Internal and emergency medicine
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Predictive models for key outcomes of coronavirus disease 2019 (COVID-19) can optimize resource utilization and patient outcome. We aimed to design and internally validate a web-based calculator predictive of hospitalization and length of stay (LOS) in a large cohort of COVID-19-positive patients presenting to the Emergency Department (ED) in a New York City health system. The study cohort consisted of consecutive adult (> 18 years) patients presenting to the ED of Mount Sinai Health System hospitals between March 2020 and April 2020, diagnosed with COVID-19. ⋯ A calculator was made available under the following URL: https://covid19-outcome-prediction.shinyapps.io/COVID19_Hospitalization_Calculator/. This study yielded internally validated models that predict hospitalization risk in COVID-19-positive patients, which can be used to optimize resource allocation. Predictors of hospitalization and extended LOS included older age, CKD, fever, oxygen desaturation, elevated C-reactive protein, creatinine, and ferritin.
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Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. ⋯ This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.
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To test the prognostic performance of different scores, both specifically designed for patients with COVID-19 and generic, in predicting in-hospital mortality and the need for mechanical ventilation (MV). We retrospectively collected clinical data of patients admitted to the Emergency Department of the University Hospital AOU Careggi, Florence, Italy, between February 2020 and January 2021, with a confirmed infection by SARS-CoV2. We calculated the following scores: Sequential Organ Failure Assessment (SOFA) score, CALL score, 4C Mortality score, QUICK score, CURB-65 and MuLBSTA score. ⋯ Compared to survivors, non-survivors showed significantly higher values of all the prognostic scores (4C: 13 [10-15] vs 8 [4-10]; CALL: 11 [10-12] vs 9 [7-11]; QUICK: 4 [1-6] vs 0 [0-3]; SOFA: 5 [4-6] vs 4 [4-5]; CURB: 2 [1-3] vs 1 [0-1]; MuLBSTA: 11 [9-13] vs 9 [7-11], all p < 0.001). Discriminative ability evaluated by the Receiver Operating Curve analysis showed the following values of the Area under the Curve: 0.83 for 4C, 0.74 for CALL, 0.70 for QUICK, 0.68 for SOFA, 0.76 for CURB and 0.64 for MuLBSTA. The mortality rate significantly increased in increasing quartiles of 4C and CALL score (respectively, 2, 8, 24 and 54% for the 4C score and 1, 17, 33 and 68% for the CALL score, both p < 0.001). 4C and CALL score allowed an early and good prognostic stratification of patients admitted for pneumonia induced by SARS-CoV2.
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Risk factors for COVID-19-related outcomes have been variably reported. We used the standardised LACE index to examine admissions and in-hospital mortality associated with COVID-19. Data were collected in the pre-pandemic period (01-04-2019 to 29-02-2020) from 10,173 patients (47.7% men: mean age ± standard deviation = 68.3 years ± 20.0) and in the pandemic period (01-03-2019 to 31-03-2021) from 12,434 patients. ⋯ In conclusion, patients with LACE index scores ≥ 4 have disproportionally greater risk of COVID-19 hospital admissions and deaths, in support of previous studies in patients without COVID-19. However, of importance, our data also emphasise their increased risk in patients with COVID-19. Because the LACE index has a good predictive power of mortality, it should be considered for routine use to identify high-risk COVID-19 patients.