Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in COVID-19 Patients

Abstract Background In critically ill COVID-19 patients, the crucial turning point before critical illness onset (CIO) remain largely unknown, and the combination of baseline risk factors with the turning point during hospitalization was rarely reported.Methods In this retrospective cohort study, 1150 consecutively admitted patients with confirmed COVID-19 were enrolled, including 296 critical and 854 non-critical patients. We compared the differences of all the clinically tested indicators and their dynamic changes between critical and non-critical patients. Three prediction models were established and validated based on the risk factors at admission, and an online baseline predictive tool was developed. Linear mixed model (LMM) was applied for longitudinal data analysis in 296 critical patients throughout the hospitalization, to predict the likelihood and possible time of critical illness in COVID-19 patients. A crucial turning point, where several indicators will experience a greater and significantly continuous change before CIO, was defined as “burning point” in our study. This point indicates the deterioration of patient’s condition before CIO.Results We established a novel two-checkpoint system to predict critical illness for COVID-19 patients in which the first checkpoint happened at patient admission was assessed by a baseline prediction model to project the likelihood of critical illness based on the variables selected from random forest and LASSO regression analysis, including age, SOFA score, neutrophil-to-lymphocyte ratio (NLR), D-dimer, lactate dehydrogenase (LDH), International Normalized Ratio (INR), and pneumonia area derived from CT images, which yields an AUC of 0.960 (95% confidence interval, 0.941-0.972) and 0.958 (0.936-0.980) in the training and testing sets, respectively. This model has been translated into a public web-based risk calculator. Furthermore, the second checkpoint (designated as “burning point” in our study) could be identified as early as 5 days preceding the CIO, and 12 (IQR, 7-17) days after illness onset. Seven most significant and representative “burning point” indicators were SOFA score, NLR, C-reactive protein (CRP), glucose, D-dimer, LDH, and blood urea nitrogen (BUN).Conclusions With this two-checkpoint prediction system, the deterioration of COVID-19 patients could be early identified and more intensive treatments could be started in advance to reduce the incidence of critical illness.