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BACKGROUND: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19. RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration. INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities. METHODS: We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments. RESULTS: There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% (n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% (n = 5297), and for 99.8% (n = 5284) of these admissions, the patient received laboratory testing, for 86.9% (n = 4604) the patient received plain radiography, for 41.5% (n = 2197) the patient received computed tomography and for 81.2% (n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each. INTERPRETATION: The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases.

In December 2020, the US Food and Drug Administration (FDA) issued Emergency Use Authorizations (EUAs) for the Pfizer BioNTech (https://www. pfizer.com) and Moderna (https://www.modernatx. com) 2-dose primary mRNA COVID-19 vaccine [...]

Although more common among adults, severe coronavirus disease (COVID-19) and hospitalization can occur in children. Among >8,300 hospitalized children 5-11 years of age, 1/3 required intensive care (1,2). Children can [...]

Objective: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. Design: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. Setting: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010—March 2015 (primary cohort) and 2015–2019 (secondary cohort). Patients: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. Measurements and main results: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015–2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56–83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015–2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. Conclusions: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications. [ABSTRACT FROM AUTHOR]

BackgroundIn Canada, 30%-60% of patients presenting to emergency departments are ambulatory. This category has been labeled as a source of emergency department overuse. Acting on the presumption that primary care practices and walk-in clinics offer equivalent care at a lower cost, governments have invested massively in improving access to these alternative settings in the hope that patients would present there instead when possible, thereby reducing the load on emergency departments. Data in support of this approach remain scarce and equivocal. ObjectiveThe aim of this study is to compare the value of care received in emergency departments, walk-in clinics, and primary care practices by ambulatory patients with upper respiratory tract infection, sinusitis, otitis media, tonsillitis, pharyngitis, bronchitis, influenza-like illness, pneumonia, acute asthma, or acute exacerbation of chronic obstructive pulmonary disease. MethodsA multicenter prospective cohort study will be performed in Ontario and Québec. In phase 1, a time-driven activity-based costing method will be applied at each of the 15 study sites. This method uses time as a cost driver to allocate direct costs (eg, medication), consumable expenditures (eg, needles), overhead costs (eg, building maintenance), and physician charges to patient care. Thus, the cost of a care episode will be proportional to the time spent receiving the care. At the end of this phase, a list of care process costs will be generated and used to calculate the cost of each consultation during phase 2, in which a prospective cohort of patients will be monitored to compare the care received in each setting. Patients aged 18 years and older, ambulatory throughout the care episode, and discharged to home with one of the aforementioned targeted diagnoses will be considered. The estimated sample size is 1485 patients. The 3 types of care settings will be compared on the basis of primary outcomes in terms of the proportion of return visits to any site 3 and 7 days after the initial visit and the mean cost of care. The secondary outcomes measured will include scores on patient-reported outcome and experience measures and mean costs borne wholly by patients. We will use multilevel generalized linear models to compare the care settings and an overlap weights approach to adjust for confounding factors related to age, sex, gender, ethnicity, comorbidities, registration with a family physician, socioeconomic status, and severity of illness. ResultsPhase 1 will begin in 2021 and phase 2, in 2023. The results will be available in 2025. ConclusionsThe end point of our program will be for deciders, patients, and care providers to be able to determine the most appropriate care setting for the management of ambulatory emergency respiratory conditions, based on the quality and cost of care associated with each alternative. International Registered Report Identifier (IRRID)PRR1-10.2196/25619