Predict occurrence of ICU admission, mechanical ventilation, or death in hospitalized patients with COVID-19
Early identification of patients with COVID–19 who may develop critical illness is important to aid in delivering proper treatment and optimize use of limited resources.
The COVID–GRAM predictive risk score was developed in collaboration with the National Health Commission of China from a retrospective cohort of patients diagnosed with COVID–19 before January 31, 2020.
Epidemiological, clinical, laboratory, and imaging data were collected from 575 hospitals in 31 provincial administrative regions in China.
10 variables at the time of admission were identified to be independently statistically significant predictors of critical illness:
- Age
- Unconsciousness
- Hemoptysis
- Dyspnea
- Number of comorbidities
- Cancer history
- CXR abnormality
- Neutrophil-to-lymphocyte ratio
- Lactate dehydrogenase
- Direct bilirubin
The risk score was validated with data from 4 additional cohorts hospitalized in China with COVID–19. It estimates the risk of developing critical illness (defined as requiring ICU admission, mechanical ventilation, or death.)
The accuracy of the risk score was assessed using the area under the receiver-operator characteristic curve (AUC). Based on data from the development cohort, the accuracy of the risk score was 0.88 (95% CI, 0.85–0.91). The AUC for patients in the epicenter at Hubei was 0.87 (95% CI, 0.83–0.91) and outside Hubei was 0.82 (95% CI, 0.73–0.90).
CURB–6 models, which have been used to classify the severity of community-acquired pneumonia, had an AUC of 0.75 (95% CI, 0.70–0.80) comparatively.
Liang W, Liang H, Ou L, et al.
Early identification of patients with COVID–19 who may develop critical illness is important to aid in delivering proper treatment and optimize use of limited resources.
The COVID–GRAM predictive risk score was developed in collaboration with the National Health Commission of China from a retrospective cohort of patients diagnosed with COVID–19 before January 31, 2020.
Epidemiological, clinical, laboratory, and imaging data were collected from 575 hospitals in 31 provincial administrative regions in China.
10 variables at the time of admission were identified to be independently statistically significant predictors of critical illness:
- Age
- Unconsciousness
- Hemoptysis
- Dyspnea
- Number of comorbidities
- Cancer history
- CXR abnormality
- Neutrophil-to-lymphocyte ratio
- Lactate dehydrogenase
- Direct bilirubin
The risk score was validated with data from 4 additional cohorts hospitalized in China with COVID–19. It estimates the risk of developing critical illness (defined as requiring ICU admission, mechanical ventilation, or death.)
The accuracy of the risk score was assessed using the area under the receiver-operator characteristic curve (AUC). Based on data from the development cohort, the accuracy of the risk score was 0.88 (95% CI, 0.85–0.91). The AUC for patients in the epicenter at Hubei was 0.87 (95% CI, 0.83–0.91) and outside Hubei was 0.82 (95% CI, 0.73–0.90).
CURB–6 models, which have been used to classify the severity of community-acquired pneumonia, had an AUC of 0.75 (95% CI, 0.70–0.80) comparatively.
Liang W, Liang H, Ou L, et al.
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