Trends in management and outcomes of COVID patients admitted to a Swiss tertiary care hospital.

Two successive COVID-19 flares occurred in Switzerland in spring and autumn 2020. During these periods, therapeutic strategies have been constantly adapted based on emerging evidence. We aimed to describe these adaptations and evaluate their association with patient outcomes in a cohort of COVID-19 patients admitted to the hospital. Consecutive patients admitted to the Geneva Hospitals during two successive COVID-19 flares were included. Characteristics of patients admitted during these two periods were compared as well as therapeutic management including medications, respiratory support strategies and admission to the ICU and intermediate care unit (IMCU). A mutivariable model was computed to compare outcomes across the two successive waves adjusted for demographic characteristics, co-morbidities and severity at baseline. The main outcome was in-hospital mortality. Secondary outcomes included ICU admission, Intermediate care (IMCU) admission, and length of hospital stay. A total of 2'983 patients were included. Of these, 165 patients (16.3%, n = 1014) died during the first wave and 314 (16.0%, n = 1969) during the second (p = 0.819). The proportion of patients admitted to the ICU was lower in second wave compared to first (7.4 vs. 13.9%, p < 0.001) but their mortality was increased (33.6% vs. 25.5%, p < 0.001). Conversely, a greater proportion of patients was admitted to the IMCU in second wave compared to first (26.6% vs. 22.3%, p = 0.011). A third of patients received lopinavir (30.7%) or hydroxychloroquine (33.1%) during the first wave and none during second wave, while corticosteroids were mainly prescribed during second wave (58.1% vs. 9.1%, p < 0.001). In the multivariable analysis, a 25% reduction of mortality was observed during the second wave (HR 0.75; 95% confidence interval 0.59 to 0.96). Among deceased patients, 82.3% (78.2% during first wave and 84.4% during second wave) died without beeing admitted to the ICU. The proportion of patients with therapeutic limitations regarding ICU admission increased during the second wave (48.6% vs. 38.7%, p < 0.001). Adaptation of therapeutic strategies including corticosteroids therapy and higher admission to the IMCU to receive non-invasive respiratory support was associated with a reduction of hospital mortality in multivariable analysis, ICU admission and LOS during the second wave of COVID-19 despite an increased number of admitted patients. More patients had medical decisions restraining ICU admission during the second wave which may reflect better patient selection or implicit triaging.

Association Between Prehospital Blood Glucose Levels and Outcomes in Patients With COVID-19 Infection: A Retrospective Cohort Study.

BACKGROUND: Hyperglycaemia is associated with worse outcomes in many settings. However, the association between dysglycaemia and adverse outcomes remains debated in COVID-19 patients. This study determined the association of prehospital blood glucose levels with acute medical unit (intensive care unit or high dependency unit) admission and mortality among COVID-19-infected patients. METHODS: This was a single-centre, retrospective cohort study based on patients cared for by the prehospital medical mobile unit from a Swiss university hospital between March 2020 and April 2021. All adult patients with confirmed or suspected COVID-19 infection during the study period were included. Data were obtained from the prehospital medical files. The main exposure was prehospital blood glucose level. A 7.8 mmol/L cut-off was used to define high blood glucose level. Restricted cubic splines were also used to analyse the exposure as a continuous variable. The primary endpoint was acute medical unit admission; secondary endpoints were 7-day and 30-day mortality. Multivariable logistic regressions were performed to compute odds ratios. RESULTS: A total of 276 patients were included. The mean prehospital blood glucose level was 8.8 mmol/l, and 123 patients presented high blood glucose levels. The overall acute medical unit admission rate was 31.2%, with no statistically significant difference according to prehospital blood glucose levels. The mortality rate was 13.8% at 7 days and 25% at 30 days. The 30-day mortality rate was higher in patients with high prehospital blood glucose levels, with an adjusted odds ratio of 2.5 (1.3-4.8). CONCLUSIONS: In patients with acute COVID-19 infection, prehospital blood glucose levels do not seem to be associated with acute medical unit admission. However, there was an increased risk of 30-day mortality in COVID-19 patients who presented high prehospital blood glucose levels.

Abdominal pain patterns during COVID-19: an observational study.

Abdominal pain and liver injury have been frequently reported during coronavirus disease-2019 (COVID-19). Our aim was to investigate characteristics of abdominal pain in COVID-19 patients and their association with disease severity and liver injury.Data of all COVID-19 patients hospitalized during the first wave in one hospital were retrieved. Patients admitted exclusively for other pathologies and/or recovered from COVID-19, as well as pregnant women were excluded. Patients whose abdominal pain was related to alternative diagnosis were also excluded.Among the 1026 included patients, 200 (19.5%) exhibited spontaneous abdominal pain and 165 (16.2%) after abdomen palpation. Spontaneous pain was most frequently localized in the epigastric (42.7%) and right upper quadrant (25.5%) regions. Tenderness in the right upper region was associated with severe COVID-19 (hospital mortality and/or admission to intensive/intermediate care unit) with an adjusted odds ratio of 2.81 (95% CI 1.27-6.21, p = 0.010). Patients with history of lower abdomen pain experimented less frequently dyspnea compared to patients with history of upper abdominal pain (25.8 versus 63.0%, p < 0.001). Baseline transaminases elevation was associated with history of pain in epigastric and right upper region and AST elevation was strongly associated with severe COVID-19 with an odds ratio of 16.03 (95% CI 1.95-131.63 p = 0.010).More than one fifth of patients admitted for COVID-19 presented abdominal pain. Those with pain located in the upper abdomen were more at risk of dyspnea, demonstrated more altered transaminases, and presented a higher risk of adverse outcomes.

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study.

BACKGROUND: The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection. METHODS AND ANALYSIS: Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses. RESULTS: A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62). CONCLUSION: Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.

Deep SNOMED CT Enabled Large Clinical Database About COVID-19.

In spring 2020, as the COVID-19 pandemic is in its first wave in Europe, the University hospitals of Geneva (HUG) is tasked to take care of all Covid inpatients of the Geneva canton. It is a crisis with very little tools to support decision-taking authorities, and very little is known about the Covid disease. The need to know more, and fast, highlighted numerous challenges in the whole data pipeline processes. This paper describes the decisions taken and processes developed to build a unified database to support several secondary usages of clinical data, including governance and research. HUG had to answer to 5 major waves of COVID-19 patients since the beginning of 2020. In this context, a database for COVID-19 related data has been created to support the governance of the hospital in their answer to this crisis. The principles about this database were a) a clearly defined cohort; b) a clearly defined dataset and c) a clearly defined semantics. This approach resulted in more than 28 000 variables encoded in SNOMED CT and 1 540 human readable labels. It covers more than 216 000 patients and 590 000 inpatient stays. This database is used daily since the beginning of the pandemic to feed the "Predict" dashboards of HUG and prediction reports as well as several research projects.

Adaptive Time-Dependent Priors and Bayesian Inference to Evaluate SARS-CoV-2 Public Health Measures Validated on 31 Countries.

With the rapid spread of the SARS-CoV-2 virus since the end of 2019, public health confinement measures to contain the propagation of the pandemic have been implemented. Our method to estimate the reproduction number using Bayesian inference with time-dependent priors enhances previous approaches by considering a dynamic prior continuously updated as restrictive measures and comportments within the society evolve. In addition, to allow direct comparison between reproduction number and introduction of public health measures in a specific country, the infection dates are inferred from daily confirmed cases and confirmed death. The evolution of this reproduction number in combination with the stringency index is analyzed on 31 European countries. We show that most countries required tough state interventions with a stringency index equal to 79.6 out of 100 to reduce their reproduction number below one and control the progression of the pandemic. In addition, we show a direct correlation between the time taken to introduce restrictive measures and the time required to contain the spread of the pandemic with a median time of 8 days. This analysis is validated by comparing the excess deaths and the time taken to implement restrictive measures. Our analysis reinforces the importance of having a fast response with a coherent and comprehensive set of confinement measures to control the pandemic. Only restrictions or combinations of those have shown to effectively control the pandemic.