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1.
Crit Care ; 27(1): 417, 2023 10 31.
Article in English | MEDLINE | ID: mdl-37907989

ABSTRACT

BACKGROUND: Sepsis is one of the leading causes of death. Treatment attempts targeting the immune response regularly fail in clinical trials. As HCMV latency can modulate the immune response and changes the immune cell composition, we hypothesized that HCMV serostatus affects mortality in sepsis patients. METHODS: We determined the HCMV serostatus (i.e., latency) of 410 prospectively enrolled patients of the multicenter SepsisDataNet.NRW study. Patients were recruited according to the SEPSIS-3 criteria and clinical data were recorded in an observational approach. We quantified 13 cytokines at Days 1, 4, and 8 after enrollment. Proteomics data were analyzed from the plasma samples of 171 patients. RESULTS: The 30-day mortality was higher in HCMV-seropositive patients than in seronegative sepsis patients (38% vs. 25%, respectively; p = 0.008; HR, 1.656; 95% CI 1.135-2.417). This effect was observed independent of age (p = 0.010; HR, 1.673; 95% CI 1.131-2.477). The predictive value on the outcome of the increased concentrations of IL-6 was present only in the seropositive cohort (30-day mortality, 63% vs. 24%; HR 3.250; 95% CI 2.075-5.090; p < 0.001) with no significant differences in serum concentrations of IL-6 between the two groups. Procalcitonin and IL-10 exhibited the same behavior and were predictive of the outcome only in HCMV-seropositive patients. CONCLUSION: We suggest that the predictive value of inflammation-associated biomarkers should be re-evaluated with regard to the HCMV serostatus. Targeting HCMV latency might open a new approach to selecting suitable patients for individualized treatment in sepsis.


Subject(s)
Cytomegalovirus Infections , Sepsis , Humans , Cytomegalovirus , Cytomegalovirus Infections/complications , Immunity , Interleukin-6 , Sepsis/complications
2.
Clin Microbiol Infect ; 26(9): 1255.e1-1255.e8, 2020 Sep.
Article in English | MEDLINE | ID: mdl-32088331

ABSTRACT

OBJECTIVES: Pharmacokinetic-pharmacodynamic (PK-PD) considerations are at the heart of defining susceptibility breakpoints for antibiotic therapy. However, current approaches follow a fragmented workflow. The aim of this study was to develop an integrative pharmacometric approach to define MIC-based breakpoints for killing and suppression of resistance development for plasma and tissue sites, integrating clinical microdialysis data as well as in vitro time-kill curves and heteroresistance information, exemplified by moxifloxacin against Staphylococcus aureus and Escherichia coli. METHODS: Plasma and target site samples were collected from ten patients receiving 400 mg moxifloxacin/day. In vitro time-kill studies with three S. aureus and two E. coli strains were performed and resistant subpopulations were quantified. Using these data, a hybrid physiologically based (PB) PK model and a PK-PD model were developed, and utilized to predict site-specific breakpoints. RESULTS: For both bacterial species, the predicted MIC breakpoint for stasis at 400 mg/day was 0.25 mg/L. Less reliable killing was predicted for E. coli in subcutaneous tissues where the breakpoint was 0.125 mg/L. The breakpoint for resistance suppression was 0.06 mg/L. Notably, amplification of resistant subpopulations was highest at the clinical breakpoint of 0.25 mg/L. High-dose moxifloxacin (800 mg/day) increased all breakpoints by one MIC tier. CONCLUSIONS: An efficient pharmacometric approach to define susceptibility breakpoints was developed; this has the potential to streamline the process of breakpoint determination. Thereby, the approach provided additional insight into target site PK-PD and resistance development for moxifloxacin. Application of the approach to further drugs is warranted.


Subject(s)
Anti-Bacterial Agents/pharmacology , Escherichia coli/drug effects , Moxifloxacin/pharmacology , Staphylococcus aureus/drug effects , Anti-Bacterial Agents/metabolism , Bacteriological Techniques , Drug Resistance, Bacterial , Humans , Microbial Sensitivity Tests , Models, Biological , Moxifloxacin/metabolism
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