1776. Step-Wise Algorithm for the Detection of Respiratory Viruses: Integrating a Rapid Influenza A/B and RSV PCR with a Multiplex Respiratory Virus Panel to Target High-Risk Patient Populations
Bibliographic record
Abstract
Abstract Background In clinical settings, multiplex molecular panels are becoming increasingly common for the detection of respiratory pathogens. Little evidence is available to guide appropriate use of respiratory multiplex panels, particularly with respect to the patient populations most likely to benefit from such testing. Methods During the 2018–2019 influenza season, all patients with a nasopharyngeal swab submitted for respiratory virus detection were initially tested on a commercial rapid PCR platform for influenza A/B and respiratory syncytial virus (RSV) (Cepheid GeneXpert, Sunnyvale, CA). Patients with negative swabs were reviewed by a laboratory physician based on pre-defined criteria (Table 1) for additional testing by a laboratory-developed multiplex assay for parainfluenza 1/2/3, adenovirus, and human metapneumovirus (hMPV). Results In total, 1144 nasopharyngeal swabs were tested. 287 (25.1%) were positive on the GeneXpert: influenza A (234, 81.5%), influenza B (13, 4.5%), and RSV (40, 13.9%). Of the patients who tested negative, 234 (27.3%) met criteria for further respiratory virus testing. The most commonly detected viral pathogens on the multiplex assay were hMPV (20/30, 66.7%), parainfluenza 3 (7/30, 23.3%) and adenovirus (3/30, 10%). The yield of the multiplex assay was highest for patients selected for antimicrobial stewardship (AS) criteria (13/56, 23.2%), followed by transplant (2/16, 12.5%), HIV (7/64, 10.9%), cystic fibrosis (2/19, 10.5%), critical care (6/68, 8.8%), and other/upon physician request (0/11, 0%). Of the patients who received multiplex testing for AS criteria and tested positive for a viral pathogen, only 3/13 (23.1%) had antibiotics discontinued by the medical team within 48 hours of the report. Conclusion Additional testing for respiratory viral pathogens had low overall diagnostic yield, and further refinement of the algorithm is needed to better target utilization of respiratory virus testing. The patient population with the highest yield (those who met AS criteria) failed to demonstrate consistent timely discontinuation of unnecessary antibiotics by the medical team. Implementation of respiratory multiplex panels would be strengthened by collaboration with AS teams. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".