Search

Background: Mortality attributable to coronavirus disease-19 (COVID-19) 2 infection occurs mainly through the development of viral pneumonia-induced acute respiratory distress syndrome (ARDS)., Research Question: The objective of the study is to delineate the clinical profile, predictors of disease progression, and 30-day mortality from ARDS using the Veterans Affairs Corporate Data Warehouse., Study Design and Methods: Analysis of a historical cohort of 7,816 hospitalized patients with confirmed COVID-19 infection between January 1, 2020, and August 1, 2020. Main outcomes were progression to ARDS and 30-day mortality from ARDS, respectively., Results: The cohort was comprised predominantly of men (94.5%) with a median age of 69 years (interquartile range [IQR] 60-74 years). 2,184 (28%) were admitted to the intensive care unit and 643 (29.4%) were diagnosed with ARDS. The median Charlson Index was 3 (IQR 1-5). Independent predictors of progression to ARDS were body mass index (BMI) ≥40 kg/m 2 , diabetes, lymphocyte counts <700 × 109/L, LDH >450 U/L, ferritin >862 ng/ml, C-reactive protein >11 mg/dL, and D-dimer >1.5 ug/ml. In contrast, the use of an anticoagulant lowered the risk of developing ARDS (OR 0.66 [95% CI 0.49-0.89]. Crude 30-day mortality rate from ARDS was 41% (95% CI 38%-45%). Risk of death from ARDS was significantly higher in those who developed acute renal failure and septic shock. Use of an anticoagulant was associated with 2-fold reduction in mortality. Survival benefit was observed in patients who received corticosteroids and/or remdesivir but there was no advantage of combination therapy over either agent alone., Conclusions: Among those hospitalized for COVID-19, nearly 1 in 10 progressed to ARDS. Septic shock, and acute renal failure are the leading causes of death in these patients. Treatment with either remdesivir and corticosteroids reduced the risk of mortality from ARDS. All hospitalized patients with COVID-19 should be placed at a minimum on prophylactic doses of anticoagulation.

Objective: Our objective is to compare the predictive accuracy of four recently established outcome models of patients hospitalized with coronavirus disease 2019 (COVID-19) published between January 1st and May 1st 2020., Methods: We used data obtained from the Veterans Affairs Corporate Data Warehouse (CDW) between January 1st, 2020, and May 1st 2020 as an external validation cohort. The outcome measure was hospital mortality. Areas under the ROC (AUC) curves were used to evaluate discrimination of the four predictive models. The Hosmer-Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases., Results: During the study period, 1634 unique patients were identified. The mean age of the study cohort was 68.8±13.4 years. Hypertension, hyperlipidemia, and heart disease were the most common comorbidities. The crude hospital mortality was 29% (95% confidence interval [CI] 0.27-0.31). Evaluation of the predictive models showed an AUC range from 0.63 (95% CI 0.60-0.66) to 0.72 (95% CI 0.69-0.74) indicating fair to poor discrimination across all models. There were no significant differences among the AUC values of the four prognostic systems. All models calibrated poorly by either overestimated or underestimated hospital mortality., Conclusions: All the four prognostic models examined in this study portend high-risk bias. The performance of these scores needs to be interpreted with caution in hospitalized patients with COVID-19., Competing Interests: The authors have declared that no competing interests exist