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Objectives To explore the association between MIC/EUCAST breakpoint ratio and 28 day mortality in patients with a Gram-negative bloodstream infection (BSI). Methods Using data from the Bloodstream Infection—Focus on Outcomes (BSI-FOO) observational study, we defined an average MIC/EUCAST breakpoint ratio that was updated daily to reflect changes in treatment in the first 7 days after blood culture. Cox regression analysis was performed to estimate the association between MIC/EUCAST breakpoint ratio and mortality, adjusting for organism and a risk score calculated using potential confounding variables. The primary outcome was 28 day all-cause mortality from the date of blood culture. Results Of the 1903 study participants, 514 met the eligibility criteria and were included in the analysis (n = 357 Escherichia coli, n = 6 Klebsiella and n = 151 Pseudomonas aeruginosa). The average age was 74.0 years (IQR 60.0–82.0). The mortality rate varied from 11.1% (in patients treated with an average MIC/EUCAST breakpoint ratio of 1) to 27.6% (in patients treated with antibiotics with an average MIC/EUCAST breakpoint ratio >1). After adjusting for risk score and organism, MIC/EUCAST breakpoint ratio was not associated with 28 day mortality (P = 0.148). Conclusions In an adjusted model controlling for potential confounding variables, there was no evidence to suggest a relationship between MIC/EUCAST breakpoint ratio and 28 day mortality in patients with a Gram-negative BSI.

BackgroundMyocardial infarction induces elevation of progenitor cells in the circulation, a reparative response inhibited by type-2 diabetes.ObjectivesDetermine if myocardial infarct severity and diabetes interactively influence the migratory activity of CD34+/CXCR4+ progenitor cells and if the migratory test predicts cardiac outcomes.Materials and methodsA longitudinal study was conducted on patients with or without diabetes with a STEMI or NSTEMI. CD34+/CXCR4+ cells were measured in the peripheral blood using flow cytometry, and migratory activity was tested in vitro on cells isolated from samples collected on days 0 and 4 post-infarct. Cardiac function was assessed at three months using cardiac MRI.ResultsOf 1,149 patients screened, 71 (6.3%) were eligible and consented. Fifty had STEMI (16 with diabetes) and 21 NSTEMI (8 with diabetes). The proportion of CD34+/CXCR4+ cells within blood mononuclear cells was 1.96 times higher after STEMI compared with NSTEMI (GMR = 1.96, 95% CI 0.87, 4.37) and 1.55 times higher in patients with diabetes compared to patients without diabetes (GMR = 1.55, 95% CI 0.77, 3.13). In the latter, STEMI was associated with a 2.42-times higher proportion of migrated CD34 + /CXCR4 + cells compared with NSTEMI (GMR = 2.42, 95% CI 0.66, 8.81). In patients with diabetes, the association was the opposite, with a 55% reduction in the proportion of migrated CD34+/CXCR4+ cells. No statistically significant associations were observed between the frequency in peripheral blood or in vitro migration capacity of CD34+/CXCR4+ cells and MRI outcomes.ConclusionWe document the interaction between infarct and diabetes on the migratory activity of CD34+/CXCR4+ cells. The test did not predict functional outcomes in the studied cohort.

Objective To estimate the effect of treatment duration on in-hospital mortality in patients with Staphylococcus aureus blood stream infection and demonstrate the biases that can arise when immortal-time bias is ignored. Exposure We compared three treatment strategies: short therapy (18 days). Main outcome measures Twenty-eight-day all-cause in-hospital mortality. Methods Using data from the BSI-FOO study, we implemented an approach proposed by Hernán to overcome confounding and immortal-time biases. The first stage is to clone all participants, so that each participant is assigned to each treatment strategy. Second, observations are censored when their data becomes inconsistent with their assigned strategy. Finally, inverse-probability weights are applied to adjust for potential selection. We compared our results to a naïve approach where immortal-time bias is ignored. Results Of the 1903 participants in BSI-FOO, 587 were eligible and included in the analysis. After cloning, the weighted estimates of hazard ratio of mortality for short versus long therapy was 1.74 (95% CI 1.36, 2.24) and for intermediate versus long therapy was 1.09 (0.98, 1.22). In the naïve approach, the hazard ratios with reference to the long therapy group are 37.4 (95% CI 18.9 to 74.4) in the short therapy group and 4.1 (95% CI 1.9 to 8.9) in the intermediate therapy group. Conclusions Our findings suggest that duration of therapy >18 days is beneficial with respect to 28-day in-hospital mortality, however, there remains uncertainty around the efficacy of reducing duration of treatment to 10–18 days.

ObjectiveTo illustrate the challenges of estimating the effect of an exposure that is bounded by duration of follow-up on all-cause 28-day mortality, whilst simultaneously addressing missing data and time-varying covariates.Study design and methodsBSI-FOO is a multicentre cohort study with the primary aim of quantifying the effect of modifiable risk factors, including time to initiation of therapy, on all-cause 28-day mortality in patients with bloodstream infection. The primary analysis involved two Cox proportional hazard models, first one for non-modifiable risk factors and second one for modifiable risk factors, with a risk score calculated from the first model included as a covariate in the second model. Modifiable risk factors considered in this study were recorded daily for a maximum of 28 days after infection. Follow-up was split at daily intervals from day 0 to 28 with values of daily collected data updated at each interval (i.e., one row per patient per day).Analytical challengesEstimating the effect of time to initiation of treatment on survival is analytically challenging since only those who survive to time t can wait until time t to start treatment, introducing immortal time bias. Time-varying covariates representing cumulative counts were used for variables bounded by survival time e.g. the cumulative count of days before first receipt of treatment. Multiple imputation using chained equations was used to impute missing data, using conditional imputation to avoid imputing non-applicable data e.g. ward data after discharge.ConclusionUsing time-varying covariates represented by cumulative counts within a one row per day per patient framework can reduce the risk of bias in effect estimates. The approach followed uses established methodology and is easily implemented in standard statistical packages.