Identifying patterns in unplanned hospital admissions during the COVID-19 pandemic: a single-centre retrospective study.

BACKGROUND: Countries with a high prevalence of COVID-19 have identified a reduction in crude hospital admission rates for non-COVID-19 conditions during the pandemic. There remains a paucity of such data from lower prevalence countries, including Australia. AIMS: To describe the patterns of unplanned hospital daily admission rates during the COVID-19 pandemic in a major Australian metropolitan hospital, with a focus on acute medical presentations including acute coronary syndrome (ACS), stroke and falls. METHODS: This single-centre retrospective analysis analysed hospital admission episodes between 1 March and 30 April 2020 (COVID-19-era) and compared this to a historical cohort during the same period between 2017 and 2019 (pre-COVID-19). Information collected included total admission rates and patient characteristics for ACS, stroke and falls patients. RESULTS: A total of 12 278 unplanned admissions was identified across the study period. The daily admission rate was lower in the COVID-19-era compared with pre-COVID-19 (46.59 vs 51.56 days, P < 0.001). There was also a reduced average daily admission rate for falls (7.79 vs 9.95 days, P < 0.001); however, similar admission rates for ACS (1.52 vs 1.49 days, P = 0.83) and stroke (1.56 vs 1.76 days, P = 0.33). CONCLUSIONS: Public health interventions have been effective in reducing domestic cases of COVID-19 in Australia. At our tertiary metropolitan hospital, we have observed a significant reduction in unplanned hospital admission rates during the COVID-19-era, particularly for falls. Public health messaging needs to focus on educating the public how to seek medical care safely and promptly in the context of the ongoing COVID-19 crisis.

COVID-MATCH65-A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2.

OBJECTIVES: We report on the key clinical predictors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present a clinical decision rule that can risk stratify patients for COVID-19. DESIGN, PARTICIPANTS AND SETTING: A prospective cohort of patients assessed for COVID-19 at a screening clinic in Melbourne, Australia. The primary outcome was a positive COVID-19 test from nasopharyngeal swab. A backwards stepwise logistic regression was used to derive a model of clinical variables predictive of a positive COVID-19 test. Internal validation of the final model was performed using bootstrapped samples and the model scoring derived from the coefficients, with modelling performed for increasing prevalence. RESULTS: Of 4226 patients with suspected COVID-19 who were assessed, 2976 patients underwent SARS-CoV-2 testing (n = 108 SARS-CoV-2 positive) and were used to determine factors associated with a positive COVID-19 test. The 7 features associated with a positive COVID-19 test on multivariable analysis were: COVID-19 patient exposure or international travel, Myalgia/malaise, Anosmia or ageusia, Temperature, Coryza/sore throat, Hypoxia-oxygen saturation < 97%, 65 years or older-summarized in the mnemonic COVID-MATCH65. Internal validation showed an AUC of 0.836. A cut-off of ≥ 1.5 points was associated with a 92.6% sensitivity and 99.5% negative predictive value (NPV) for COVID-19. CONCLUSIONS: From the largest prospective outpatient cohort of suspected COVID-19 we define the clinical factors predictive of a positive SARS-CoV-2 test. The subsequent clinical decision rule, COVID-MATCH65, has a high sensitivity and NPV for SARS-CoV-2 and can be employed in the pandemic, adjusted for disease prevalence, to aid COVID-19 risk-assessment and vital testing resource allocation.