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Clinical Characteristics Predicting Progression of COVID-19
18 Pages Posted: 20 Feb 2020
More...Abstract
Background: With the outbreak of COVID-19 from Wuhan, Hubei Province, China since January 2020, there is a tremendous pressure on medical resources. We studied predictive factors of progression to severe disease to facilitate proper allocation of patients to different level of medical facilities.
Methods: All COVID-19 patients admitted to our hospital from 20 Jan 2020 were enrolled and follow-up until16 Feb 2020.The patients were divided into two groups: stable non-severe and progressive to severe diseases. Clinical data was prospectively collected and compared between the two groups.
Findings: Forty-nine COVID-19 patients, mean age: 43·6±17·1 years, 63·3% male, were enrolled. Sixteen (32·7%) had at least one comorbidity (hypertension, diabetes mellitus, cardiovascular disease and lung disease). Thirty-four 34 (69·4%) had stable non-severe disease and 15 (30·6%) progressed to severe disease. Univariate analysis showed that comorbidity, age >50, lymphocyte counts <1500 /μL and serum ferritin >400 ng/mL at presentation were predictive of progression to severe diseases. Seventy-three percent of patients (11/15) with three or all four risk factors progressed to severe disease, requiring intubation or intensive unit care as compared with 11·8% (4/34) of subjects with 0-2 risk factors (odds ratio 6·2,95 % CI: 1·7 to 22·8, p =0·006). None of the subjects with absence of all 4 risk factors progressed to more severe diseases.
Interpretation: Around one-fifth of patients with COVID-19 will progress to severe diseases.Four simple clinical parameters at presentation, namely comorbidity, age, lymphocyte counts and serum ferritin were able to identify a group of patients with low risk of progression. This will greatly facilitate optimal utilization of the very tight medical resources in places with huge patient loads.
Funding Statement: This work was funded by the Special Emergency Research Project for Novel Coronavirus Pneumonia of Chinese PLA General Hospital (20EP013), Medical big data and artificial intelligence development fund of Chinese PLA General Hospital (2019MBD-024), and the Capital Characteristic Clinic Project of Beijing Municipal Science and Technology Commission (Z181100001718034).
Declaration of Interests: The authors declare no competing interests.
Ethics Approval Statement: The study was approved by the Ethics Committees of the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China (2020005D). Written informed consent was waived in view of the new emerging infectious diseases in a designated hospital.
Keywords: COVID-19, prediction, risk factors, severity of disease
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