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      Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock — rationale and design of a multicenter randomized controlled superiority trial

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          Abstract

          Background

          Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines and clinicians continue to rely on standard dosing schemes. To address this challenge, we developed AutoKinetics, a clinical decision support system for antibiotic dosing. By feeding large amounts of electronic health record patient data into pharmacokinetic models, patient-specific predicted future plasma concentrations are displayed graphically. In addition, a tailored dosing advice is provided at the bedside in real time. To evaluate the effect of AutoKinetics on pharmacometric and clinical endpoints, we are conducting the Right Dose Right Now multicenter, randomized controlled, two-arm, parallel-group, non-blinded, superiority trial.

          Methods

          All adult intensive care patients with a suspected or proven infection and having either lactatemia or receiving vasopressor support are eligible for inclusion. Randomization to the AutoKinetics or control group is initiated at the bedside when prescribing at least one of four commonly administered antibiotics: ceftriaxone, ciprofloxacin, meropenem and vancomycin. Dosing advice is available for patients in the AutoKinetics group, whereas patients in the control group receive standard dosing.

          The primary outcome of the study is pharmacometric target attainment during the first 24 h. Power analysis revealed the need for inclusion of 42 patients per group per antibiotic. Thus, a total of 336 patients will be included, 168 in each group. Secondary pharmacometric endpoints include time to target attainment and fraction of target attainment during an entire antibiotic course. Secondary clinical endpoints include mortality, clinical cure and days free from organ support. Several other exploratory and subgroup analyses are planned.

          Discussion

          This is the first randomized controlled trial to assess the effectiveness and safety of bedside data-driven automated antibiotic dosing advice. This is important as adequate antibiotic exposure may be crucial to treat severe sepsis and septic shock. In addition, the trial could prove to be a significant contribution to clinical pharmacometrics and serve as a stepping stone for the use of big data and artificial intelligence in the field.

          Trial registration

          Netherlands Trial Register (NTR), NL6501/NTR6689. Registered on 25 August 2017.

          European Clinical Trials Database (EudraCT), 2017-002478-37. Registered on 6 November 2017.

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          Most cited references 23

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          Epidemiology of severe sepsis

          Severe sepsis is a leading cause of death in the United States and the most common cause of death among critically ill patients in non-coronary intensive care units (ICU). Respiratory tract infections, particularly pneumonia, are the most common site of infection, and associated with the highest mortality. The type of organism causing severe sepsis is an important determinant of outcome, and gram-positive organisms as a cause of sepsis have increased in frequency over time and are now more common than gram-negative infections. Recent studies suggest that acute infections worsen pre-existing chronic diseases or result in new chronic diseases, leading to poor long-term outcomes in acute illness survivors. People of older age, male gender, black race, and preexisting chronic health conditions are particularly prone to develop severe sepsis; hence prevention strategies should be targeted at these vulnerable populations in future studies.
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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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              The Surviving Sepsis Campaign Bundle

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                Author and article information

                Contributors
                l.roggeveen@amsterdamumc.nl
                l.fleuren@amsterdamumc.nl
                t.guo@amsterdamumc.nl
                p.thoral@amsterdamumc.nl
                h.degrooth@amsterdamumc.nl
                e.l.swart@amsterdamumc.nl
                t.klausch@amsterdamumc.nl
                p.h.j.vandervoort@olvg.nl
                arj.girbes@amsterdamumc.nl
                r.j.bosman@olvg.nl
                p.elbers@amsterdamumc.nl
                Journal
                Trials
                Trials
                Trials
                BioMed Central (London )
                1745-6215
                18 December 2019
                18 December 2019
                2019
                : 20
                Affiliations
                [1 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Research VUmc Intensive Care (REVIVE), Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), , Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, ; De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
                [2 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Clinical Pharmacology and Pharmacy, , Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, ; De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
                [3 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Epidemiology and Biostatistics, , Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, ; De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
                [4 ]Intensive Care Unit, OLVG Oost, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
                Article
                3911
                10.1186/s13063-019-3911-5
                6921499
                31852491
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, ZonMw;
                Award ID: 80-83600-98-40050
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2019

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