TY - JOUR
T1 - Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
AU - Elemam, Noha M.
AU - Hammoudeh, Sarah
AU - Salameh, Laila
AU - Mahboub, Bassam
AU - Alsafar, Habiba
AU - Talaat, Iman M.
AU - Habib, Peter
AU - Siddiqui, Mehmood
AU - Hassan, Khalid Omar
AU - Al-Assaf, Omar Yousef
AU - Taneera, Jalal
AU - Sulaiman, Nabil
AU - Hamoudi, Rifat
AU - Maghazachi, Azzam A.
AU - Hamid, Qutayba
AU - Saber-Ayad, Maha
N1 - Funding Information:
We would like to acknowledge the efforts and help of the COVID-lab team at the University of Sharjah, Prof. Rabih Halwani, Dr Abdul Wahid Ansari, Dr Narjes Saheb Sharif-Askari, and Dr Fatemeh Saheb Sharif-Askari.
Funding Information:
MS-A and RH are funded by the University of Sharjah Research Grants CoV-19 #0304 and CoV-19 #0308.
Publisher Copyright:
Copyright © 2022 Elemam, Hammoudeh, Salameh, Mahboub, Alsafar, Talaat, Habib, Siddiqui, Hassan, Al-Assaf, Taneera, Sulaiman, Hamoudi, Maghazachi, Hamid and Saber-Ayad.
PY - 2022/4/20
Y1 - 2022/4/20
N2 - Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
AB - Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
KW - Aritficial Intelligence
KW - COVID-19
KW - Machine Learning
KW - multiplex
KW - RNA seq
KW - ROC analysis
KW - transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85129533734&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2022.865845
DO - 10.3389/fimmu.2022.865845
M3 - Article
C2 - 35529862
AN - SCOPUS:85129533734
SN - 1664-3224
VL - 13
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 865845
ER -