559. Results of the implementation of a molecular pneumonia panel at a hospital in the Dominican Republic: expanding the scope of detection and management
Bibliographic record
Abstract
Abstract Background Lower respiratory tract infections (LRTIs) have been associated to significant morbidity and mortality. Conventional microbiology methods often fail to identify the etiological agent due to lack of sensitivity or viral or fastidious pathogens. Pneumonia molecular diagnostics is able to expand the scope of pathogens detected, therefore, we describe our experience in the incorporation of this method, the difference in etiological identification and clinical decision making. Methods We performed a retrospective cohort study of 75 patients with Filmarray pneumonia panel from November 2020 to September 2022. We described demographics, co-morbid conditions, outcomes and correlation with standard cultures in patients who were admitted with LRTI at the Hospital General de la Plaza de la Salud, a 289-bed tertiary teaching hospital in the Dominican Republic. Results Amongst the 75 patients, 56.6% were male and the average age was 51 years old. The most common comorbidities were diabetes mellitus (81.6%), hypertension (40.8%) and nephropathy (10.5%). The panels had a positivity rate of 74.6% (56/75) versus a 35% (14/40) seen in cultures. The most frequent microbial targets detected were S. aureus (18%), P. aeruginosa (14,3%) and K. pneumoniae group (10%) and at least one antimicrobial resistance gene was detected in 50% (28/56), distributed as MecA/C and MREJ (35%), CTX-M (38%), KPC (10%), NDM (10%), VIM (8%), IMP (8%) and OXA-48 like (3%). A positive culture was most likely to occur when the number of copies/mL in the panel was ≥ 10˄5 (p-value < 0.001). A viral target was detected in 21 patients (28%) alone or coinfecting with a bacterial agent. Conclusion Not only did the pneumonia panels allow an earlier detection of the etiological agent, but they also identified more pathogens compared to cultures. This enabled targeted management by reducing the unnecessary use of antimicrobials as well as activating infection control measures when necessary (i.e Influenza). The results seen in this study, along with the expanded scope of pathogens detected, will definitely serve as an update into our current LRTI hospital guidelines. Disclosures All Authors: No reported disclosures
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".