Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting

Abstract

Objective: This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging.

Methods: A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O₂-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of <0.05 was considered statistically significant.

Results: The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups.

Conclusions: The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19.

Keywords: Computed tomography; Prediction of COVID-19; Pretest probability; Vital parameters.

Fig. 1

Predictive value of clinical parameters compared to CT. This figure shows the diagnostic value of vital parameters alone for the diagnosis of COVID-19 and compares its accuracy with that of CT (results obtained with the baseline model). The point estimate of CT accuracy remains above the curve in all subfigures, indicating that vital parameters alone are not more accurate than a CT scan.

Fig. 2

Probability of COVID-19 after CT. This figure shows the probability of COVID-19 before CT on the x-axis and the calculated and extrapolated posttest probabilities after a positive (blue line) or negative (red line) CT scan. It becomes apparent that for patients in the low-risk group, even after a positive scan, the probability of COVID-19 is only 50%, casting doubt on the appropriateness of using CT in this patient subset. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)