This is a summary, written by members of the CITF Secretariat, of:

Su CY, Zhou S, Gonzalez-Kozlova E, Butler-Laporte G, Brunet-Ratnasingham E, Nakanishi T, Jeon W, Morrison DR, Laurent L, Afilalo J, Afilalo M, Henry D, Chen Y, Carrasco-Zanini J, Farjoun Y, Pietzner M, Kimchi N, Afrasiabi Z, Rezk N, Bouab M, Petitjean L, Guzman C, Xue X, Tselios C, Vulesevic B, Adeleye O, Abdullah T, Almamlouk N, Moussa Y, DeLuca C, Duggan N, Schurr E, Brassard N, Durand M, Del Valle DM, Thompson R, Cedillo MA, Schadt E, Nie K, Simons NW, Mouskas K, Zaki N, Patel M, Xie H, Harris J, Marvin R, Cheng E, Tuballes K, Argueta K, Scott I; Mount Sinai COVID-19 Biobank Team; Greenwood CMT, Paterson C, Hinterberg MA, Langenberg C, Forgetta V, Pineau J, Mooser V, Marron T, Beckmann ND, Kim-Schulze S, Charney AW, Gnjatic S, Kaufmann DE, Merad M, Richards JB. Circulating proteins to predict COVID-19 severity. Sci Rep. 2023 Apr 17;13(1):6236. doi: 10.1038/s41598-023-31850-y.

The results and/or conclusions contained in the research do not necessarily reflect the views of all CITF members.

A CITF-funded study, published in Scientific Reports, found circulating proteins measured in the early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Predicting COVID-19 severity is difficult and the biological pathways involved are not fully understood. This study relied on data from two cohorts, including the CITF-funded Biobanque québécoise de la COVID-19 (BQC19), and was led by Dr. Brent Richards (McGill University), in collaboration with Drs. Daniel Kauffman (University of Montreal), and Vincent Mooser (McGill University).

Key findings: 

  • A small subset of 92 circulating proteins found in sera samples from individuals with severe COVID-19Severe COVID-19 was defined as individuals who died or required any form of oxygen supplementation. and critical COVID-19Critical COVID-19, defined as individuals who died or experienced severe respiratory failure (requiring non-invasive ventilation, high flow oxygen therapy, intubation, or extracorporeal membrane oxygenation). were strong predictors of COVID-19 severity.
  • The sensitivitySensitivity (true positive rate) is the probability of a correct positive result. of the protein model at identifying severe COVID-19 cases was 73.2% and for critical COVID-19 cases was 74.3%.
  • The proteomic model was able to predict severe COVID-19, with an accuracy of 86%, as predicted by the AUC (area under the curve) analysis. The positive predictive valuePositive predictive value is the probability that a person with a positive test result actually has the disease/infection. was 89%, while the negative predictive valueNegative predictive value is the probability that a person with a negative test result is truly free of disease/infection. was 57.3%. These results suggest that a protein model could predict severe COVID-19 with relatively high confidence.

4701 circulating human (as opposed to viral) proteins in two independent cohorts totaling 986 individuals were measured. In the BQC19 cohort, the mean age across all samples was 65.3 years and 52% of the cohort were men. In the Mount Sinai cohort, the mean age was 59.6 years, and 58.2% of the cohort were men. In both cohorts, people with severe or critical COVID-19 were older and male, and men had a tendency of developing severe or critical COVID-19 at a younger age than women.