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

BACKGROUND

To 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.

METHODS

Three 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.

RESULTS

The 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.

CONCLUSION

While 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.

Epidemic protection zones: centred on cases or based on connectivity?

When an exotic infectious disease invades a susceptible environment, protection zones are enforced. Historically, such zones have been shaped as circles of equal radius (ER), centred on the location of infected premises. Because the ER policy seems to assume that epidemic dissemination is driven by a similar number of secondary cases generated per primary case, it does not consider whether local features, such as connectivity, influence epidemic dispersal. Here we explored the efficacy of ER protection zones. By generating a geographically explicit scenario that mimicked an actual epidemic, we created protection zones of different geometry, comparing the cost-benefit estimates of ER protection zones to a set of alternatives, which considered a pre-existing connecting network (CN) - the road network. The hypothesis of similar number of cases per ER circle was not substantiated: the number of units at risk per circle differed up to four times among ER circles. Findings also showed that even a small area (of <115 km(2) ) revealed network properties. Because the CN policy required 20% less area to be protected than the ER policy, and the CN-based protection zone included a 23.8% greater density of units at risk/km(2) than the ER-based alternative, findings supported the view that protection zones are likely to be less costly and more effective if they consider connecting structures, such as road, railroad and/or river networks. The analysis of local geographical factors (contacts, vectors and connectivity) may optimize the efficacy of control measures against epidemics.

Critical response time (time available to implement effective measures for epidemic control): model building and evaluation

The time available to implement successful control measures against epidemics was estimated. Critical response time (CRT), defined as the time interval within which the number of epidemic cases remains stationary (so that interventions implemented within CRT may be the most effective or least costly), was assessed during the early epidemic phase, when the number of cases grows linearly over time. The CRT was calculated from data of the 2001 foot-and-mouth disease (FMD) epidemic that occurred in Uruguay. Significant regional CRT differences (ranging from 1.4 to 2.7 days) were observed. The CRT may facilitate selection of control measures. For instance, a CRT equal to 3 days would support the selection of measures, such as stamping-out, implementable within 3 days, but rule out measures, such as post-outbreak vaccination, because intervention and immunity building require more than 3 days. Its use in rapidly disseminating diseases, such as FMD, may result in regionalized decision-making.