Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China

Abstract

Background: Crucial roles of hematologic and immunologic responses in progression of coronavirus disease 2019 (COVID-19) remain largely unclear.

Objective: We sought to address the dynamic changes in hematologic and immunologic biomarkers and their associations with severity and outcomes of COVID-19.

Methods: A retrospective study including 548 patients with COVID-19 with clarified outcome (discharged or deceased) from a national cohort in China was performed. Cross-sectional and longitudinal variations were compared and the associations with different severity and outcomes were analyzed.

Results: On admission, the counts of lymphocytes, T-cell subsets, eosinophils, and platelets decreased markedly, especially in severe/critical and fatal patients. Increased neutrophil count and neutrophils-to-lymphocytes ratio were predominant in severe/critical cases or nonsurvivors. During hospitalization, eosinophils, lymphocytes, and platelets showed an increasing trend in survivors, but maintained lower levels or dropped significantly afterwards in nonsurvivors. Nonsurvivors kept a high level or showed an upward trend for neutrophils, IL-6, procalcitonin, D-dimer, amyloid A protein, and C-reactive protein, which were kept stable or showed a downward trend in survivors. Positive correlation between CD8⁺ T-cell and lymphocytes count was found in survivors but not in nonsurvivors. A multivariate Cox regression model suggested that restored levels of lymphocytes, eosinophils, and platelets could serve as predictors for recovery, whereas progressive increases in neutrophils, basophils, and IL-6 were associated with fatal outcome.

Conclusions: Hematologic and immunologic impairment showed a significantly different profile between survivors and nonsurvivors in patients with COVID-19 with different severity. The longitudinal variations in these biomarkers could serve to predict recovery or fatal outcome.

Keywords: COVID-19; Hematologic indices; immunologic responses; outcome; severity.

Graphical abstract

Fig 1

Dynamic changes in hematologic and immunologic biomarkers in patients with COVID-19 across 3 time periods, including on admission (OA), midhospitalization (MH), and end-hospitalization (EH). Medium value of 3 time periods is shown in patients with mild, severe, or critical severity on admission for both survivors and nonsurvivors. The significant difference between survivors and nonsurvivors in each of 3 severity groups was compared using Kruskal-Wallis test and indicated as †P ≤ .05 and ‡P ≤ .01.

Fig 2

Correlation networks for immunologic cells and biomarkers. Networks showed different profiles of correlations in COVID-19 nonsurvivors (A and C) and survivors (B and D), on admission (Fig 2, A and B) and end hospitalization (Fig 2, C and D). The width of the edge showing stronger or weaker interactions is proportional to the absolute value of biomarker-biomarker correlation (|r|). Edges were shown only when |r| > 0.2. A purple edge indicates a positive correlation, and a blue edge indicates a negative correlation. PCT, Procalcitonin.

Fig 3

PCA biplot of biomarkers on admission (A) or at end hospitalization (B). Samples are shown as dots and colored by outcomes (survivors and nonsurvivors). Biomarkers shown as lines with arrows. The configuration of biomarkers on biplot represented the relationship between variables and principal components. PC, Principal component; PCA, Principal-component analysis; PCT, procalcitonin.

Fig 4

Kaplan-Meier survival plots for different prognostic factors. Kaplan-Meier survival plots according to (A) eosinophil OA, (B) platelets OA, (C) Δ neutrophils, (D) Δ lymphocytes, (E) Δ eosinophils, (F) Δ basophils, (G) Δ platelets, and (H) Δ IL-6. Δ index(a) = index(a) end hospitalization − index(a) OA. OA, On admission.

Fig 5

Risk factors of fatal outcome in the multivariate Cox proportional hazards regression model. Shown in the figure are the HR and the 95% CI associated with the end point. Δ index(a) = index(a) end hospitalization − index(a) OA. OA, On admission.