Background/Objectives Nursing home (NH) residents are a vulnerable population, susceptible to respiratory disease outbreaks such as coronavirus disease 2019 (COVID‐19). Poor outcome in COVID‐19 is at least partly attributed to hypercoagulability, resulting in a high incidence of thromboembolic complications. It is unknown whether commonly used antithrombotic therapies may protect the vulnerable NH population with COVID‐19 against mortality. This study aimed to investigate whether the use of oral antithrombotic therapy (OAT) was associated with a lower mortality in NH residents with COVID‐19. Design A retrospective case‐series Setting 14 NH facilities from the NH organization Envida, Maastricht, the Netherlands Participants 101 NH residents with COVID‐19 were enrolled. Measurements The primary outcome was all‐cause mortality. The association between age, sex, comorbidity, OAT, and mortality was assessed using logistic regression analysis. Results Overall mortality was 47.5% in NH residents from 14 NH facilities. Age, comorbidity and medication use were comparable among NH residents who survived and who died. OAT was associated with a lower mortality in NH residents with COVID‐19 in the univariable analysis (OR 0.89 95%CI 0.41‐1.95). However, additional adjustments for sex, age and comorbidity, attenuated this difference. Mortality in males was higher compared with female residents (OR 3.96 (95%CI 1.62‐9.65)). Male residents who died were younger compared to female residents (82.2 (SD 6.3) vs. 89.1 years (SD 6.8), p<.001). Conclusion NH residents in the 14 facilities we studied were severely affected by the COVID‐19 pandemic, with a mortality of 47.5%. Male NH residents with COVID‐19 had worse outcomes than females. We did not find evidence for any protection against mortality by OAT, necessitating further research into strategies to mitigate poor outcome of COVID‐19 in vulnerable NH populations. This article is protected by copyright. All rights reserved.

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IntroductionCoronavirus disease 2019 (COVID-19) has a high burden on the healthcare system and demands information on the outcome early after admission to the emergency department (ED). Previously developed prediction models may assist in triaging patients when allocating healthcare resources. We aimed to assess the value of several prediction models when applied to COVID-19 patients in the ED.MethodsAll consecutive COVID-19 patients who visited the ED of a combined secondary/tertiary care center were included. Prediction models were selected based on their feasibility. The primary outcome was 30-day mortality, secondary outcomes were 14-day mortality, and a composite outcome of 30-day mortality and admission to the medium care unit (MCU) or the intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).ResultsA total of 403 ED patients were diagnosed with COVID-19. Within 30 days, 95 patients died (23.6%), 14-day mortality was 19.1%. Forty-eight patients (11.9%) were admitted to the MCU, 66 patients (16.4%) to the ICU and 152 patients (37.7%) met the composite endpoint. Eleven models were included: RISE UP score, 4C mortality score, CURB-65, MEWS, REMS, abbMEDS, SOFA, APACHE II, CALL score, ACP index and Host risk factor score. The RISE UP score and 4C mortality score showed a very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84 respectively, 95% CI 0.79-0.88 for both), for 14-day mortality (AUC 0.83, 95% CI: 0.79-0.88, for both) and for the composite outcome (AUC 0.79 and 0.77 respectively, 95% CI 0.75-0.84). The discriminatory performance of the RISE UP score and 4C mortality score was significantly higher compared to that of the other models.ConclusionThe RISE UP score and 4C mortality score have good discriminatory performance in predicting adverse outcome in ED patients with COVID-19. These prediction models can be used to recognize patients at high risk for short-term poor outcome and may assist in guiding clinical decision-making and allocating healthcare resources.