Environmental surveillance for SARS-CoV-2 for outbreak detection in hospital: A single centre prospective study (preprint)

Identifying COVID-19 outbreaks in hospitals at an early stage requires active surveillance. Our objective was to assess whether floor swabs correlated with COVID-19 outbreak status in hospital. We swabbed the floors of an inpatient ward at Mount Sinai Hospital for 32 weeks, from October 31, 2022 to June 15, 2023 and RT-qPCR analysis provided a quantification cycle of detection for each positive swab. 182 swabs were processed for SARS CoV-2, of which 98.4% were positive. Two COVID-19 outbreaks were declared during the study period. The median viral copy number was 210 (IQR, 49 to 1018) during non-outbreak periods and 653 (IQR, 300 to 1754) during outbreak periods. Analyzing the number of viral copies of SARS-CoV-2, instead of percentage positivity, gave a clearer view of changes in outbreak status over time, thereby illustrating the benefits of this approach to monitor pathogen load in hospital settings.

The association between diabetes and mortality among patients hospitalized with COVID-19: Cohort Study of Hospitalized Adults in Ontario, Canada and Copenhagen, Denmark (preprint)

Importance: Diabetes has been reported to be associated with an increased risk of death among patients with COVID-19. However, available studies lack detail on COVID illness severity and measurement of relevant comorbidities. Design, Setting, and Participants: We conducted a multicenter, retrospective cohort study of patients over the age of 18 years who were hospitalized with COVID-19 between January 1, 2020 and November 30, 2020 in Ontario, Canada and Copenhagen, Denmark. Chart abstraction emphasizing co-morbidities and disease severity was performed by trained research personnel. The association between diabetes and death was measured using Poissson regression. Main Outcomes and Measures: within hospital 30-day risk of death. Results: Our study included 1018 hospitalized patients with COVID-19 in Ontario and 305 in Denmark, of whom 405 and 75 patients respectively had pre-existing diabetes. In both Ontario and Denmark, patients with diabetes were more likely to be older, have chronic kidney disease, cardiovascular disease, higher troponin levels, and to receive antibiotics compared with adults who did not have diabetes. In Ontario, the crude mortality rate ratio among patients with diabetes was 1.60 [1.24 -- 2.07 95% CI] and in the adjusted regression model was 1.20 [0.86 -- 1.66 95% CI]. In Denmark, the crude mortality rate ratio among patients with diabetes was 1.27 (0.68 -- 2.36 95% CI) and in the adjusted model was 0.90 (0.49 -- 1.54 95% CI)]. Meta-analyzing the two rate ratios from each region resulted in a crude mortality rate ratio of 1.55 (95% CI 1.22,1.96) and an adjusted mortality rate ratio of 1.11 (95% CI 0.84, 1.47). Conclusions: Presence of diabetes was not strongly associated with in-hospital COVID mortality independent of illness severity and other comorbidities.

Detection of COVID-19 Outbreaks in Long-Term Care Homes Using Built Environment Testing for SARS-CoV-2: A Multicentre Prospective Study (preprint)

Background Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance of COVID-19. Built environment sampling may provide a more spatially refined approach for surveillance of COVID-19 in congregate living settings and other high risk settings (e.g., schools, daycares). Methods We conducted a prospective study in 10 long-term care homes (LTCHs) across three cities in Ontario, Canada between September 2021 and May 2022. Floor surfaces were sampled weekly at multiple locations (range 10 to 24 swabs per building) within each building and analyzed for the presence of SARS-CoV-2 using RT-qPCR. The exposure variable was detection of SARS-CoV-2 on floors. The primary outcome was the presence of a COVID-19 outbreak in the week that floor sampling was performed. Results Over the 9-month study period, we collected 3848 swabs at 10 long-term care homes. During the study period, 19 COVID-19 outbreaks occurred with 103 cumulative weeks under outbreak. During outbreak periods, the proportion of floor swabs positive for SARS-CoV-2 was 50% (95% CI: 47-53) with a median quantification cycle of 37.3 (IQR 35.2-38.7). During non-outbreak periods the proportion of floor swabs positive was 18% (95% CI:17-20) with a median quantification cycle of 38.0 (IQR 36.4-39.1). Using the proportion of positive floor swabs for SARS-CoV-2 to predict COVID-19 outbreak status in a given week, the area under the receiver operating curve (AUROC) was 0.85 (95% CI: 0.78-0.92). Using thresholds of [≥]10%, [≥]30%, and [≥]50% of floor swabs positive for SARS-CoV-2 yielded positive predictive values for outbreak of 0.57 (0.49-0.66), 0.73 (0.63-0.81), and 0.73 (0.6-0.83) respectively and negative predictive values of 0.94 (0.87-0.97), 0.85 (0.78-0.9), and 0.75 (0.68-0.81) respectively. Among 8 LTCHs with an outbreak and swabs performed in the antecedent week, 5 had positive floor swabs exceeding 10% at least five days prior to outbreak identification. For 3 of these 5 LTCHs, positivity of floor swabs exceeded 10% more than 10 days before the outbreak being identified. Conclusions Detection of SARS-CoV-2 on floors is strongly associated with COVID-19 outbreaks in LTCHs. These data suggest a potential role for floor sampling in improving early outbreak identification.

Prone positioning of patients with moderate hypoxia due to COVID-19: A multicenter pragmatic randomized trial (preprint)

What is already known on this topic: Prone positioning is considered standard of care for mechanically ventilated patients who have severe acute respiratory distress syndrome. Recent data suggest prone positioning is beneficial for patients with COVID-19 who are requiring high flow oxygen. It is unknown of prone positioning is beneficial for patients not on high flow oxygen. What this study adds: Prone positioning is generally not well tolerated and innovative approaches are needed to improve adherence. Clinical and physiologic outcomes were not improved with prone positioning among hypoxic but not critically ill patients hospitalized with COVID-19. Objectives: To assess the effectiveness of prone positioning to reduce the risk of death or respiratory failure in non-critically ill patients hospitalized with COVID-19 Design: Pragmatic randomized clinical trial of prone positioning of patients hospitalized with COVID-19 across 15 hospitals in Canada and the United States from May 2020 until May 2021. Settings: Patients were eligible is they had a laboratory-confirmed or a clinically highly suspected diagnosis of COVID-19, required supplemental oxygen (up to 50% fraction of inspired oxygen [FiO2]), and were able to independently prone with verbal instruction. (NCT04383613). Main Outcome Measures: The primary outcome was a composite of in-hospital death, mechanical ventilation, or worsening respiratory failure defined as requiring at least 60% FiO2 for at least 24 hours. Secondary outcomes included the change in the ratio of oxygen saturation to FiO2 (S/F ratio). Results: A total of 248 patients were included. The trial was stopped early on the basis of futility for the pre-specified primary outcome. The median time from hospital admission until randomization was 1 day, the median age of patients was 56 years (interquartile range [IQR] 45,65), 36% were female, and 90% of patients were receiving oxygen via nasal prongs at the time of randomization. The median time spent prone in the first 72 hours was 6 hours total (IQR 1.5,12.8) for the prone arm compared to 0 hours (0,2) in the control arm. The risk of the primary outcome was similar between the prone group (18 [14.3%] events) and the standard care group (17 [13.9%] events), odds ratio 0.92 (95% CI 0.44 to 1.92). The change in the S/F ratio after 72 hours was similar for patients randomized to prone compared to standard of care. Conclusion: Among hypoxic but not critically patients with COVID-19 in hospital, a multifaceted intervention to increase prone positioning did not improve outcomes. Adherence to prone positioning was poor, despite multiple efforts. Subsequent trials of prone positioning should aim to develop strategies to improve adherence to awake prone positioning.

Assessing the utility of lymphocyte count to diagnose COVID-19 (preprint)

Background: COronaVirus Disease 2019 (COVID-19) can be challenging to diagnose, because symptoms are non-specific, clinical presentations are heterogeneous, and false negative tests can occur. Our objective was to assess the utility of lymphocyte count to differentiate COVID-19 from influenza or community-acquired pneumonia (CAP). Methods: We conducted a cohort study of adults hospitalized with COVID-19 or another respiratory infection (i.e., influenza, CAP) at seven hospitals in Ontario, Canada.The first available lymphocyte count during the hospitalization was used. Standard test characteristics for lymphocyte count (x109/L) were calculated (i.e., sensitivity, specificity, area under the receiver operating curve [AUC]). All analyses were conducting using R. Results: There were 869 hospitalizations for COVID-19, 669 for influenza, and 3009 for CAP. The mean age across the three groups was 67 and patients with pneumonia were older than those with influenza or COVID19, and approximately 46% were woman. The median lymphocyte count was nearly identical for the three groups of patients: 1.0 x109/L (interquartile range [IQR]:0.7,2.0) for COVID-19, 0.9 x109/L (IQR 0.6,1.0) for influenza, and 1.0 x109/L (IQR 0.6,2.0) for CAP. At a lymphocyte threshold of less than 2.0 x109/L, the sensitivity was 87% and the specificity was approximately 10%. As the lymphocyte threshold increased, the sensitivity of diagnosing COVID-19 increased while the specificity decreased. The AUC for lymphocyte count was approximately 50%. Interpretation: Lymphocyte count has poor diagnostic discrimination to differentiate between COVID-19 and other respiratory illnesses. The lymphopenia we consistently observed across the three illnesses in our study may reflect a non-specific sign of illness severity. However, lymphocyte count above 2.0 x109/L may be useful in ruling out COVID-19 (sensitivity = 87%).

Federated Learning used for predicting outcomes in SARS-COV-2 patients (preprint)

‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.