| Autor: |
Maruti Kumaran, A.J. Gangemi, Michael R. Jacobs, O’Brien J, N. Guo, Navjot Kaur, S. Codella, Maulin Patel, Abramian O, J.M. Chowdhury, Halsey B, M. Zantah, Rohit Gupta, Buckey T, Jurkowski Z, Frankovich J, Jurkowski M, G.J. Criner, Huaqing Zhao, David Fleece, Dominguez-Castillo E, L.P. Vien, Prosniak R, Fernandez-Romero G, Chandra Dass, Stanley P, M. Zheng, Kahlon J, Earley C |
| Rok vydání: |
2021 |
| Předmět: |
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| DOI: |
10.1101/2021.02.26.21252256 |
| Popis: |
BackgroundSurges in COVID-19 disease cases can rapidly overwhelm healthcare resources; triaging to appropriate levels of care can assist in resource planning. At the beginning of the pandemic, we developed a simple triage tool, the Temple COVID-19 Pneumonia Triage Tool (TemCOV) based on a combination of clinical and radiographic features that are readily available on presentation to categorize and predict illness severity.MethodsWe prospectively examined 579 sequential cases admitted to Temple University Hospital who were assigned severity categories on admission. Our primary outcome was to compare the performance of TemCOV in predicting patients who have the highest likely of admission to the ICU at 24 and at 72 hours to other standard triage tools: the National Early Warning System (NEWS), the Modified Early Warning System (MEWS) and the CURB65 score. Additional endpoints included need for invasive mechanical ventilation (IMV) within 72 hours, total hospital admission charges, and mortality.Results26% of patients fell within our highest risk Category 4 and were more likely to require ICU admission at 24 hours (OR 11.51) and 72 hours (OR 8.6). Additionally they had the highest likelihood of needing IMV (OR 29.47) and in-hospital mortality (OR 2.37)., TemCOV performed similar to MEWS in predicting ICU admission at 24 hours (receive operator characteristic (ROC) curve area under the curve (AUC) 0.77 vs. 0.74, p=0.21) but better than NEWS2 and CURB65 (ROC AUC 0.77 vs. 0.69 and 0.77 vs. 0.64, respectively, pConclusionTemCOV is a simple triage score that can be used upon hospitalization in patients with COVID-19 that predicts the need for hospital resources such as ICU bed capacity, invasive mechanical ventilation and personnel staffing. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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