Can we predict which COVID-19 patients will need transfer to intensive care within 24 hours of floor admission?

BACKGROUND: Patients with COVID-19 can present to the emergency department (ED) at any point during the spectrum of illness, making it difficult to predict what level of care the patient will ultimately require. Admission to a ward bed, which is subsequently upgraded within hours to an intensive care unit (ICU) bed, represents an inability to appropriately predict the patient's course of illness. Predicting which patients will require ICU care within 24 hours would allow admissions to be managed more appropriately.

METHODS: This was a retrospective study of adults admitted to a large health care system, including 14 hospitals across the state of Indiana. Included patients were aged ≥ 18 years, were admitted to the hospital from the ED, and had a positive polymerase chain reaction (PCR) test for COVID-19. Patients directly admitted to the ICU or in whom the PCR test was obtained > 3 days after hospital admission were excluded. Extracted data points included demographics, comorbidities, ED vital signs, laboratory values, chest imaging results, and level of care on admission. The primary outcome was a combination of either death or transfer to ICU within 24 hours of admission to the hospital. Data analysis was performed by logistic regression modeling to determine a multivariable model of variables that could predict the primary outcome.

RESULTS: Of the 542 included patients, 46 (10%) required transfer to ICU within 24 hours of admission. The final composite model, adjusted for age and admission location, included history of heart failure and initial oxygen saturation of <93% plus either white blood cell count > 6.4 or glomerular filtration rate < 46. The odds ratio (OR) for decompensation within 24 hours was 5.17 (95% confidence interval [CI] = 2.17 to 12.31) when all criteria were present. For patients without the above criteria, the OR for ICU transfer was 0.20 (95% CI = 0.09 to 0.45).

CONCLUSIONS: Although our model did not perform well enough to stand alone as a decision guide, it highlights certain clinical features that are associated with increased risk of decompensation.