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Record W4413112231 · doi:10.1007/s00134-025-08067-w

Meropenem and piperacillin/tazobactam optimised dosing regimens for critically ill patients receiving renal replacement therapy

2025· article· en· W4413112231 on OpenAlexaff
Marta Ulldemolins, Xin Liu, João Pedro Baptista, Irma Bilgrami, Clément Boidin, Alexander Brinkmann, Pedro Castro, Gordon Choi, Louise Cole, Jan J. De Waele, Renae Deans, Sine Donnellan, Glenn M. Eastwood, Otto Frey, Sylvain Goutelle, Rebecca Gresham, Janattul‐Ain Jamal, Gavin M. Joynt, Salmaan Kanji, Stefan Kluge, Christina Koenig, Vasilios Koulouras, Melissa Lassig‐Smith, Pierre‐François Laterre, Anna Lee, Jean‐Yves Lefrant, Katie Lei, Patricia Leung, Mireia Llauradó‐Serra, Ignacio Martín‐Loeches, Mohd Basri Mat Nor, Yugan Mudaliar, Marlies Ostermann, Sanjoy K. Paul, Sandra Peake, Jordi Rello, Darren M. Roberts, Michael S. Roberts, Brent Richards, Alejandro Rodríguez, Anka C. Roehr, Claire Roger, Leonardo Seoane, Mahipal Sinnollareddy, Eduardo Sousa, Dolors Soy, Anna Spring, Dianne Stephens, Fabio Silvio Taccone, Jane Thomas, John Turnidge, Miia Valkonen, Julie Varghese, Steven C. Wallis, Robert Walker, Tricia S. Williams, Luke C. Wilson, Xavier Wittebole, Daniel F. B. Wright, Xanthi Zikou, Jeffrey Lipman

Bibliographic record

VenueIntensive Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsOttawa Hospital
FundersNational Health and Medical Research CouncilUniversity of Queensland
KeywordsDosingMeropenemMedicinePiperacillinTazobactamRenal replacement therapyPiperacillin/tazobactamIntensive careIntensive care medicinePopulationAnesthesiaAntibioticsInternal medicinePseudomonas aeruginosaAntibiotic resistance

Abstract

fetched live from OpenAlex

PURPOSE: Optimal dosing of meropenem and piperacillin/tazobactam in critically ill patients receiving renal replacement therapy (RRT) is uncertain due to variable pharmacokinetics. We aimed to develop generalisable optimised dosing recommendations for these antibiotics. METHODS: Prospective, multinational pharmacokinetic study including patients requiring various forms of RRT. Independent population PK models were developed, externally validated and applied to perform Monte Carlo dosing simulations using Monolix and Simulx. We calculated the probability that these dosing regimens achieved standard and high therapeutic unbound antibiotic concentrations over 100% of the dosing interval for the treatment of Enterobacterales and Pseudomonas aeruginosa. RESULTS: We enrolled 300 patients from 22 intensive care units across 12 countries receiving continuous veno-venous haemodialysis (13.0%), haemofiltration (23.3%), haemodiafiltration (48.4%) or sustained low-efficiency dialysis (15.3%). Models were developed using data from 234 patients (8322 samples) and validated with 66 additional patients (560 samples). Predictive performance was high, with mean prediction errors of - 5.2% for meropenem and - 16.9% for piperacillin. Dosing simulations showed that meropenem and piperacillin/tazobactam dosing requirements were dependent on urine output and RRT intensity and duration (p < 0.05). In all scenarios, extended/continuous infusions led to a better achievement of effective concentrations with lower daily doses compared to short infusion. Dosing nomograms were developed to inform dosing for different RRT settings, urine outputs, and target concentrations. CONCLUSION: RRT intensity and duration and urine output determine meropenem and piperacillin/tazobactam dosing requirements in critically ill patients receiving RRT. Extended/continuous infusions facilitate the attainment of effective concentrations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.333
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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