Evidence map›Paper›PMID 40924422›Full record

Observational studyJAMA network open2025

Broad-Spectrum Antibiotic Use at the End of Life in Patients With Advanced Cancer.

Jeong-Han Kim, Jiwon Yu, Shin Hye Yoo, Jin-Ah Sim, Bhumsuk Keam, Dae Seog Heo

Abstract readObservational Study
In one paragraph

Observational study in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. 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

6 authors.

Jeong-Han KimDivision of Infectious Diseases, Department of Internal Medicine, Ewha Woman University College of Medicine, Mokdong Hospital, Seoul, South Korea.
Jiwon YuDepartment of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea.
Shin Hye YooCenter for Palliative Care and Clinical Ethics, Seoul National University Hospital, Seoul, South Korea.
Jin-Ah SimDepartment of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea.
Bhumsuk KeamCenter for Palliative Care and Clinical Ethics, Seoul National University Hospital, Seoul, South Korea.
Dae Seog HeoDepartment of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Patients with advanced cancer frequently receive broad-spectrum antibiotics, but changing use patterns across the end-of-life trajectory remain poorly understood. Objective: To describe the patterns of broad-spectrum antibiotic use across defined end-of-life intervals in patients with advanced cancer. Design, Setting, and Participants: This nationwide, population-based, retrospective cohort study used data from the South Korean National Health Insurance Service database to examine broad-spectrum antibiotic use among patients with advanced cancer who died between July 1, 2002, and December 31, 2021. Data extraction and analysis were conducted between September 2023 and August 2024. Exposure: A diagnosis of lung cancer, liver cancer, stomach cancer, colorectal cancer, pancreatic cancer, prostate cancer, gallbladder and biliary tract cancer, breast cancer, non-Hodgkin lymphoma, leukemia, or multiple myeloma. Main Outcomes and Measures: The use of broad-spectrum antibiotics (ie, antipseudomonal β-lactams, carbapenems, or glycopeptides) was evaluated using 2 metrics according to end-of-life trajectory: (1) prescription proportion (percentage of patients receiving antibiotics) and (2) consumption amount (days of therapy per 1000 patient-days). The end-of-life trajectory was divided into 5 intervals: T1 (6 months to 3 months before death), T2 (3 months to 1 month before death), T3 (1 month to 2 weeks before death), T4 (2 weeks to 1 week before death), and T5 (final week before death). Logistic regression was performed to calculate odds ratios and 95% CIs for antibiotic prescription proportion without adjustment for multiple comparisons, and Poisson regression was used to calculate adjusted relative risks. Results: Among the 515 366 decedents included, the mean (SD) age was 68.8 (11.7) years, and 347 327 (67.4%) were male. A total of 483 405 patients (93.8%) had solid tumors, with lung cancer (122 142 patients [23.7%]) being the most common type. Overall, 288 151 patients (55.9%) received broad-spectrum antibiotics during the last 6 months of life. The proportion of patients receiving broad-spectrum antibiotics peaked during T2, with 144 920 (28.1%) receiving at least 1 dose, and declined to 68 564 (13.3%) during T5. In contrast, total consumption peaked during T3, reaching 190.0 days of therapy per 1000 patient-days. These patterns were consistent across antibiotic classes and cancer types. During the last week of life, patients with leukemia had the highest exposure to broad-spectrum antibiotics compared with those with lung cancer, both for prescription proportions (crude odds ratio, 1.50; 95% CI, 1.43-1.58) and total consumption (adjusted relative risk, 1.21; 95% CI, 1.19-1.23). Conclusions and Relevance: In this cohort study of patients with advanced cancer, broad-spectrum antibiotic use increased from 3 months to 2 weeks before death, suggesting that this may be a key period for optimizing use and aligning care with patient goals.

Indexed as

Anti-Bacterial AgentsBacterial InfectionsNeoplasmsTerminal CareAgedAged, 80 and overDatabases, FactualFemaleHumansMaleMiddle AgedNational Health ProgramsNeoplasm StagingPractice Patterns, Physicians'Republic of KoreaRetrospective StudiesAnti-Bacterial Agents

Identifiers

PMID40924422
PMCPMC12421337

What OpenQuestion holds

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LicenceCC BY
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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.