Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection

• Published in: PloS one Vol. 16; no. 7; p. e0254580
• Main Authors: Jiwa, Nasheena, Mutneja, Rahul, Henry, Lucie, Fiscus, Garrett, Zu Wallack, Richard
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 16-07-2021; Public Library of Science (PLoS)
• Subjects: Age; Asthma; Biology and Life Sciences; C-reactive protein; Cardiac arrhythmia; Cardiovascular disease; Coronary vessels; Coronaviruses; COVID-19; Critical care; Diabetes; Ethnicity; Evaluation; Health risks; Heart failure; Hospital patients; Hospitalization; Hospitals; Hypertension; Infections; Ischemia; Laboratories; Low income groups; Medicaid; Medical personnel; Medicare; Medicine; Medicine and Health Sciences; Mortality; Oxygen; Patient outcomes; Patients; Physical Sciences; Pneumonia; Research and Analysis Methods; Risk; Thrombosis; United States; Viral diseases; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0254580

Patients hospitalized with COVID-19 infection are at a high general risk for in-hospital mortality. A simple and easy-to-use model for predicting mortality based on data readily available to clinicians in the first 24 hours of hospital admission might be useful in directing scarce medical and personnel resources toward those patients at greater risk of dying. With this goal in mind, we evaluated factors predictive of in-hospital mortality in a random sample of 100 patients (derivation cohort) hospitalized for COVID-19 at our institution in April and May, 2020 and created potential models to test in a second random sample of 148 patients (validation cohort) hospitalized for the same disease over the same time period in the same institution. Two models (Model A: two variables, presence of pneumonia and ischemia); (Model B: three variables, age > 65 years, supplemental oxygen ≥ 4 L/min, and C-reactive protein (CRP) > 10 mg/L) were selected and tested in the validation cohort. Model B appeared the better of the two, with an AUC in receiver operating characteristic curve analysis of 0.74 versus 0.65 in Model A, but the AUC differences were not significant (p = 0.24. Model B also appeared to have a more robust separation of mortality between the lowest (none of the three variables present) and highest (all three variables present) scores at 0% and 71%, respectively. These brief scoring systems may prove to be useful to clinicians in assigning mortality risk in hospitalized patients.