Global trends in carbapenem- and difficult-to-treat-resistance among World Health Organization priority bacterial pathogens: ATLAS surveillance program 2018–2022
Bibliographic record
Abstract
OBJECTIVES: To report trends in carbapenem resistance and difficult-to-treat resistance (DTR) among clinical isolates of Gram-negative priority pathogens collected by the ATLAS global surveillance program from 2018 to 2022. METHODS: Reference broth microdilution testing was performed in a central laboratory for 79,214 Enterobacterales, 30,504 Pseudomonas aeruginosa, and 13,500 Acinetobacter baumannii-calcoaceticus complex isolates collected by a constant set of 157 medical centres in 49 countries in Asia Pacific (APAC), Europe (EUR), Latin America (LATAM), Middle East-Africa (MEA), and North America (NA) regions. MICs were interpreted by 2023 CLSI M100 breakpoints. β-lactamase genes were identified for meropenem-nonsusceptible (MIC ≥2 mg/L) Enterobacterales isolates. RESULTS: Carbapenem-resistant Enterobacterales (CRE) detection increased (P < 0.05) in APAC, EUR, LATAM, and MEA regions and decreased in NA, while annual DTR percentages increased in all five regions. Carbapenem-resistant P. aeruginosa (CRPA; decreased in MEA region) and carbapenem-resistant A. baumannii-calcoaceticus complex (CRAB; decreased in MEA region and increased in EUR) remained relatively stable over time in all regions, although notably, annual percentages of CRAB and DTR A. baumannii-calcoaceticus complex isolates were consistently >25 percentage points lower in NA than in other regions. For all regions except NA, the majority of changes in CRE percentages could be attributed to hospital-acquired infections. Among meropenem-nonsusceptible Enterobacterales, KPC was the most frequent carbapenemase in NA and EUR each year. NDM was the most prevalent carbapenemase detected in 2022 in other global regions. CONCLUSION: CRE, CRPA, CRAB, and DTR rates vary among global regions over time highlighting the need for continuing surveillance to inform treatment strategies and antimicrobial stewardship.
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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 | 0.001 | 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) | 0.000 | 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".