Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Abstract

BackgroundDiagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases.MethodsLeukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version.ResultsIn all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed.ConclusionsInfectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.

Personalized and dynamic antibiograms-an exploration in seven infectious syndromes

“…42,43 While trajectory has been investigated in infections, earlier studies did not assess antibiotics. 40,44 Data collected from patients affected by seven infectious syndromes were explored.…”

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

“…Synergy, pleiotropy, complexity, and dynamics can express ambiguity : the same numerical value of the same variable does not always have the same meaning nor always performs the same function. Vice versa, different values of the same variable may be associated with the same function or meaning (8). For example, interleukin (IL) 6 is ambiguous.…”

“…Thus, each value of each variable may, at least, have two meanings or perform two functions which are associated with either positive (feed forward) or negative feedback responses. For instance, similar values of mononuclear cells (MC or lymphocytes and monocytes) can predict both high and low MC/neutrophil (MC/N) ratios (8). The apparent ambiguity of the MC/N ratio may not be so but a valuable new piece of information: in the example mentioned above, septic patients that, after showing similar immunological values, exhibited opposite profiles, were infected by different bacteria, i.e., what seemed to be ambiguous could be distinguished (8).…”

“…For instance, similar values of mononuclear cells (MC or lymphocytes and monocytes) can predict both high and low MC/neutrophil (MC/N) ratios (8). The apparent ambiguity of the MC/N ratio may not be so but a valuable new piece of information: in the example mentioned above, septic patients that, after showing similar immunological values, exhibited opposite profiles, were infected by different bacteria, i.e., what seemed to be ambiguous could be distinguished (8). While circularity is observed when temporal data are plotted (11), tests conducted at a single time point may also display circularity or other non-linear patterns.…”

“…Randomization, alone, does not prevent ambiguity (11). However, 3D/4D temporal data directionality (arrows that denote where the data are coming from) may prevent ambiguity (8).…”