Low utilisation of bronchoscopy to assess COVID-19 respiratory infection: a multicenter experience
Bibliographic record
Abstract
OBJECTIVE: For the diagnosis of COVID-19, the yield of nasopharyngeal (NP) swabs is unclear, and bronchoalveolar lavage (BAL) is obtained to confirm the diagnosis. We assessed the utilisation of bronchoscopy for COVID-19 diagnosis in a multicenter study and compared the diagnostic yield of BAL versus NP swabs. METHODS: This retrospective study included all patients who were admitted with clinical presentation concerning for COVID-19 and underwent BAL from 1 March to 31 July 2020 at four tertiary care centres in North America. We also compared concordance of BAL with NP swabs for diagnosis of COVID-19 infection. RESULTS: Fifty-three patients, with clinical suspicion for COVID-19 and admitted for respiratory failure, underwent bronchoscopy to collect BAL for SARS-CoV-2 testing. During the same period, 2039 bronchoscopies were performed on patients not infected with COVID-19. Of 42 patients with NP swabs and BAL collected within ≤7 days, 1 was NP swab negative but positive by BAL for SARS-CoV-2 (n=1/42 (2.4%)). Across a wide array of testing platforms, the overall agreement between NP swabs and BAL results was 97.6% (95% CI: 93.0% to 100%) with Cohen's k of 0.90 (95% CI: 0.69 to 1.00). The sensitivity, specificity, positive and negative predictive values of NP swabs compared with BAL were 83.3% (95% CI: 53.5% to 100%), 100%, 100% and 97.3% (95% CI: 92.1% to 100%), respectively. CONCLUSIONS: BAL was used infrequently to assess COVID-19 in busy institutions. NP swabs have a high concordance with BAL for COVID-19 testing, but negative NP swabs should be confirmed with BAL when clinical suspicion is high.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".