Biology Stack Exchange is a question and answer site for biology researchers, academics, and students. It only takes a minute to sign up.
Sign up to join this community
Clinical implications of the hematologic profile of COVID-19 patients including cytokine storm, coagulation profile and thrombophilic complications are starting to be recognized. Hypercoagulability could be the potential cause of death due to COVID-19: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585459/
How good is the hematologic profile, including hematologic markers (such as interleukins and others) in predicting if COVID-19 is severe, has mild manifestations or it is asymptomatic?
A recent pre-print has studied this by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalisation.
We identified 26 protein biomarkers and 14 routine diagnostic markers (Fig. 4a, Supp. Fig. SF20) that correlate with the time between the first sampling point and release from inpatient care. The proteomic signature associated with longer duration of inpatient treatment and hence more severe COVID-19 progression includes upregulated components of the complement system (C1QA, C1QB, C1QC) and reflects altered coagulation (KLKB1, PLG, SERPIND1) and inflammation (CD14, B2M, SERPINA3, CRP, GPLD1, PGLYRP2, AHSG).
They used a learning classifier to then predict survival in critically ill patients:
In a last step, we established a machine learning model based on parenclitic networks52,53 (Methods) to predict survival in critically ill patients (WHO grade 7, n=49) from the proteome of the earliest single time point sample at WHO grade 7 for a particular patient. On the test subjects (the data of whom were excluded when training the machine learning model), high prediction accuracy was achieved, with AUC = 0.81 for the receiver-operating characteristic (ROC) curve. Of note, the model correctly predicted survival or adverse outcome weeks in advance (median time from prediction to outcome 39 days, interquartile range 16 - 64 days).
The 'predictive power' can be captured (not in a perfect way) by the AUC value - a value of 0 has the worst power to separate patients and 1 has perfect power, so 0.81 is pretty good overall. I haven't read the paper fully, but from a brief scan it looks as though their methodology is at least reasonably sound.
