Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial
Luca F. Roggeveen,
Tingjie Guo,
Lucas M. Fleuren,
Ronald Driessen,
Patrick Thoral,
Reinier M. van Hest,
Ron A. A. Mathot,
Eleonora L. Swart,
Harm-Jan de Grooth,
Bas van den Bogaard,
Armand R. J. Girbes,
Rob J. Bosman,
Paul W. G. Elbers
Affiliations
Luca F. Roggeveen
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Tingjie Guo
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Lucas M. Fleuren
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Ronald Driessen
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Patrick Thoral
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Reinier M. van Hest
Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam
Ron A. A. Mathot
Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam
Eleonora L. Swart
Department of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam
Harm-Jan de Grooth
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Bas van den Bogaard
Department of Intensive Care, OLVG Hospital
Armand R. J. Girbes
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Rob J. Bosman
Department of Intensive Care, OLVG Hospital
Paul W. G. Elbers
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AI&II), Amsterdam UMC, Vrije Universiteit
Abstract Background Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation. Methods In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury. Results After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4–1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18–42 h, p < 0.001) and better (65% increase, CI 49–84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure. Conclusions In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin. Trial registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017.
