Detecting the Hidden Properties of Immunological Data and Predicting the Mortality Risks of Infectious Syndromes

Abstract

BackgroundTo extract more information, the properties of infectious disease data, including hidden relationships, could be considered. Here, blood leukocyte data were explored to elucidate whether hidden information, if uncovered, could forecast mortality.MethodsThree sets of individuals (n = 132) were investigated, from whom blood leukocyte profiles and microbial tests were conducted (i) cross-sectional analyses performed at admission (before bacteriological tests were completed) from two groups of hospital patients, randomly selected at different time periods, who met septic criteria [confirmed infection and at least three systemic inflammatory response syndrome (SIRS) criteria] but lacked chronic conditions (study I, n = 36; and study II, n = 69); (ii) a similar group, tested over 3 days (n = 7); and (iii) non-infected, SIRS-negative individuals, tested once (n = 20). The data were analyzed by (i) a method that creates complex data combinations, which, based on graphic patterns, partitions the data into subsets and (ii) an approach that does not partition the data. Admission data from SIRS+/infection+ patients were related to 30-day, in-hospital mortality.ResultsThe non-partitioning approach was not informative: in both study I and study II, the leukocyte data intervals of non-survivors and survivors overlapped. In contrast, the combinatorial method distinguished two subsets that, later, showed twofold (or larger) differences in mortality. While the two subsets did not differ in gender, age, microbial species, or antimicrobial resistance, they revealed different immune profiles. Non-infected, SIRS-negative individuals did not express the high-mortality profile. Longitudinal data from septic patients displayed the pattern associated with the highest mortality within the first 24 h post-admission. Suggesting inflammation coexisted with immunosuppression, one high-mortality sub-subset displayed high neutrophil/lymphocyte ratio values and low lymphocyte percents. A second high-mortality subset showed monocyte-mediated deficiencies. Numerous within- and between-subset comparisons revealed statistically significantly different immune profiles.ConclusionWhile the analysis of non-partitioned data can result in information loss, complex (combinatorial) data structures can uncover hidden patterns, which guide data partitioning into subsets that differ in mortality rates and immune profiles. Such information can facilitate diagnostics, monitoring of disease dynamics, and evaluation of subset-specific, patient-specific therapies.

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

“…The similar ranges of neutrophil data found in survivors and non-survivors (Figures 5A,B), together with the mononuclear cell-related data differences displayed across outcomes are compatible with a monocyte-mediated increased endothelial permeability previously reported in hantavirus-infected patients (42, 43). As expected, more information was extracted from structured than non-structured data (9, 44–47). A deterministic process was suggested by the fact that eight data structures were enough to prognosticate all but one of the infected survivors.…”

“…Recognition of immunological patterns occurs when indicators are designed to show some features, such as those of ‘anchors’ and/or ‘amplifiers’ (11). Pattern recognition is further fostered when large numbers of data combinations are derived from the primary (directly measurable) data (45, 47). Noise reduction is one special case of pattern recognition, in which one data point-wide lines of observations are created (44).…”

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

“…They also distinguish subsets of septic patients that differ in mortality rates and immunological profiles (44). …”

“…Errors also happen due to inadequate procedures —such as those commonly used with “ compositional ” data (e.g., leukocyte percentages). Because the same ratio value may be found in different biological conditions, simple leukocyte ratios induce ambiguity ( 42 – 44 ). Errors are also generated by dichotomization : when a cutoff divides continuous data (e.g., leukocyte percentages) into two subsets and discontinuous labels—e.g., “infection-negative” and “-positive”—are assigned to each subset, false-positive and -negative errors invariably occur ( 45 ).…”

“…Infectious disease-related data reveal, at least, four properties: (i) circularity , (ii) heterogeneous temporal scales , (iii) ambiguity , and (iv) hidden structures ( 41 , 43 , 44 ). Understanding their features or consequences may prevent errors and information loss.…”