Develop a severity score to better assess COVID-19
Assessing the severity of COVID-19 is difficult and in the absence of any diagnosis to better understand the progression of the disease, clinical care is difficult.
A new study used an analyzer to examine host proteins to identify the severity of COVID-19. According to the researchers, it outperformed other candidate gravity biomarkers, including interleukin 6 (IL-6).
The data was presented during the virtual sessions of the 23rd annual Making a Difference in Infectious Disease Meeting 2021.
The analyzer examined TNF-bound apoptosis-induced ligand (TRAIL), gamma interferon-induced protein (IP-10), and C-reactive protein (CRP). Investigators used machine learning to incorporate TRAIL, IP-10, and CRP levels into a score (0-100). They created 4 To make the model clinically intuitive, 4 score workbooks were developed.
âIn the area under the analysis of the Receiver Operating Characteristic (AUC) curve, the score with AUC 0.86 (95% confidence interval, CI: 0.81-0.91) outperformed the others. Candidate severity biomarkers, including IL-6 (n = 139; AUC 0.77 (95% CI: 0.67-0.87); p = 0.03), âthe investigators reported. âPerformance was also assessed by demonstrating a significant trend in the likelihood of severe outcomes ranging from low to high score classes (p
The analyzer was developed by MeMed, based in Haifa, Israel. Contagion spoke with Tanya Gottlieb, PhD, MBA, Vice President, Scientific Affairs, MeMed about the analyzer, including how it can help identify what’s going on in the immune system and the potential role of the machine learning in clinical care.