Evidence map›Paper›PMID 38413858›Full record

ArticleBMC nephrology2024

Antibiotic dosing recommendations in critically ill patients receiving new innovative kidney replacement therapy.

Susan J Lewis, Bruce A Mueller

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.2field-weighted citation impact, top 14% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Susan J LewisDepartment of Pharmacy Practice, College of Pharmacy, University of Findlay, 1000 N. Main Street, 45840, Findlay, OH, USA. slewis@findlay.edu.
Bruce A MuellerClinical Pharmacy Department, College of Pharmacy, University of Michigan, MI, 48109, Ann Arbor, USA.
University of Findlay · USUniversity of Michigan–Ann Arbor · US

Funding

Outset Medical Inc N032926-493065
6 · The paper itself

Abstract

backgroundThe Tablo Hemodialysis System is a new innovative kidney replacement therapy (KRT) providing a range of options for critically ill patients with acute kidney injury. The use of various effluent rate and treatment durations/frequencies may clear antibiotics differently than traditional KRT. This Monte Carlo Simulation (MCS) study was to develop antibiotic doses likely to attain therapeutic targets for various KRT combinations.

methodsPublished body weights and pharmacokinetic parameter estimates were used to predict drug exposure for cefepime, ceftazidime, imipenem, meropenem and piperacillin/tazobactam in virtual critically ill patients receiving five KRT regimens. Standard free β-lactam plasma concentration time above minimum inhibitory concentration targets (40-60%fT

resultsMCS determined β-lactam doses achieving ∼90% PTA in all KRT options. KRT characteristics influenced antibiotic dosing. Cefepime and piperacillin/tazobactam regimens designed for rigorous efficacy targets were likely to exceed toxicity thresholds.

conclusionThe flexibility offered by new KRT systems can influence β-lactam antibiotic dosing, but doses can be devised to meet therapeutic targets. Further clinical validations are warranted.

Indexed as

Anti-Bacterial AgentsCritical IllnessCefepimeCeftazidimeHumansPiperacillin, Tazobactam Drug CombinationRenal DialysisAnti-Bacterial AgentsCefepimeCeftazidimePiperacillin, Tazobactam Drug CombinationAntibioticsBeta-lactamsCritically-illMonte Carlo simulationPharmacokinetics/pharmacodynamicsTablo kidney replacement therapy

Identifiers

PMID38413858
PMCPMC10900833
OpenAlexW4392200052

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.