Dynamic changes in coagulation parameters and correlation with disease severity and mortality in patients with COVID-19.

OBJECTIVE: This study aimed to describe the dynamic changes of coagulation parameters and evaluate the relationship between longitudinal coagulation parameters abnormalities and prognosis of COVID-19 patients. METHODS: We performed a retrospective study of 1131 COVID-19 patients. Longitudinal coagulation parameters and clinical outcomes were analyzed. RESULTS: Abnormal coagulation parameters were observed in patients with COVID-19, both at hospital admission (INR 2.3%, PT 7.9%, APTT 15.4%, TT 0.9%, FDP 2.3%, D-dimer 19.7%) and peak hospitalization (INR 4.8%, PT 13.4%, APTT 25.6%, TT 2.7%, FDP 10.4%, D-dimer 31.5%). Compared with non-severe patients with COVID-19, severe patients had a slightly higher INR, PT, APTT, whereas remarkably higher FDP and D-dimer (p < 0.05). On multivariate analysis, age > 60 years, male, obesity, comorbidity, abnormal D-dimer on hospital admission, and abnormal peak hospitalization PT, APTT, FDP and D-dimer were associated with COVID-19 severity. The extreme coagulation parameters abnormalities (PT > 16s, FDP > 50 ug/ml, and D-dimer > 5 ug/ml) were associated with a significantly higher mortality. CONCLUSION: Longitudinal coagulation parameters abnormalities are common in patients with COVID-19, and associated with disease severity and mortality. Monitoring coagulation parameters is advisable to improve the management of patients with COVID-19.

A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-Severe Adult Hospitalized Patients with COVID-19: A Retrospective Cohort Study.

INTRODUCTION: Due to the lack of clear direction (evidence) on the duration of viral shedding and thus potential for transmission, this retrospective study aimed to come up with a prediction model of prolonged coronavirus disease-19 (COVID-19) transmission or infection-spreading potential. METHODS: A total of 1211 non-severe patients with COVID-19 were retrospectively enrolled. Multivariate Cox regression was performed to identify the risk factors associated with long-term SARS-CoV-2 RNA shedding, and a prediction model was established. RESULTS: In the training set, 796 patients were divided into the long-term (> 21 days) group (n = 116, 14.6%) and the short-term (≤ 21 days) group (n = 680, 85.4%) based on their viral shedding duration. Multivariate analysis identified that age > 50 years, comorbidity, CD4-positive T-lymphocytes count (CD4 + T cell) ≤ 410 cells/ul, C-reactive protein (CRP) > 10 mg/L, and the corticosteroid use were independent risk factors for long-term SARS-CoV-2 RNA shedding. Incorporating the five risk factors, a prediction model, named as the CCCCA score, was established, and its area under the receiver operator characteristic curve (AUROC) was 0.87 in the training set and 0.83 in the validation set, respectively. In the validation set, using a cut-off of 8 points, we found sensitivity, specificity, positive predictive value, and negative predictive value of 51.7%, 92.2%, 33.3%, and 96.2%, respectively. Long-term SARS-CoV-2 RNA shedding increased from 14/370 (3.8%) in patients with CCCCA < 8 points to 15/45 (33.3%) in patients with CCCCA ≥ 8 points. CONCLUSION: Using the CCCCA score, clinicians can identify patients with long-term SARS-CoV-2 RNA shedding.