Background In order for healthcare systems to prepare for future waves of COVID-19, an in-depth understanding of clinical predictors is essential for efficient triage of hospitalized patients. Methods We performed a retrospective cohort study of 259 patients admitted to our hospitals in Rhode Island to examine differences in baseline characteristics (demographics and comorbidities) as well as presenting symptoms, signs, labs, and imaging findings that predicted disease progression and in-hospital mortality. Results Patients with severe COVID-19 were more likely to be older (p = 0.02), Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or have COPD (15.4% vs. 6.6%, p = 0.02). In multivariate regression, Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1–3.9) and diabetes (aOR 2.2, 95%CI: 1.3–3.9) were independent predictors of severe disease, while older age (aOR 1.04, 95% CI: 1.01–1.07), admission from a nursing facility (aOR 2.7, 95% CI 1.1–6.7), and hematological co-morbidities predicted mortality (aOR 3.4, 95% CI 1.1–10.0). In the first 24 hours, respiratory symptoms (aOR 7.0, 95% CI: 1.4–34.1), hypoxia (aOR 19.9, 95% CI: 2.6–152.5), and hypotension (aOR 2.7, 95% CI) predicted progression to severe disease, while tachypnea (aOR 8.7, 95% CI: 1.1–71.7) and hypotension (aOR 9.0, 95% CI: 3.1–26.1) were associated with increased in-hospital mortality. Conclusions Certain patient characteristics and clinical features can help clinicians with early identification and triage of high-risk patients during subsequent waves of COVID-19.
Objective We aimed to externally validate the predictive performance of two recently developed COVID‐19‐specific prognostic tools, the COVID‐GRAM and CALL scores, and prior prognostic scores for community‐acquired pneumonia (CURB‐65), viral pneumonia (MuBLSTA) and H1N1 influenza pneumonia (Influenza risk score) in a contemporary US cohort. Methods We included 257 hospitalised patients with laboratory‐confirmed COVID‐19 pneumonia from three teaching hospitals in Rhode Island. We extracted data from within the first 24 hours of admission. Variables were excluded if values were missing in >20% of cases, otherwise, missing values were imputed. One hundred and fifteen patients with complete data after imputation were used for the primary analysis. Sensitivity analysis was performed after the exclusion of one variable (LDH) in the complete dataset (n = 257). Primary and secondary outcomes were in‐hospital mortality and critical illness (mechanical ventilation or death), respectively. Results Only the areas under the receiver‐operating characteristic curves (RO‐AUC) of COVID‐GRAM (RO‐AUC = 0.775, 95% CI 0.525‐0.915) for in‐hospital death, and CURB65 for in‐hospital death (RO‐AUC = 0.842, 95% CI 0.674‐0.932) or critical illness (RO‐AUC = 0.766, 95% CI 0.584‐0.884) were significantly better than random. Sensitivity analysis yielded similar trends. Calibration plots showed better agreement between the estimated and observed probability of in‐hospital death for CURB65, compared with COVID‐GRAM. The negative predictive value (NPV) of CURB65 ≥2 was 97.2% for in‐hospital death and 88.1% for critical illness. Conclusions The COVID‐GRAM score demonstrated acceptable predictive performance for in‐hospital death. The CURB65 score had better prognostic utility for in‐hospital death and critical illness. The high NPV of CURB65 values ≥2 may be useful in triaging and allocation of resources.
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