Importance of combining laboratory parameters and RAT in
diagnosing COVID-19 patients
Considering the intermediate diagnostic power (AUC = 0.7)(Figure 2 A) and low diagnostic performance of the RAT(Tables 2 and 3) , we investigated whether combining
laboratory indices measured in blood and RAT would enhance the true
identification of COVID-19 patients by the RAT. First, the SVM model
revealed that HB, urea, RAT and S. ferritin were the top-4 parameters
most frequently selected during the model building and cross validation
followed by the other features (Figure 2 B). Various
combination between these parameters (i.e. those listed in ascending
order in Figure 2 B ) yielded various accuracies in predicting
true COVID-19 cases. The highest prediction accuracy (59.3%) was
obtained when combining RAT with both HB and urea (top 2- features inFigure 2 B). Coupling RAT with HB, urea, S. ferritin and CRP
(top 5- ranked features) yielded slightly lower prediction accuracy
(58%) than the one produced by the 3-feature model. Combining all
features together revealed low prediction accuracy of 48%. Subsequent
evaluation of the “top 3-feature” model by predictive class
probability analyses (Figure 2 D) revealed a sensitivity of
75.4%, where this model correctly identified 43 as positive subjects
out of the 57 true positive ones (as determined by RT-qPCR). The model
specificity was 81.8%, where the model correctly identified nine
negative subjects out of the 11 true negative ones. The misclassified
subjects are labelled in figure 2 D .