Infections due to carbapenem-resistant Klebsiella pneumoniae (CR-KP) have emerged as a public health problem worldwide given their spread dynamics and the limited therapeutic options. Our aim was to study the clinical outcome of patients with CR-KP infections in relation to antimicrobial treatment. CR-KP infections that occurred in a 10-month period (September 2009 to June 2010) in patients admitted to 19 intensive care units all over Greece were studied. A total of 127 CR-KP infections were reported. Central venous catheter bacteraemia was the most frequent infection, followed by ventilator-associated pneumonia (39 (30.7%) and 35 (27.6%) cases, respectively). Resistance to colistin, tigecycline, gentamicin and amikacin was detected in 20%, 33%, 21% and 64% of isolates, respectively. Regarding treatment, 107 cases received active treatment, including 1 or ≥2 active antibiotics in 65 (60.7%) and 42 (39.3%) cases, respectively. The most frequent combination was colistin plus aminoglycoside and tigecycline plus aminoglycoside (17 and 11 cases, respectively). Forty-eight (45.2%) of the cases that received active treatment were considered clinical failures, with 23.5% mortality at 14 days. Logistic regression analysis revealed that age ≤55 years, non-immunocompromised patients and patients who received colistin had higher successful response rates, while patients ≤55 years old had lower mortality rates at 14 days after the introduction of active treatment. CR-KP infections are associated with a significant clinical failure rate. Colistin remains a valuable antimicrobial agent for treating these infections, while the rise of resistance to the last available antibiotics further limits treatment options.

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Despite strict glycemic control, diabetic patients have a 1.7-fold probability of developing an ICU-acquired BSI compared to nondiabetic subjects.

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.

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.