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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".