In this study, we developed and validated a novel nomogram to predict COVID-19 infection among patients who were suspected viral pneumonia. This diagnostic nomogram mainly relies on CT findings. Our study found that COVID-19 pneumonia is not significantly different from other suspected viral pneumonia in clinical symptoms and signs. There was also no significant difference in blood routine test, liver and kidney function test. Thus, according to clinical symptoms and signs and laboratory examinations, the COVID-19 pneumonia is difficult to distinguish from other viral pneumonia. However, they have similar and different manifestations on lung imaging detected by CT scan. [10, 17] To this end, we have established this nomogram mainly based on lung imaging. All CT findings were analyzed from three aspects including distribution characteristics, morphology and density of pulmonary inflammation lesions.
The imaging features of viral pneumonia usually appear as multifocal ground glass opacities which correspond to pathological diffuse alveolar damage.According to univariate analysis, ground glass opacities, crazy paving pattern, a wedge-shaped or fan-shaped lesion parallel to or near the pleura, the distribution characteristics of bilateral lower lobes and peripheral distribution of lesions are the characteristic imaging manifestation of COVID-19 pneumonia. But, the crazy paving pattern on the basis of multivariable was unassociated with COVID-19 pneumonia, which may be due to other potential confounding factors. But this does not mean that crazy paving pattern in COVID-19 pneumonia are unimportant. In addition, many studies have shown that the crazy paving pattern formed by interlobular septal thickening which was regarded as a typical imaging of viral pneumonia. Therefore, we kept this factor in our model development. Another important imaging feature related to the characteristics of morphology and distribution, the wedge-shaped or fan-shaped lesion parallel to or near the pleura, which actually includes peripheral distribution of lesions. Because of their strong collinearity, it will seriously affect the accuracy of our research results. Therefore, the imaging features of peripheral distribution was removed.
No matter from univariate or multivariate analysis results, epidemiological history plays a central role in the clinical diagnosis of COVID-19 pneumonia. Lymphocyte count did not show enough predictive strength. It may be related to the fact that these patients in our study are mainly suspected virus infection, and their lymphocyte count are generally low, with little difference. In view of the WBC of patients with viral infection is usually not high[21, 22], although it was unassociated in multivariate analysis, we still kept it in the process of establishing the diagnostic nomogram.
Finally, the nomogram incorporates 4 items of the imaging features, epidemiological contact history and WBC count status. Nomogram is a visualization of regression analysis, which is more and more widely used in clinical disease diagnosis, prognosis evaluation and efficacy evaluation[23–26]. Our results show that the nomogram based on imaging features has good sensitivity and specificity in the diagnosis of COVID-19 pneumonia. Moreover, its discrimination for COVID-19 pneumonia is better than the first detection of viral nucleic acid. If there is a lack of virus nucleic acid test kit, COVID-19 pneumonia can be determined by lung CT preferentially.
In order to prove the calibration of the nomogram, clinical data was collected from different institutions. As is well known, the internal validity associated with the explanation of the results, and the external validity related to the generalizability of the results[27, 28]. Through the internal and external validation data set analysis, the calibration of our nomogram has been proved to be highly consistent. This means that our nomogram may be popularized and applied widely in other hospital. However, to evaluate its clinical usefulness, it depends on how much it benefits the patient, not just its popularization. DCA is an novel method[30, 31], it offers insight into clinical consequences on the basis of threshold probability, from which the net benefit could be derived. The DCA showed that if we choose to diagnose COVID-19 pneumonia with a 60% threshold probability, 40 out of every 100 people will benefit.
Our study has several limitations. Firstly, only 178 patients were included in primary cohort and another hospital was selected for external validation (116 patients). Whether this nomogram is applicable to patients with other areas background is still unclear. A large number of patients as data need to be collected to verify its clinical application. Secondly, this nomogram is mainly used to identify COVID-19 pneumonia in the patients with suspected viral pneumonia, not all types of pneumonia. Although the decrease of lymphocyte count is more common among COVID-19 pneumonia, not observed in our study. It may be related to our inclusion criteria.