Comparison of Clinical Features, Complete Blood Count Parameters, and Outcomes between Two Distinct Waves of COVID-19: A Monocentric Report from Italy

Abstract

Background: Since the beginning of the SARS-CoV-2 pandemic, the ability to predict the trajectory of the disease has represented a major challenge for clinicians. There is recent evidence that complete blood cell count (CBC)-derived inflammation indexes have predictive value in COVID-19. We aimed to describe any changes in the clinical features, CBC-derived ratios, and outcomes of patients admitted to our hospital across two temporally distinct waves. Methods: We retrospectively assessed and compared the clinical characteristics and blood cell count values of patients hospitalized during the second and fourth waves of COVID-19, and explored any outcome differences in terms of the level of respiratory support required and transfer to intensive care. Results: We observed that fourth-wave patients were older, less male-predominant, and carried more comorbidities compared to the second-wave patients but, nevertheless, experienced more favorable outcomes. A strong internal correlation was documented for both waves between outcomes and CBC-derived ratios, with the fourth-wave cases displaying lower admission values of the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). No significant differences were found for lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). Conclusions: We observed that both admission values of CBC-derived indexes and adverse respiratory outcomes decreased from the second to the fourth wave of COVID-19. These data represent a contribution to the existing knowledge on the role of CBC-derived indexes as a potential tool to help clinicians to quickly differentiate in-hospital patients at increased risk of serious illness and death.

Evolution of in-hospital patient characteristics and predictors of death in the COVID-19 pandemic across four waves: are they moving targets with implications for patient care?

“…To provide confirmation of the significant prediction of the study variables as a possible means to validate our cohort data against the results of the current literature we also performed Cox regression analyses limited to univariate models and adjustment of each variable for age (bivariate models), while multivariable analysis was not conducted to prevent misleading interpretation of secondary risk factors alongside the estimate effect of the primary exposure ( 15 ). Age was included in the bivariate models since it consistently emerged as a very important predictor of death ( 2 – 4 , 6 , 7 , 14 ). Associations were expressed using hazard ratios (HRs) with their 95% confidence intervals (CIs).…”

“…Similar data were found elsewhere in Europe ( 11 – 13 ). However, to the best of our knowledge, only a retrospective, single center study conducted in Sardinia (Italy) focused on clinical characteristics and outcomes of patients admitted during the fourth wave, observing that patients admitted during this one were older but experienced a milder disease and had a lower risk of admission to the intensive care units (ICU) rather than patients admitted during the previous waves of pandemic, although no data on the vaccination status of these patients were reported ( 14 ).…”

Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis

“…After initially identifying 52 articles, 36 were removed (either duplicates or irrelevant). After full-text review, a further three were excluded (missing data: two studies; participants aged <18 years: one study), leaving 13 articles, all retrospective studies, for final analysis (Figure 1) [25,[40][41][42][43][44][45][46][47][48][49][50][51]. Clinical endpoints included mortality (11 study groups) [25,40,43,44,[47][48][49][50][51], and the following measures of disease severity: transfer to the intensive care unit (two study groups) [42,46], invasive mechanical ventilation (two study groups) [44,45], prolonged hospital stay (one study group) [41], acute limb ischemia (one study group) [42], deep vein thrombosis (one study group) [47], and acute pulmonary embolism (one study group) [47].…”

“…The AISI was measured on admission in all studies. The risk of bias and the initial certainty of evidence (case-control design; rating 2, ⊕⊕ ) were low in all studies (Supplementary Table S3) [25,[40][41][42][43][44][45][46][47][48][49][50][51].…”

“…Concomitantly, abnormalities in specific blood cell types, particularly, neutrophilia, lymphopenia, and thrombocytopenia, have led to studies reporting significant associations between derived indices, particularly the neutrophil-to-lymphocyte ratio (NLR), and severe disease and mortality [15][16][17][18]. The potential clinical use of another haematological index, the aggregate index of systemic inflammation (AISI, calculated by multiplying the neutrophil, monocyte, and platelet count, and dividing the product by the lymphocyte count), originally developed by Paliogiannis et al in surgical patients [19], has been increasingly investigated in other disease states as well as COVID-19 [20][21][22][23][24][25].…”