Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Abstract

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.

Bias reduction in the logistic model parameters with the LogF(1,1) penalty under MAR assumption

“…Furthermore, the results from real life COVID-19 data also support the conclusion that the proposed LogF outperforms both the Firth and Ibrahim methods. These results from the application agree with the clinician's theory that admission to ICU for the COVID-19 patient is significantly explained by the three factors that is, conjuctivitis, unconsciousness and having a neurological condition [36,37]. We also note that using the complete case analysis alone in modeling may lead to incorrect logit model, as it may be determined that a predictor is not significant, when actually the reverse is true or otherwise.…”

Liver injury associated with the severity of COVID-19: A meta-analysis

“…Twenty-two studies ( 5 , 18 , 19 , 23 , 26 – 33 , 35 , 37 , 38 , 40 , 47 , 48 , 51 , 52 , 55 , 56 ) (16,574 subjects) reported the mean level of serum albumin ALB. The mean level of albumin was lower in the severe COVID-19 group than in the non-severe group (WMD = −4.52 g/L, 95% CI: −6.28 to −2.75), with high heterogeneity ( I 2 = 99.9%, P Heterogenity = 0.000) ( Figure 3E , Supplementary Table 3 ).…”

“…Thirteen studies ( 1 , 5 , 18 , 19 , 26 , 29 , 32 , 37 , 42 , 48 , 51 – 54 ) (9,739 subjects) reported the mean level of PT. The mean level of PT was significantly higher in severe cases (WMD = 0.84 s, 95% CI: 0.46–1.23), with high heterogeneity ( I 2 = 99.4%, P Heterogenity = 0.000) ( Figure 3F , Supplementary Table 3 ).…”

IL-6, IL-10, sFas, granulysin and indicators of intestinal permeability as early biomarkers for a fatal outcome in COVID-19

“…The severe and critical COVID-19 patients included in this study had increased levels of IL-6, IL-10, sFas, granulysin and IP-10, and non-surviving patients had increased levels of IL-6, IL-8, IL-10, IFN-β, sFas and IP-10. IL-6 has been identified as a biomarker for disease progression and fatality in COVID-19 ( Santa Cruz, et al, 2021 ), while IL-8, IL-10 and IP-10 have been evaluated as predictors of disease severity ( Elemam et al, 2022 , Guo et al, 2021 ). IP-10 (CXCL10) limits endothelial repair ( Lupieri, et al, 2020 ), which may promote thrombosis in critical and severe COVID-19 patients ( Chen, et al, 2020 ).…”