Risk score may help predict occurrence of critical illness in hospitalised patients with COVID-19
By Denise Baez
NEW YORK -- May 13, 2020 -- A clinical risk score can help clinicians identify patients with coronavirus disease 2019 (COVID-19) who may subsequently develop critical illness at the time of hospital admission, according to a study published in JAMA Internal Medicine.
The tool, dubbed COVID-GRAM, uses 10 variables that are generally readily available at hospital admission to predict critical illness.
“Early identification of patients with COVID-19 who may develop critical illness is of great importance and may aid in delivering proper treatment and optimising use of resources,” wrote Wenhua Liang, MD, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, and colleagues. “If the patient’s estimated risk for critical illness is low, the clinician may choose to monitor, whereas high-risk estimates might support aggressive treatment or admission to the intensive care unit (ICU).”
For the development cohort, the researchers used data from the 1,590 patients with COVID-19 who were hospitalised (575 hospitals) between February 20, 2020, and March 17, 2020. A total of 72 variables measured at hospital admission were entered into the selection process. To validate the risk score, the researchers used data from 710 patients with COVID-19 from hospitals that were not included in the development cohort.
The accuracy of the risk score was assessed using the area under the receiver-operator characteristic curve (AUC). Accuracy was also compared with CURB-6 models, which have been used in classification of the severity of community-acquired pneumonia. The severity of COVID-19 was based on the American Thoracic Society guidelines for community-acquired pneumonia.
At hospital admission, 24 (1.5%) of the patients were considered to be severe and the rest (98.5%) were considered to be mild. A total of 131 (8.2%) patients eventually developed critical illness. The overall mortality was 3.2%.
Overall, the mean (SD) age of patients in the cohort was 48.9 (15.7) years, 57.3% were male, and 25.1% had at least 1 coexisting condition, the most common being hypertension (16.9%) and diabetes (8.2%).
After LASSO regression, 19 variables remained significant predictors of critical illness, and after logistic regression, 10 variables that were independently statistically significant predictors of critical illness were included in risk score. These variables included chest radiography (CXR) abnormality (odds ratio [OR] = 3.39; 95% confidence interval [CI], 2.14-5.38; P < 0.001), age (OR = 1.03; 95% CI, 1.01-1.05; P = 0.002), haemoptysis (OR = 4.53; 95% CI, 1.36-15.15; P = 0.01), dyspnoea (OR = 1.88; 95% CI, 1.18-3.01; P = 0.01), unconsciousness (OR = 4.71; 95% CI, 1.39-15.98; P = 0.01), number of comorbidities (OR = 1.60; 95% CI, 1.27-2.00; P < 0.001), cancer history (OR = 4.07; 95% CI, 1.23-13.43; P = 0.02), neutrophil-to-lymphocyte ratio (OR = 1.06; 95% CI, 1.02-1.10; P = 0.003), lactate dehydrogenase (OR = 1.002; 95% CI, 1.001-1.004; P < 0.001), and direct bilirubin (OR = 1.15; 95% CI, 1.06-1.24; P = 0.001).
The mean AUC based on data from the development cohort was 0.88 (95% CI, 0.85-0.91) and the mean AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The predictive value of COVID-GRAM was higher than the CURB-6 model, which had an AUC of 0.75 for correct prediction of development of critical illness (P < 0.001).
“We deliberately did not categorise risk into low-, moderate-, and high-risk groups, as we believe that clinicians are better informed by calculating the risk estimate for each individual patient and making decisions based on local or regional conditions,” the authors wrote. “For example, in areas with good access to clinical and supportive care, patient outcomes might be optimised by deciding to provide more aggressive care to moderate risk patients. In contrast, in areas with high case volume and/or limited resources, the decision might be to provide less aggressive care to moderate-risk patients to maximise availability of ICU beds and ventilators.”
Potential limitations of the study include a modest sample size for constructing the risk score and a relatively small sample for validation. The data for score development and validation are entirely from China, which could potentially limit the generalizability of the risk score in other areas of the world. Additional validation studies of the COVID-GRAM from areas outside China should be completed.
The score has been translated into an online risk calculator that is freely available to the public.