Aetiology of hospital-acquired pneumonia and trends in antimicrobial resistance
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) continue to present very significant diagnostic and management challenges. The development, introduction and use of a wider range of immunosuppressive therapies are leading to a broader spectrum of microorganisms causing HAP and VAP. The persistent clinical dilemma regarding their cause is that detection of a microorganism from a respiratory tract sample does not necessarily signify it is the causative agent of the pneumonia. The ever-increasing antibiotic resistance problem means that HAP and VAP are becoming progressively more difficult to treat. In this article, we review the cause, antimicrobial resistance, diagnosis and treatment of HAP and VAP and encapsulate recent developments and concepts in this rapidly moving field. RECENT FINDINGS: Although the microbial causes of HAP and VAP remain at present similar to those identified in previous studies, there are marked geographical differences. Resistance rates among Gram-negative bacteria are continually increasing, and for any species, multiresistance is the norm rather than the exception. The development and introduction of rapid point-of-care diagnostics may improve understanding of the cause of HAP and VAP and has immense potential to influence the treatment and clinical outcomes in HAP/VAP, with patients likely to receive much faster, microorganism-specific treatment with obvious downstream improvements to clinical outcome and antimicrobial stewardship. SUMMARY: We describe recent trends in aetiology of HAP and VAP and recent trends in antimicrobial resistance, including resistance mechanisms causing particular concern. The potential for novel molecular diagnostics to revolutionize the diagnosis and treatment of HAP/VAP is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".