Hemogram Derived Clinical Traits Evinced Differential Contribution to Explained Variance of Mortality Risk Model at Index Hospitalization for Sars-Cov-2 Infection across Wuhan/D614G, Delta, Omicron and Omicron BA.x Variants

• Published in: Blood Vol. 142; no. Supplement 1; p. 5348
• Main Authors: Garcia, Gustavo, Hameed, Maha, Kayali, Zeina, Hamad, Karen, Wiese-Rometsch, Wilhelmine, Idogun, Precious, Patel, Anjan J.
• Format: Journal Article
• Language: English
• Published: 02.11.2023
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2023-178814

Introduction SARS-CoV-2 infection promulgates an immune dysregulation that drives an inflammatory cascade therefore inducingmorbidity and mortality across the COVID-19 presentation spectrum. Hemogram (CBC) derived traits associated with COVID-19 severity have been reported (Meizlish ML et al., 2021, Dhinata KS et al., 2021), but their relative impact across SARS-CoV-2 variants is unclear. We employed sequential machine learning approachesto identify significantly contributing traits with attendant portion of explained variance (EV%) in hospital mortality risk models across predominant SARS-CoV-2 variants. Methods Our retrospective design, analyses and interpretations followed constructs detailed in the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline. Adult patients with laboratory confirmed COVID-19 who underwent index hospitalization during Wuhan/D614G (March 14, 2020- June 18, 2021), Delta (June 19, 2021- December 18, 2021), Omicron (December 19-March 30, 2022) or Omicron BA.x (March 31, 2022 - April 14, 2023) variant waves at an 820-bed academic public health trust hospital in Florida were studied. Demographics, laboratory results (within 24h of presentation), ICD-10-CM-based comorbidity, COVID-19 directed treatment and administrative data were extracted from electronic medical records under IRB exemption. Generalized regression with adaptive LASSO identified hemogram-derived traits significantly associated with mortality in at least one of the variants while controlling for presentation vital signs, age, sex, extant comorbidities and ultimate COVID-19 directed treatment. Boosted Tree modeling computed within-variant proportion contributed by each trait to model's accuracy (R 2) representing EV% of mortality risk. Traits contributing to one or more variants for at least 1% portion of EV were retained in model. A Bonferroni corrected two-tailed p < .0125 was considered significant. Results We included 6490 consecutively discharge patients distributed across Wuhan/D614G (n=2249), Delta (n=1196), Omicron (n=953) and Omicron BA.x (n=2092) variants with respective mortality of 9.3%, 13.0%, 4.4%, and 2.4% (p< .0001). Patient characteristics and on-presentation laboratory medicine clinical traits are in Table 1. Table 2 provides ranked contribution for each of the 7 traits retained in the model with attendant presentation mortality risk accuracy (R 2) across variant waves. Potent synergy can be generated with biomarkers ratios, as seen with Segmented neutrophil/monocyte ratio (SMR) and lymphocyte/monocyte ratio (LMR), compared to studying these variables individually. Conclusion Sequential machine-learning approaches identified differential expression intensity across SARS-CoV-2 variants in presentation hemogram-derived traits associated with hospital mortality risk modeling at index infection. These findings suggest that COVID-19 presentation risk and level of care assessment tools employing hemogram-derived traits may not generalize across pandemic variants. Platelets counts were associated with mortality in Delta, Wuhan/D614G and Omicron variants. Although it was also associated in Omicron Bx.5, the R 2 is lower, this can be explained because in this study the variant of Omicron Bx.5 had the lowest mortality rate. Studies have shown that platelet activation generates a cascade with more thromboxane A2 and platelet thrombofactor A which increases prothrombotic states (Dhinata KS et al., 2021). The absence of eosinophils was seen in Wuhan/D614G and Delta, the cause of which cannot be ascertained. A theory is that the inflammation caused by SARS-CoV-2 infection releases certain cytokines that induce eosinophil apoptosis (Dhinata KS et al., 2021). Immature neutrophils evidenced across variants can be explained by the inflammatory cascade inducing G-CSF and IL-8 production, which induces neutrophil activation (Meizlish ML et al., 2021).Lymphopenia seen throughout variants contributes to an increase in LMR. One presumption is that the SARS-CoV-2 affects T-Lymphocytes through ACE-2 receptor and CD117 spike proteins (Meizlish ML et al., 2021). Overall, our study brings to attention how hemogram-derived traits express differently across SARS-CoV-2 variants, which could guide the stratification of COVID-19 severity and assist in the care of patients before decompensation is noticed.