anzctr.org.au Identifiers: ACTRN12613000532707 and ACTRN12615000403538 and ClinicalTrials.gov Identifier: NCT02176122.
The Middle East Respiratory Syndrome coronavirus (MERS-CoV) has been a focus of international attention since its identification in 2012. Epidemiologically it is characterized by sporadic community cases, which are amplified by hospital-based outbreaks. Healthcare facilities in 27 countries from most continents have experienced imported cases, with the most significant outbreak involving 186 cases in Korea. The mortality internationally is 36% and guidance for clinical management has yet to be developed. Most facilities and healthcare providers outside of the Middle East receiving patients have no or little experience in the clinical management of MERS. When a case does occur there is likely little time for a critical appraisal of the literature and putative pharmacological options. We identified published literature on the management of both MERS-CoV and the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) through searches of PubMed and WHO and the US CDC websites up to 30 April 2016. A total of 101 publications were retrieved for critical appraisal. Most published literature on therapeutics for MERS are in vitro experiments, animal studies and case reports. Current treatment options for MERS can be categorized as: immunotherapy with virus-specific antibodies in convalescent plasma; polyclonal and monoclonal antibodies produced in vitro or in genetically modified animals; and antiviral agents. The use of any therapeutics in MERS-CoV remains investigational. The therapeutic agents with potential benefits and warranting further investigation include convalescent plasma, interferon-β/ribavirin combination therapy and lopinavir. Corticosteroids, ribavirin monotherapy and mycophenolic acid likely have toxicities that exceed potential benefits.
Background: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance reduces the efficacy of empiric therapy guidelines derived from population data. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collect patient data, it would be possible to obtain individualized predictions for targeted empiric antibiotic choices. Methods and Findings: We analysed blood culture data collected from a 100-bed children's hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information was captured with 35 independent variables. Using these variables, we used a suite of machine learning algorithms to predict Gram stains and whether bacterial pathogens could be treated with common empiric antibiotic regimens: i) ampicillin and gentamicin; ii) ceftriaxone; iii) none of the above. 243 patients with bloodstream infections were available for analysis. We found that the random forest method had the best predictive performance overall as assessed by the area under the receiver operating characteristic curve (AUC). The random forest method gave an AUC of 0.80 (95%CI 0.66-0.94) for predicting susceptibility to ceftriaxone, 0.74 (0.59-0.89) for susceptibility to ampicillin and gentamicin, 0.85 (0.70-1.00) for susceptibility to neither, and 0.71 (0.57-0.86) for Gram stain result. Most important variables for predicting susceptibility were time from admission to blood culture, patient age, hospital versus community-acquired infection, and age-adjusted weight score. Conclusions: Applying machine learning algorithms to patient data that are readily available even in resource-limited hospital settings can provide highly informative predictions on antibiotic susceptibilities to guide appropriate empiric antibiotic therapy. When used as a decision support tool, such approaches have the potential to improve targeting of empiric therapy, patient outcomes and reduce the burden of antimicrobial resistance.
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