1.25-19.63; P=.02).

This retrospective study describes the characteristics and clinical course of an Australian cohort of patients with COVID-19 treated in a newly established virtual ward. To our knowledge, this is the first study to evaluate a community virtual ward model for patients with COVID-19 and the largest clinical assessment of patients with COVID-19 in Australia to date.

Epidemiologically, most cases were attributed to overseas travel, particularly to the United Kingdom, the United States, or cruise ships, consistent with a previous report from Australia [24]. In this cohort, cough, fever, and headache were the most common symptoms; however, their frequencies differed from those reported in a meta-analysis of hospitalized patients in China [10], thus potentially reflecting a lower disease severity in our cohort or reporting certain differences; our cohort recorded a higher incidence of sore throat (43.5% vs 11.6%) and rhinorrhea (36.8% vs 7.3%) and a lower incidence of chest tightness (9% vs 22.9%) and sputum expectoration (10.8% vs 23.7%) than those reported in the meta-analysis in China [10]. Approximately half of our patients had comorbidities, most commonly including hypertension (n=38, 17%) and asthma (n=24, 10.8%), comparable with a previous report [11].

Our results suggest that a virtual ward model is safe for patients with COVID-19. Overall, 205 (92%) patients recovered without escalation to hospital care. Furthermore, 18 (8.1%) patients required hospital assessment, of which only 12 (5.4%) were admitted to hospital and 2 (0.9%) were admitted to the ICU. This reflects a lower severity of COVID-19 in our cohort (0.9%) compared to that reported previously (5%) [15]. The mortality rate of 0 (0%) in our cohort is consistent with the low nationwide mortality rate of 1.3% in Australia [24], at the time of writing. The median virtual ward admission length was 8 days (range 1-44 days) with a median time to clinical recovery of 16 days (range 10-52 days). This discrepancy may be potentially attributed to returning overseas travelers who acquired the infection abroad. In the context of comorbidity and prior COVID-19–related hospitalization, higher-risk patients stayed longer in the virtual ward. These findings suggest that a virtual model of care can potentially preserve in-patient capacity and resources in hospitals and reduce the risk of disease transmission and hospital-acquired sequelae. Although most of our patients had mild illness, regular monitoring and supportive care may have reduced hospital presentations.

Timely identification of disease exacerbation is imperative for safe virtual care. Several studies have reported high diagnostic agreement between virtual and in-person consultations [25,26]. However, clinicians have cited concerns regarding telephonic consultations, primarily owing to the lack of physical examination [27]. COVID-19 has provided an opportunity to introduce a range of telemedicine approaches to the medical staff, owing to the necessity to deliver safe patient care during a pandemic. Remote assessment of dyspnea, the most common reason for hospital referral in our study, has been challenging during COVID-19 [28]. A difficulty in ruling out urgency upon telephonic consultations may have resulted in a lower threshold for hospital referral in our study, with one-third of hospital-referred patients subsequently discharged after in-person assessment [26]. Our virtual ward model was simple without monitoring equipment beyond household thermometers. Enhanced assessment capabilities through real-time telemonitoring may reduce diagnostic uncertainty [29].

Our hospital-referred patients had a higher median age of 54 (range 14-78) years, being referred to hospital at a median of 8.5 days (range 3-20 days) into their illness. Preexisting hypertension, a proposed risk factor for severe COVID-19, was associated with a 3.6-fold increase in hospitalization rates. Initial symptoms of sputum production and arthralgia were associated with hospital referral, which have not been previously reported. Patients with a PCR Ct for SARS-CoV-2 RNA of ≤20 on diagnostic NPS were 5-fold more likely to be referred to hospital. Although a lower Ct indicates a higher RNA sample quantity, the implication of this value for disease progression is unclear. Further studies are required to validate these findings.

The 2 (0.9%) patients admitted to the ICU had risk factors associated with severe COVID-19 [14]. These 2 men aged >60 years, with preexisting hypertension, presented disease exacerbation on day 10 of their illness and hence required ICU admission and ventilatory support; this is consistent with the reported median of 8-12 days to progression to ARDS and 10 days to ICU admission for patients with severe COVID-19 [14-16,19]. The pathogenesis of this late decline remains unclear; however, pathological hyperinflammation seems likely [30].

The limitations of this study include its observational design and retrospective data collection, which resulted in missing data across several variables reported herein. Few patients in our cohort had severe COVID-19. There may have been an ascertainment bias as patients with more severe COVID-19 may have been directly admitted to hospital for the duration of their illness, bypassing the virtual ward.

To our knowledge, this is the largest cohort study of COVID-19 patients in Australia to be described to date and the first to evaluate a virtual ward model of care. This study provides evidence regarding the safety and feasibility of a virtual ward setting to treat patients with COVID-19. Further studies are needed to identify the early predictors of COVID-19 exacerbation and to validate this health care model.