Evidence map›Paper›PMID 41670408›Full record

ArticleCritical care explorations2026

Strategies for Safer Cefepime Use to Prevent Neurotoxicity Using the Electronic Health Record.

Sahar F Zafar, Hemi Park, Alyssa R Letourneau, Alex E Rock, M Brandon Westover, Shibani S Mukerji

Abstract read
In one paragraph

Article in Critical care explorations, 2026. 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

6 authors.

Sahar F ZafarDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Hemi ParkDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Alyssa R LetourneauHarvard Medical School, Boston, MA.
Alex E RockDepartment of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
M Brandon WestoverHarvard Medical School, Boston, MA.
Shibani S MukerjiDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.ORCID 0000-0002-5677-6954

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cefepime, a cornerstone antibiotic in critical care, is associated with underrecognized cefepime-induced neurotoxicity (CIN), particularly in patients 65 years old and older. The true incidence is unknown due to inconsistent monitoring and a lack of diagnostic criteria. The recent Antibiotic Choice on Renal Outcomes (ACORN) trial underscored CIN's clinical significance, finding that cefepime recipients experienced 21% fewer delirium- and coma-free days than those on piperacillin-tazobactam. Current guidelines lack active surveillance recommendations, leading to delayed diagnosis and intervention. We propose three informatics-based strategies to address these challenges: 1) electronic health record (EHR)-integrated datasets utilizing machine learning and natural language processing to identify CIN at scale, 2) automated electroencephalogram tools to provide real-time alerts to clinicians, and 3) dynamic risk scores that continuously update from EHR data to guide prescribing. Implementing these safeguards to optimize CIN prevention, which may be relevant for other antibiotics with neurotoxicity risk, can improve neurologic outcomes and patient safety in critically ill populations.

Indexed as

Anti-Bacterial AgentsCefepimeElectronic Health RecordsNeurotoxicity SyndromesHumansAnti-Bacterial AgentsCefepimeantimicrobial stewardshipcefepimedeliriumelectronic health recordneurotoxicity

Identifiers

PMID41670408
PMCPMC12900184

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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.