235 research outputs found

    Time to antibiotic therapy and outcome in bacterial meningitis:a Danish population-based cohort study

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    BACKGROUND: Community-acquired bacterial meningitis (CABM) is a life-threatening disease and timing of antibiotic therapy remains crucial. We aimed to analyse the impact of antibiotic timing on the outcome of CABM in a contemporary cohort. METHODS: We conducted a population-based cohort study based on chart reviews of all adult cases (>16 years of age) of CABM in North Denmark from 1998 to 2014 excluding patients given pre-hospital parenteral antibiotics. We used modified Poisson regression analyses to compute the adjusted risk ratio (adj. RR) with 95 % confidence intervals (CIs) for in-hospital mortality and unfavourable outcome at discharge by time after arrival to hospital to adequate antibiotic therapy. RESULTS: We identified 195 adults with CABM of whom 173 patients were eligible for further analyses. The median door-to-antibiotic time was 2.0 h (interquartile range (IQR) 1.0–5.5). We observed increased adjusted risk ratios for in-hospital mortality of 1.6 (95 % CI 0.8–3.2) and an unfavourable outcome at discharge of 1.5 (95 % CI 1.0–2.2, p = 0.03) when treatment delays exceeded 6 h versus treatment within 2 h of admission. These findings corresponded to adjusted risk ratios of in-hospital mortality of 1.1 per hour of delay (95 % CI 0.8–1.5) and an unfavourable outcome at discharge of 1.1 per hour of delay (95 % CI 1.0–1.3) within the first 6 h of admission. Some patients (31 %) were diagnosed after admission and had more delays in antibiotic therapy and correspondingly increased in-hospital mortality (30 vs 14 %, p = 0.01) and unfavourable outcome (62 vs 37 %, p = 0.002). CONCLUSIONS: Delay in antibiotic therapy was associated with unfavourable outcome at discharge. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1711-z) contains supplementary material, which is available to authorized users

    Monitoring Poisson time series using multi-process models

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