Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes

Abstract

Background: While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated.

Materials and methods: Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested.

Results: While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly.

Discussion: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Keywords: COVID-19; immune-competence; infectious disease.

Figure 1

Non-structured data analysis. Total leukocyte counts (WBC) and relative percentages of lymphocytes, neutrophils and monocytes were assessed considering only the data available at hospitalization day 1 (A,B) and all temporal observations (C,D). In all comparisons, non-survivors and survivors displayed overlapping data distributions, which prevented their differentiation (rectangles, (A–D)).

Figure 2

Day-specific, non-structured data analysis. The blood concentrations of IL-6 and hemoglobin (Hb) (A,B) as well as the values of a complex indicator that captured numerous relationships among leukocytes (C) were determined at each post-hospitalization day for non-survivors and survivors. The overlapping data distributions of IL-6 and Hb prevented the differentiation of outcomes (A,B). However, for the non-cellular, non-structured (IL-6, Hb) and leukocyte-related (AAT) structured variables in the first 3 days, survivors displayed a range of values for the complex indicator not including non-survivors (rectangle, (C)).

Figure 3

Day 1-specific assessments of kurtosis. Histograms of variables collected on the first hospitalization day did not reveal kurtosis (values equal to or less than 0.13) when, regardless of outcome, non-structured, leukocyte-related data were investigated (A,B). While non-survivors did not show kurtosis when other variables were investigated (values equal to or less than 0.29, (C,E,G)), the same variables displayed L-shaped patterns when survivors were evaluated (values equal to or higher than 16.89, (D,F,H)).

Figure 4

Longitudinal assessments of kurtosis—I. When all longitudinal observations were assessed together (n = 291), no kurtosis was observed (all estimates were equal to or less than 1.22) when, regardless of outcome, the percentages of lymphocytes, monocytes or neutrophils were separately investigated (A–F).

Figure 5

Longitudinal assessments of kurtosis—II. When non-cellular indicators as well as leukocyte-related complex indicators were investigated and all longitudinal observations were considered (n = 291), survivors showed kurtosis estimates ranging from 13.41 to 253.78. In contrast, non-survivors displayed smaller kurtosis estimates (A–F).

Figure 6

Longitudinal assessments of kurtosis—III (dynamics of paired observations, separate analyses). When the net difference between day 2 and day 1 values of 92 pairs of observations was considered, non-survivors did not show evidence of kurtosis (values ranging between −0.44 and 8.37), while survivors exhibited kurtosis (values between 18.37 and 80.99, (A–F)).

Figure 7

Longitudinal assessments of kurtosis—IV (dynamics of paired observations, integrated analysis). Survivors displayed a larger dispersion of values than non-survivors when the difference between the day 2 and day 1 data values of survivors and non-survivors was simultaneously investigated in a single plot (92 paired observations). Both non-cellular indicators (such as IL-6, D-dimer and PCT, (A–C)) and complex (structured) indicators derived from leukocyte data (AAT, BBF, BBT, (D–F)) revealed that non-survivors did not vary much in values over time (the difference of day 2 minus day 1 approached zero). In contrast, all other variables (except PCT) revealed broad oscillations over time when survivors were considered, which expressed both above- and below-zero values. This assessment may be interpreted as a single and perpendicular perspective of the data reported separately for survivors and non-survivors in Figure 6.

Figure 8

Detection of simple, bi-dimensional relationships associated with survival. While on days 1 and 2, survivors exhibited a broad data interval that facilitated relationships between non-structured variables (neutrophils and monocytes), non-survivors lacked such a data range (rectangles, (A–D)). A similar absence was observed when lymphocytes and monocytes were assessed (rectangles, (E–H)).

Figure 9

Detection of complex, bi-dimensional relationships associated with survival. When structured, leukocyte-related variables were investigated at days 1 and 2 in survivors and non-survivors, survivors exhibited two data ranges that were lacking when non-survivors were evaluated (A–D). Because the analysis of non-structured data did not reveal two separate data ranges associated with survival (shown in Figure 8), the analysis of structured (complex) indicators extracted more information from the same data.

Figure 10

Influence of time on the complexity of bi-dimensional assessments associated with survival. A bi-dimensional, outcome-related, immunological and temporal assessment of structured data is not influenced by time: each of the two data ranges occupied only by survivors included both day 1 and day 2 observations (A–C). The two subsets of survivor-only observations (here named A and B) were biologically valid and distinguishable: subsets A and B differed from one another both in terms of non-structured data (non-overlapping percentages of lymphocytes, neutrophils and monocytes) and in several ratios, including the small leukocyte (lymphocyte and neutrophil)/monocyte (SL/M), neutrophil/monocyte (N/M) and neutrophil/lymphocyte (N/L) ratios (D,E).

Figure 11

Three-dimensional, long-term, biologically validated detection of structured data ranges associated with survival. More information was retrieved from the same data when three-dimensional relationships were investigated with data collected on all testing days (n = 291). Three non-randomly distributed data subsets—perpendicular to one another—were populated by survivors (A). Data oscillations (values far from zero) indicated protective responses if they were observed in the first three hospitalization days (B). The combination of spatial and outcome (quantitative and qualitative) data identified three data subsets (named I–III) composed of only survivors (C). All three (I–III) data subsets were validated: they differed from one another by non-overlapping intervals of neutrophil or monocyte percentages or monocyte/lymphocyte ratios (D). Therefore, immuno-competence was an early response that involved, at least, three complex immunological functions that could involve all cell types.

Figure 12

Three-dimensional, two-day long, biologically validated detection of structured data ranges associated with survival. Structured data collected on days 1 and 2 (n = 195) displayed several 3D patterns that facilitated numerous inferences. For example, every data point detected within a data subset perpendicular to the remaining data prognosticated survival (rectangle, (A)). Personalized prognoses were supported through redundant analyses: the predictions generated by plot (A) were corroborated by the three data subsets distinguished in plot (B). The three data subsets identified in (B) were biologically valid: each subset associated with survival (‘left’, ‘top’ and ‘right’) differed from one another by two or more variables (C). Because this differentiation and validation corroborated the findings reported in Figure 11, it is concluded that the assessment of either day 1 only or days 1 and 2 conveyed similar information. Other structured indicators showed additional potential targets of immunomodulatory therapies and facilitated earlier evaluations of treatments. For example, the temporal data directionality described by a single (one data point-wide) line of observations showed wo desirable trajectories (temporal data movements that followed the arrows, (D)). Because this assessment (which was based on data collected over 48 h) corroborated the findings reported both in the analysis of all temporal data and the one that considered only day 1 data, it is concluded than the analysis of complex (structured) leukocyte-related, alone or together with the analysis of other non-cellular hematological variables, may assess immuno-competence, facilitate personalized prognosis and (when temporal data directionality is considered) evaluate therapies earlier.