Evidence map›Paper›PMID 39853643›Full record

Trial reportJournal of clinical monitoring and computing2025

Cost-effectiveness of data driven personalised antibiotic dosing in critically ill patients with sepsis or septic shock.

Hana M Broulikova, Jacqueline Wallage, Luca Roggeveen, Lucas Fleuren, Tingjie Guo, Paul W G Elbers, Judith E Bosmans

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of clinical monitoring and computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Hana M BroulikovaDepartment of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health research institute, Van der Boechorststraat 7, Amsterdam, 1081 BT, the Netherlands. h.m.broulikova@vu.nl.ORCID 0000-0002-1662-3005
Jacqueline WallageDepartment of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Cardiovascular Science, Amsterdam Institute for Infection and Immunity, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, De Boelelaan 1117, Amsterdam, 1081 HV, the Netherlands.
Luca RoggeveenDepartment of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Cardiovascular Science, Amsterdam Institute for Infection and Immunity, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, De Boelelaan 1117, Amsterdam, 1081 HV, the Netherlands.
Lucas FleurenDepartment of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Cardiovascular Science, Amsterdam Institute for Infection and Immunity, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, De Boelelaan 1117, Amsterdam, 1081 HV, the Netherlands.
Tingjie GuoSystem Pharmacology and Pharmacy, Leiden Academic Center for Drug Research (LACDR), Leiden University, Wassenaarseweg 76, Leiden, 2333 AL, the Netherlands.ORCID 0000-0002-7892-6220
Paul W G ElbersDepartment of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Cardiovascular Science, Amsterdam Institute for Infection and Immunity, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, De Boelelaan 1117, Amsterdam, 1081 HV, the Netherlands.ORCID 0000-0003-0447-6893
Judith E BosmansDepartment of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health research institute, Van der Boechorststraat 7, Amsterdam, 1081 BT, the Netherlands.ORCID 0000-0002-1443-1026

Funding

ZonMw ZonMW GGG 836041018
6 · The paper itself

Abstract

purposeThis study provides an economic evaluation of bedside, data-driven, and model-informed precision dosing of antibiotics in comparison with usual care among critically ill patients with sepsis or septic shock.

methodsThis economic evaluation was conducted alongside an AutoKinetics randomized controlled trial. Effect measures included quality-adjusted life years (QALYs), mortality and pharmacokinetic target attainment. Costs were measured from a societal perspective. Missing data was multiply imputed, and bootstrapping was used to estimate statistical uncertainty. Differences in effects and costs were estimated using bivariate regression and used to calculate incremental cost-effectiveness ratios.

resultsPatients in the intervention group had higher costs (€42,684 vs. 39,475), lower mortality (42% vs. 49%), more QALYs (0.184 vs. 0.153), and higher pharmacokinetic target attainment (69% vs. 48%). Only the difference for target attainment was found statistically significant. An additional €18,129, €55,576, and €123,493 needs to be invested to attain the targeted plasma levels for one more patient, to save one life and gain one QALY, respectively. The probability of cost-effectiveness for all effect outcomes is below 60% for most acceptable willingness-to-pay thresholds.

conclusionsData-driven personalised antibiotic dosing in critically ill patients as implemented in the AutoKinetics trial cannot be recommended for implementation as a cost-effective intervention.

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

Anti-Bacterial AgentsCost-Benefit AnalysisPrecision MedicineSepsisShock, SepticAgedCritical IllnessFemaleHumansMaleMiddle AgedQuality-Adjusted Life YearsAnti-Bacterial AgentsAntibioticsCost-effectiveness analysisIntensive care softwareModel-informed precision dosingSepsisSeptic shock

Identifiers

PMID39853643
PMCPMC12474602

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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.