Current best evidence for clinical care (more info)
Importance: Early identification of patients with novel corona virus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.
Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China.
Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.
Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death.
Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://22.214.171.124/).
Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.
|Discipline / Specialty Area||Score|
|Family Medicine (FM)/General Practice (GP)||
|General Internal Medicine-Primary Care(US)||
|Pediatric Hospital Medicine||
|Pediatric Emergency Medicine||
The magnitude of the OR for age, neutrophil to lymphocyte ratio, and lactate dehydrogenase are so low that these variables may drop out if a validation study is conducted in Europe or N America.
Most clinicians have already been using the variables included in the risk score developed in this paper. Its virtue is that it provides an estimation the likelihood of critical illness in hospitalized patients with COVID-19 based on a logistic model. For this reason, it will be very useful to guide clinicians in their decision to derive patients to A&E Departments as well as to admit patients into ICUs. One rather surprising aspect is that the risk score does not include an acute phase reactant among the risk variables. Certainly, one variable that has proved to be useful in the identification of complications is the level of ferritine –which the models and risk score do not evaluate. Further work will be needed to validate the risk score developed in this article using data from patients in other countries. The role of additional variables should be evaluated in order to provide more precise risk scores. Nevertheless, this is a timely and important contribution.
Clinical risk score and a web-based risk calculator to predict the development of critical illness among hospitalized COVID-19 infected patients.
It is not clear what value this score may have in practice. Several of the variables in the score are "common sense", or are well-known predictors of outcomes in patients with COVID-19 or other respiratory illness, or in some cases markers of existing critical illness (unconscious on presentation), which if present would preclude the need to use such a tool.
This is a very useful article during the current COVID-19 pandemic crisis. The authors of this article developed a risk score and web-based calculator to estimate the risk of developing critical illness among patients with COVID-19 based on 10 variables which are commonly measured on admission to the hospital. At this time when we are still figuring out and learning more about COVID-19 pathogenesis, this article provides useful information in the learning. Although it has several limitations, including, but not limited to, small sample size and no validation of the risk scoring with any other established scoring scale, it still provides useful information.
We will see more of these scores. It will be interesting to see if this score works in other jurisdictions and different populations. It would be good to encourage similar score definitions so that international comparisons are made. Other parameters may come forward in other populations but if this forms the core of every analysis, we can begin to shape population characteristics.
With this COVID-19 pandemic, every emergency physician is evaluating patients for suspected COVID-19 infection. Early identification of patients who may develop critical illness is helpful for making appropriate disposition. The development of a risk score based on characteristics of COVID-19 patients in the ED may help predict a patient’s risk for developing critical illness.
Aside from my skepticism about any data emerging from China, the article is on adult disease and does not include pediatric patients. Although pediatric patients are at significantly lower risk overall, identifying those at higher risk for severe disease would be helpful, so I'm hopeful a similar effort is underway for the pediatric age range.
This article is about adult patients and probably is not useful for children because the clinical manifestations and risk factors are different.