Burden of Antimicrobial Resistance in Adult Hospitalized Patients With Cancer: A Multicenter Analysis
Bibliographic record
Abstract
BACKGROUND: Infections are a leading cause of death in patients with cancer, but the proportion and rate of antimicrobial resistance (AMR) in hospitalized patients with cancer are not well understood. METHODS: This retrospective, cross-sectional evaluation of AMR assessed hospitalized adult patients in 168 United States (US) healthcare facilities between April 2018 and December 2022. Nonduplicate, noncontaminant Gram-negative and Gram-positive bacteria recovered from various samples (blood, respiratory, urine, etc.) were used to assess the rate of AMR pathogens per 1000 admissions and the proportion of AMR among bacterial isolates in patients with and without cancer. FINDINGS: Among 4,612,620 admissions, 6.4% (297,500) were of patients with cancer and 93.6% (4,315,120) were of patients without cancer. AMR pathogen rates were higher in cancer patients than patients without cancer for most pathogen groups, including vancomycin-resistant enterococci with incidence rate ratio (IRR), 1.95 (95% confidence interval [CI], 1.84, 2.07), extended-spectrum beta-lactamase (ESBL) producers (IRR, 1.48 [95% CI, 1.43, 1.53]), carbapenem-nonsusceptible Enterobacterales (IRR, 1.46 [95% CI, 1.32, 1.61]), and multidrug-resistant Pseudomonas aeruginosa (IRR, 1.31 [95% CI, 1.18, 1.45]). The percentage of nonsusceptible isolates in most pathogen groups was lower in patients with versus without cancer except for ESBL producers among Enterobacterales (odds ratio (OR), 1.11 [95% CI, 1.07, 1.15]) and vancomycin resistance among enterococci (OR, 1.22 [95% CI, 1.14, 1.30]), which were higher in cancer patients. CONCLUSION: AMR rates for certain key pathogens were 1.5-2 times greater in hospitalized cancer patients compared to hospitalized noncancer patients. The increased AMR rate in cancer patients highlights the need for enhanced infection prevention and diagnostic stewardship efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".