Diagnostic performance of rapid antigen test for COVID-19 and the effect of viral load, sampling time, subject's clinical and laboratory parameters on test accuracy

Abstract

Background: Egypt was among the first 10 countries in Africa that experienced COVID-19 cases. The sudden surge in the number of cases is overwhelming the capacity of the national healthcare system, particularly in developing countries. Central to the containment of the ongoing pandemic is the availability of rapid and accurate diagnostic tests that could pinpoint patients at early disease stages. In the current study, we aimed to (1) Evaluate the diagnostic performance of the rapid antigen test (RAT) "Standard™ Q COVID-19 Ag" against reverse transcriptase quantitative real-time PCR (RT-qPCR) in eighty-three swabs collected from COVID-19 suspected individuals showing various demographic features, clinical and radiological findings. (2) Test whether measuring laboratory parameters in participant's blood would enhance the predictive accuracy of RAT. (3) Identify the most important features that determine the results of both RAT and RT-qPCR.

Methods: Diagnostic measurements (e.g. sensitivity, specificity, etc.) and receiver operating characteristic curve were used to assess the clinical performance of "Standard™ Q COVID-19 Ag". We used the support vector machine (SVM) model to investigate whether measuring laboratory indices would enhance the accuracy of RAT. Moreover, a random forest classification model was used to determine the most important determinants of the results of RAT and RT-qPCR for COVID-19 diagnosis.

Results: The sensitivity, specificity, and accuracy of RAT were 78.2, 64.2, and 75.9%, respectively. Samples with high viral load and those that were collected within one-week post-symptoms showed the highest sensitivity and accuracy. The SVM modeling showed that measuring laboratory indices did not enhance the predictive accuracy of RAT.

Conclusion: "Standard™ Q COVID-19 Ag" should not be used alone for COVID-19 diagnosis due to its low diagnostic performance relative to the RT-qPCR. RAT is best used at the early disease stage and in patients with high viral load.

Keywords: Accuracy; Diagnosis; Rapid antigen test; SARS-CoV-2.

Fig. 1

Distribution of participants with positive and negative results of RAT according to sampling time post-symptoms in days (x-axis) and Ct values as determined by RT-qPCR (y-axis). Sampling time post-symptom onset was classified into early (0–7 d), middle (8–16 d), and late (>16 d). RT-qPCR categories are indicated on the right side of the graph.

Fig. 2

Diagnostic performance of RAT A. Receiver operating characteristic curve (ROC) analyses showing the diagnostic performance of the RAT with an area under the curve (AUC) of 0.7 B–C. Support vector machine model. B. Top-ranked features based on their frequency of being selected after the cross-validation is the model. C. Plot showing the accuracy of feature combination in predicting the COVID-19 positive subjects as determined by RT-qPCR. The most accurate classifier gave an accuracy of 59.3% for the top 3-feature as revealed in B. D. Predicted class probability analyses evaluating the performance of the 3-features model. Each dot refers to the average prediction of one subject after cross-validation. Dark and light-colored dots indicate positive and negative cases by RT-qPCR. The misclassified subjects by the 3-feature model are labeled. The classification boundary for COVID-19 positive subjects lies at the center of the x-axis (x = 0.5, vertical dotted line). Values >0.5 indicate a probability of COVID-19 positive and closer to 1 indicate high probability. The confusion matrix shows the summary of the model performance.