Reply to Hassoun et al
Bibliographic record
Abstract
Hassoun and colleagues question whether the hospital-acquired (HAP) and ventilator-associated pneumonia (VAP) guidelines recently published in Clinical Infectious Diseases were “fair and balanced” in their review of telavancin for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) pneumonia [1, 2]. Our guideline panelists had substantial concerns about the evidence supporting the use of telavancin for MRSA HAP/VAP. The use of telavancin was based on 2 randomized, double-blind, industry-sponsored studies, the Assessment of Telavancin for Treatment of Hospital-Acquired Pneumonia (ATTAIN studies). These studies compared telavancin versus vancomycin for treatment of HAP/VAP. Despite enrolling 1532 patients overall, only 221 patients had MRSA HAP and only 69 patients had MRSA VAP [3, 4]. There was no difference between the telavancin and vancomycin groups in clinical cure, but there was a discrepancy in all-cause mortality between the 2 ATTAIN studies. The first study found higher mortality rates in patients randomized to telavancin (21.5% vs 16.6%; 95% confidence interval [CI] for difference, −.7% to 10.6%) whereas the second study did not (18.5% vs 20.6%; 95% CI for difference, −7.8% to 3.5%). Further analysis of both ATTAIN studies revealed a trend toward increased renal adverse events with discontinuation of therapy in the telavancin group (14 telavancin-treated patients [1.9%] and 7 vancomycin-treated patients [0.9%]), as well as higher mortality (14% [95% CI, −29.2 to 1]) among patients with creatinine clearance <30 mL/min who received telavancin. Our concerns echo those of the Food and Drug Administration panel that reviewed telavancin. The panel voted 9 − 6 against recommending telavancin as a first-line treatment and ultimately approved telavancin only “when alternative treatments are not suitable” [5]. Hassoun and colleagues cited a subgroup post hoc analysis suggesting that the increased mortality signal in patients randomized to telavancin was driven exclusively by inadequate treatment of Gram-negative pneumonias [6]. These types of subanalyses are at best hypothesis generating and should not be used to guide first-line clinical practice. It is not appropriate to overturn the primary analysis of randomized controlled trials in favor of a manufacturer-sponsored post hoc analysis of a small subset of the studies. To ensure that the guidelines are fair and balanced, we embraced current best practices when putting together our recommendations including the use of Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. The GRADE methodology explicitly encourages guideline writers to downgrade evidence for risk of imprecision, inconsistency, bias, and indirectness. The small number of patients with MRSA infections (especially MRSA VAP), the inconsistency in mortality signals between the 2 registration studies, the recourse to a subgroup post hoc analysis to explain the higher mortality rates in the telavancin group, and the availability of better studied alternatives such as vancomycin and linezolid are the reasons we did not recommend telavancin as first-line therapy for MRSA pneumonia. Potential conflicts of interest. As reported in the 2016 Guideline, M. L. M. reports that he has participated as an investigator in clinical trials related to bronchiectasis sponsored by Aradigm and Gilead. His employer has received remuneration for this work. Prior to beginning work on this Guideline, he served as a consultant and speaker for Pfizer. J. M. reports grants from Bayer Pharma, outside the submitted work. All other authors: no reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.115 | 0.058 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".