Apply Four Laboratory Characteristics to Classify Critical Patients With COVID-19 After Admission

Abstract

Background The sudden outbreaking of COVID-19 worldwide has brought into sharp increased burden of economic and treatment in worldwide. All confirmed patients with different severity not only share the limited healthcare systems simultaneously but increase the risk of cross-infection among patients and health care workers. Hence, effective separation of critical COVID-19 patients from the common COVID-19 will be the key to success for ensuring critical patients to obtain treatment priorities and avoiding cross-infections in the hospital. Methods: A total of 105 patients with complete medical records were collected, including 84 blood samples of patients who confirmed in the First Affiliated Hospital of the University of Science and Technology at Anhui and 25 blood samples of patients in two hospitals at Shantou. Series of machine learning tools were introduced to explore and validate the most significant laboratory characteristics. Meanwhile, we compared it to three current popular assessment systems for pneumonia by using three methods, including the AUC index, NRI index and the net benefit. Results: We identified four significant potential laboratory characteristics for the classification of critical patients, including C-reactive protein, albumin, globulin, and sodium levels. The results also suggested the accurate and prediction efficacy of these selected indicators are the highest. Conclusions In conclusion, four easily available and low-cost laboratory characteristics appear to be import predictors of classification in critical patients after hospital admission. They guide therapeutic options and help clinicians make clinical decisions. Hence, we believe that such classification is essential for a more rational allocation scarce medical resource.