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      What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

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          Abstract

          Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

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          Most cited references119

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          NHSN annual update: antimicrobial-resistant pathogens associated with healthcare-associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006-2007.

          To describe the frequency of selected antimicrobial resistance patterns among pathogens causing device-associated and procedure-associated healthcare-associated infections (HAIs) reported by hospitals in the National Healthcare Safety Network (NHSN). Data are included on HAIs (ie, central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, and surgical site infections) reported to the Patient Safety Component of the NHSN between January 2006 and October 2007. The results of antimicrobial susceptibility testing of up to 3 pathogenic isolates per HAI by a hospital were evaluated to define antimicrobial-resistance in the pathogenic isolates. The pooled mean proportions of pathogenic isolates interpreted as resistant to selected antimicrobial agents were calculated by type of HAI and overall. The incidence rates of specific device-associated infections were calculated for selected antimicrobial-resistant pathogens according to type of patient care area; the variability in the reported rates is described. Overall, 463 hospitals reported 1 or more HAIs: 412 (89%) were general acute care hospitals, and 309 (67%) had 200-1,000 beds. There were 28,502 HAIs reported among 25,384 patients. The 10 most common pathogens (accounting for 84% of any HAIs) were coagulase-negative staphylococci (15%), Staphylococcus aureus (15%), Enterococcus species (12%), Candida species (11%), Escherichia coli (10%), Pseudomonas aeruginosa (8%), Klebsiella pneumoniae (6%), Enterobacter species (5%), Acinetobacter baumannii (3%), and Klebsiella oxytoca (2%). The pooled mean proportion of pathogenic isolates resistant to antimicrobial agents varied significantly across types of HAI for some pathogen-antimicrobial combinations. As many as 16% of all HAIs were associated with the following multidrug-resistant pathogens: methicillin-resistant S. aureus (8% of HAIs), vancomycin-resistant Enterococcus faecium (4%), carbapenem-resistant P. aeruginosa (2%), extended-spectrum cephalosporin-resistant K. pneumoniae (1%), extended-spectrum cephalosporin-resistant E. coli (0.5%), and carbapenem-resistant A. baumannii, K. pneumoniae, K. oxytoca, and E. coli (0.5%). Nationwide, the majority of units reported no HAIs due to these antimicrobial-resistant pathogens.
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            Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock.

            Our goal was to determine the impact of the initiation of inappropriate antimicrobial therapy on survival to hospital discharge of patients with septic shock. The appropriateness of initial antimicrobial therapy, the clinical infection site, and relevant pathogens were retrospectively determined for 5,715 patients with septic shock in three countries. Therapy with appropriate antimicrobial agents was initiated in 80.1% of cases. Overall, the survival rate was 43.7%. There were marked differences in the distribution of comorbidities, clinical infections, and pathogens in patients who received appropriate and inappropriate initial antimicrobial therapy (p < 0.0001 for each). The survival rates after appropriate and inappropriate initial therapy were 52.0% and 10.3%, respectively (odds ratio [OR], 9.45; 95% CI, 7.74 to 11.54; p < 0.0001). Similar differences in survival were seen in all major epidemiologic, clinical, and organism subgroups. The decrease in survival with inappropriate initial therapy ranged from 2.3-fold for pneumococcal infection to 17.6-fold with primary bacteremia. After adjustment for acute physiology and chronic health evaluation II score, comorbidities, hospital site, and other potential risk factors, the inappropriateness of initial antimicrobial therapy remained most highly associated with risk of death (OR, 8.99; 95% CI, 6.60 to 12.23). Inappropriate initial antimicrobial therapy for septic shock occurs in about 20% of patients and is associated with a fivefold reduction in survival. Efforts to increase the frequency of the appropriateness of initial antimicrobial therapy must be central to efforts to reduce the mortality of patients with septic shock.
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              DALI: defining antibiotic levels in intensive care unit patients: are current β-lactam antibiotic doses sufficient for critically ill patients?

              Morbidity and mortality for critically ill patients with infections remains a global healthcare problem. We aimed to determine whether β-lactam antibiotic dosing in critically ill patients achieves concentrations associated with maximal activity and whether antibiotic concentrations affect patient outcome. This was a prospective, multinational pharmacokinetic point-prevalence study including 8 β-lactam antibiotics. Two blood samples were taken from each patient during a single dosing interval. The primary pharmacokinetic/pharmacodynamic targets were free antibiotic concentrations above the minimum inhibitory concentration (MIC) of the pathogen at both 50% (50% f T>MIC) and 100% (100% f T>MIC) of the dosing interval. We used skewed logistic regression to describe the effect of antibiotic exposure on patient outcome. We included 384 patients (361 evaluable patients) across 68 hospitals. The median age was 61 (interquartile range [IQR], 48-73) years, the median Acute Physiology and Chronic Health Evaluation II score was 18 (IQR, 14-24), and 65% of patients were male. Of the 248 patients treated for infection, 16% did not achieve 50% f T>MIC and these patients were 32% less likely to have a positive clinical outcome (odds ratio [OR], 0.68; P = .009). Positive clinical outcome was associated with increasing 50% f T>MIC and 100% f T>MIC ratios (OR, 1.02 and 1.56, respectively; P < .03), with significant interaction with sickness severity status. Infected critically ill patients may have adverse outcomes as a result of inadeqaute antibiotic exposure; a paradigm change to more personalized antibiotic dosing may be necessary to improve outcomes for these most seriously ill patients.
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                Author and article information

                Journal
                Pharmaceutics
                Pharmaceutics
                pharmaceutics
                Pharmaceutics
                MDPI
                1999-4923
                07 July 2020
                July 2020
                : 12
                : 7
                : 638
                Affiliations
                [1 ]University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine, The University of Queensland, Brisbane 4006, Australia; g.chai@ 123456uq.net.au (M.G.C.); m.o.cotta@ 123456uq.edu.au (M.O.C.); h.abdulaziz@ 123456uq.edu.au (M.H.A.-A.)
                [2 ]Centre for Translational Anti-infective Pharmacodynamics, School of Pharmacy, The University of Queensland, Woollongabba 4102, Australia
                [3 ]Departments of Pharmacy and Intensive Care, Royal Brisbane and Women’s Hospital, Brisbane 4006, Australia
                [4 ]Division of Anaesthesiology Critical Care Emergency and Pain Medicine, Nimes University Hospital, University of Montpellier, 30021 Nimes, France
                Author notes
                [* ]Correspondence: j.roberts2@ 123456uq.edu.au
                Author information
                https://orcid.org/0000-0002-8889-5579
                Article
                pharmaceutics-12-00638
                10.3390/pharmaceutics12070638
                7407796
                32645953
                5afdc35c-fe1f-401c-94d1-6bf890c93c95
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 04 June 2020
                : 01 July 2020
                Categories
                Review

                nomogram,software,antimicrobials,critical illness,pharmacokinetics,bayesian forecasting,sepsis,artificial intelligence

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