Prediction of the clinical outcome of COVID-19 patients using T lymphocyte subsets with 340 cases from Wuhan, China: a retrospective cohort study and a web visualization tool

Qibin Liu; Xuemin Fang; Shinichi Tokuno; Ungil Chung; Xianxiang Chen; Xiyong Dai; Xiaoyu Liu; Feng Xu; Bing Wang; Peng Peng

We believe by looking at some of the patient's basic characteristics such as age and underlying diseases, together with the T lymphocyte subsets measures, could be a quick way to shed light on the patient's prognosis, during the time of pressure and emergency. In order for our findings to be more applicable for public health workers fighting at the frontline, we have developed an interactive web data visualization tool to implement the algorithm and made it accessible for the world at the following web address: https://rpubs.com/mindyfang/covid19. We did not use other lab tests such as the regular blood test items in our analysis because they are less differentiative than the T cell subset measures; and 3. We hope our interactive web tool could be utilized for quick use, therefore keeping as few input items as needed seems to be a more practical choice. Figure 6b shows the k-means clustering result using the Wuhan Pulmonary Hospital data. After multi-dimensional data transformation, the algorithm separates the death group and the discharged group as shown in the graph. A proportion of the discharged cases had similar profiles with the death cases, which had made it difficult for the algorithm to differentiate them apart. However, by using this algorithm, it is possible to identify a large number of patients with relatively good prognosis.