ABSTRACT
BACKGROUND: The progression of coagulation in COVID-19 patients with confirmed discharge status and the combination of autopsy with complete hemostasis parameters have not been well studied. OBJECTIVE: To clarify the thrombotic phenomena and hemostasis state in COVID-19 patients based on epidemiological statistics combining autopsy and statistical analysis. METHODS: Using autopsy results from 9 patients with COVID-19 pneumonia and the medical records of 407 patients, including 39 deceased patients whose discharge status was certain, time-sequential changes in 11 relevant indices within mild, severe and critical infection throughout hospitalization according to the Chinese National Health Commission (NHC) guidelines were evaluated. Statistical tools were applied to calculate the importance of 11 indices and the correlation between those indices and the severity of COVID-19. RESULTS: At the beginning of hospitalization, platelet (PLT) counts were significantly reduced in critically ill patients compared with severely or mildly ill patients. Blood glucose (GLU), prothrombin time (PT), activated partial thromboplastin time (APTT), and D-dimer levels in critical patients were increased compared with mild and severe patients during the entire admission period. The International Society on Thrombosis and Haemostasis (ISTH) disseminated intravascular coagulation (DIC) score was also high in critical patients. In the relatively late stage of nonsurvivors, the temporal changes in PLT count, PT, and D-dimer levels were significantly different from those in survivors. A random forest model indicated that the most important feature was PT followed by D-dimer, indicating their positive associations with disease severity. Autopsy of deceased patients fulfilling diagnostic criteria for DIC revealed microthromboses in multiple organs. CONCLUSIONS: Combining autopsy data, time-sequential changes and statistical methods to explore hemostasis-relevant indices among the different severities of the disease helps guide therapy and detect prognosis in COVID-19 infection.