Evidence map›Paper›PMID 36064438›Full record

Trial reportCritical care (London, England)2022

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 and 3 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Article
  6. Target controlled infusion in the intensive care unit: a scoping review.Journal of clinical monitoring and computing · 2026
    Article
  7. Article
  8. Review
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  15. What do we know about micronutrients in critically ill patients? A narrative review.JPEN. Journal of parenteral and enteral nutrition · 2025
    Review
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  18. The OPAT opportunity for beta-lactam individualization.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024
    Article
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  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

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, Amsterdam, The Netherlands. l.roggeveen@amsterdamumc.nl.
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, Amsterdam, The Netherlands.
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, Amsterdam, The Netherlands.
Ronald DriessenDepartment 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, Amsterdam, The Netherlands.
Patrick ThoralDepartment 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, Amsterdam, The Netherlands.
Reinier M van HestDepartment of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Ron A A MathotDepartment of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Eleonora L SwartDepartment of Pharmacy and Clinical Pharmacology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Harm-Jan de GroothDepartment 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, Amsterdam, The Netherlands.
Bas van den BogaardDepartment of Intensive Care, OLVG Hospital, Amsterdam, The Netherlands.
Armand R J GirbesDepartment 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, Amsterdam, The Netherlands.
Rob J BosmanDepartment of Intensive Care, OLVG Hospital, Amsterdam, The Netherlands.
Paul W G ElbersDepartment 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, Amsterdam, The Netherlands.

Funding

ZonMw 836041018
6 · The paper itself

Abstract

backgroundAdequate 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.

methodsIn 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.

resultsAfter 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.

conclusionsIn critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin.

trial registrationThe 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.

Indexed as

COVID-19SepsisShock, SepticAdultAnti-Bacterial AgentsCiprofloxacinCritical IllnessHumansPandemicsAnti-Bacterial AgentsCiprofloxacinClinical decision supportPharmacokineticsSepsisTherapeutic drug monitoring

Identifiers

PMID36064438
PMCPMC9443636

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.