Experience with sotrovimab treatment of SARS-CoV-2-infected patients in Denmark.

AIMS: To evaluate the experience with use of sotrovimab following severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in high-risk groups. METHODS: In a nationwide, population-based cohort study, we identified all individuals treated with sotrovimab (N = 2933) and stratified them by 4 high-risk groups: (A) malignant haematological disease, (B) solid organ transplantation, (C) anti-CD20 therapy ≤1 year and (D) other risks. Cox regression analysis was used to calculate hazard ratios for hospitalization, death and associated prognostic factors. RESULTS: Of 2933 sotrovimab-treated individuals, 83% belonged to high-risk groups (37.6% haematological malignancy, 27.4% solid organ transplantation and 17.5% treatment with anti-CD20 ≤1 year). Only 17.8% had other risks (11.8% were pregnant, 10.7% primary immunodeficiency, 21.2% other malignancy, 4.3% received anti-CD20 >1 year and 52.0% other/unknown causes). Within 90 days of infusion, 30.2% were hospitalized and 5.3% died. The main prognostic factors were the predefined high-risk groups, mainly malignant haematological disease and age ≥65 years. Number of COVID-19 vaccines (≥3) was associated with a decreased risk of hospitalization. The Delta but not the Omicron BA.2 variant was associated with a higher risk of death compared to the BA.1 variant. CONCLUSION: More than 90% of the patients treated with sotrovimab belonged to the very high-risk groups as described in the Danish guidelines. Sotrovimab-treated individuals remained at a high risk of hospitalization and death which was strongly associated with the underlying immunocompromised state and age. Having received >3 COVID-19 vaccines was association with decreased risk of death and hospitalization.

Prophylactic anticoagulation with low molecular weight heparin in COVID-19: cohort studies in Denmark and Sweden.

OBJECTIVES: To evaluate safety and effectiveness of prophylactic anticoagulation with low molecular weight heparin (LMWH) in individuals hospitalised for COVID-19. METHODS: Using healthcare records from the Capital Region of Denmark (March 2020-February 2021) and Karolinska University Hospital in Sweden (February 2020-September 2021), we conducted an observational cohort study comparing clinical outcomes 30 days after admission among individuals hospitalised for COVID-19 starting prophylactic LMWH during the first 48 hours of hospitalisation with outcomes among those not receiving prophylactic anticoagulation. We used inverse probability weighting to adjust for confounders and bias due to missing information. Risk ratios, risk differences and robust 95% confidence intervals (CI) were estimated using binomial regression. Country-specific risk ratios were pooled using random-effects meta-analysis. RESULTS: We included 1692 and 1868 individuals in the Danish and Swedish cohorts. Of these, 771 (46%) and 1167 (62%) received prophylactic LMWH up to 48 hours after admission. The combined mortality in Denmark and Sweden was 12% (N = 432) and the pooled risk ratio was 0.91 (CI 0.60-1.38) comparing individuals who received LMWH to those who did not. The relative risk of ICU admission was 1.12 (0.76-1.66), while we observed no increased risk of bleeding 0.63 (0.13-2.94). The relative risk of venous thromboembolism was 0.80 (0.43-1.47). CONCLUSION: We found no benefit on mortality with prophylactic LMWH and no increased risk of bleeding among COVID-19 patients receiving prophylactic LMWH.

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark.

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.