9 research outputs found

    Global prevalence of antibiotic resistance in paediatric urinary tract infections caused by <i>Escherichia coli</i> and association with routine use of antibiotics in primary care:a systematic review and meta-analysis

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    Objectives To systematically review studies investigating the prevalence of antibiotic resistance in urinary tract infections caused by Escherichia coli in children and, when appropriate, to meta-analyse the relation between previous antibiotics prescribed in primary care and resistance. Design and data analysis Systematic review and meta-analysis. Pooled percentage prevalence of resistance to the most commonly used antibiotics in children in primary care, stratified by the OECD (Organisation for Economic Co-operation and Development) status of the study country. Random effects meta-analysis was used to quantify the association between previous exposure to antibiotics in primary care and resistance. Data sources Observational and experimental studies identified through Medline, Embase, Cochrane, and ISI Web of Knowledge databases, searched for articles published up to October 2015. Eligibility criteria for selecting studies Studies were eligible if they investigated and reported resistance in community acquired urinary tract infection in children and young people aged 0-17. Electronic searches with MeSH terms and text words identified 3115 papers. Two independent reviewers assessed study quality and performed data extraction. Results 58 observational studies investigated 77 783 E coli isolates in urine. In studies from OECD countries, the pooled prevalence of resistance was 53.4% (95% confidence interval 46.0% to 60.8%) for ampicillin, 23.6% (13.9% to 32.3%) for trimethoprim, 8.2% (7.9% to 9.6%) for co-amoxiclav, and 2.1% (0.8 to 4.4%) for ciprofloxacin; nitrofurantoin was the lowest at 1.3% (0.8% to 1.7%). Resistance in studies in countries outside the OECD was significantly higher: 79.8% (73.0% to 87.7%) for ampicillin, 60.3% (40.9% to 79.0%) for co-amoxiclav, 26.8% (11.1% to 43.0%) for ciprofloxacin, and 17.0% (9.8% to 24.2%) for nitrofurantoin. There was evidence that bacterial isolates from the urinary tract from individual children who had received previous prescriptions for antibiotics in primary care were more likely to be resistant to antibiotics, and this increased risk could persist for up to six months (odds ratio 13.23, 95% confidence interval 7.84 to 22.31). Conclusions Prevalence of resistance to commonly prescribed antibiotics in primary care in children with urinary tract infections caused by E coli is high, particularly in countries outside the OECD, where one possible explanation is the availability of antibiotics over the counter. This could render some antibiotics ineffective as first line treatments for urinary tract infection. Routine use of antibiotics in primary care contributes to antimicrobial resistance in children, which can persist for up to six months after treatment

    Validating a prediction tool to determine the risk of nosocomial multidrug-resistant Gram-negative bacilli infection in critically ill patients: A retrospective case–control study

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    BACKGROUND:The Singapore GSDCS score was developed to enable clinicians predict the risk of nosocomial multidrug-resistant Gram-negative bacilli (RGNB) infection in critically ill patients. We aimed to validate this score in a UK setting. METHOD:A retrospective case-control study was conducted including patients who stayed for more than 24h in intensive care units (ICUs) across two tertiary National Health Service hospitals in London, UK (April 2011-April 2016). Cases with RGNB and controls with sensitive Gram-negative bacilli (SGNB) infection were identified. RESULTS:The derived GSDCS score was calculated from when there was a step change in antimicrobial therapy in response to clinical suspicion of infection as follows: prior Gram-negative organism, Surgery, Dialysis with end-stage renal disease, prior Carbapenem use and intensive care Stay of more than 5 days. A total of 110 patients with RGNB infection (cases) were matched 1:1 to 110 geotemporally chosen patients with SGNB infection (controls). The discriminatory ability of the prediction tool by receiver operating characteristic curve analysis in our validation cohort was 0.75 (95% confidence interval 0.65-0.81), which is comparable with the area under the curve of the derivation cohort (0.77). The GSDCS score differentiated between low- (0-1.3), medium- (1.4-2.3) and high-risk (2.4-4.3) patients for RGNB infection (P<0.001) in a UK setting. CONCLUSION:A simple bedside clinical prediction tool may be used to identify and differentiate patients at low, medium and high risk of RGNB infection prior to initiation of prompt empirical antimicrobial therapy in the intensive care setting

    How big should the pilot study for my cluster randomised trial be?

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    The author(s) received no financial support for the research, authorship, and/or publication of this article

    Global health systems' data science approach for precision diagnosis of sepsis in early life

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    Neonates and children in low-income and middle-income countries (LMICs) contribute to the highest number of sepsis-associated deaths globally. Interventions to prevent sepsis mortality are hampered by a lack of comprehensive epidemiological data and pathophysiological understanding of biological pathways. In this review, we discuss the challenges faced by LMICs in diagnosing sepsis in these age groups. We highlight a role for multi-omics and health care data to improve diagnostic accuracy of clinical algorithms, arguing that health-care systems urgently need precision medicine to avoid the pitfalls of missed diagnoses, misdiagnoses, and overdiagnoses, and associated antimicrobial resistance. We discuss ethical, regulatory, and systemic barriers related to the collection and use of big data in LMICs. Technologies such as cloud computing, artificial intelligence, and medical tricorders might help, but they require collaboration with local communities. Co-partnering (joint equal development of technology between producer and end-users) could facilitate integration of these technologies as part of future care-delivery systems, offering a chance to transform the global management and prevention of sepsis for neonates and children
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