Quality of evidence: B
A Tool to Early Predict Severe 2019-Novel Coronavirus Pneumonia (COVID-19): A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China
Authors: Gong et al.
Journal: Medrxiv preprint
Objectives: Find predictive markers of risk, provide an easy tool for clinical use
Strength of evidence: B (prospective, bio-informatics, limited assessment of comorbidities, limited n)
Methods/publication type: nomogram
multicenter - Wuhan and Guangdong, China (n= 189 patients for train cohort and 165 + 18 in the validation cohort).
using Risk Nomogram
Patients followed at least 15 days after admission.
Classification by s severe and non-severe group, respectively.
Compared demographic, clinical, and laboratory data between severe and non-severe groups.
On initial data, used LASSO algorithm and logistic regression model to construct a nomogram for risk prediction
Trained and validated the nomorgram
Highlights :
Older age, higher lactate dehydrogenase (LDH) and C-Reactive protein (CRP), direct bilirubin (DBIL), the coefficient of variation of red blood cell distribution width (RDW), blood urea nitrogen (BUN), and lower Albumin (ALB) on admission correlated with odds of severity
A nomogram was developed to predict severe cases
Patients with higher inflammation at admission seem at higher risk
Nomogram :
Nomogram predicting the severe COVID-19 probability in patients with COVID-19 infection was plotted. To use this nomogram in clinical management, an individual patient’s value is located on each variable axis, and a line is plotted upward to calculate the number of points received for each variable value. The sum of these scores is located on the Total points axis and draw a line straight down to get the probability of severe COVID-19.
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