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Record W2598786180 · doi:10.1093/cid/cix275

Reply to Hassoun et al

2017· letter· fr· W2598786180 on OpenAlexaff
Daniel A. Sweeney, Michael Klompas, John Muscedere, Mark L. Metersky, André C. Kalil

Bibliographic record

VenueClinical Infectious Diseases · 2017
Typeletter
Languagefr
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0070.005
Open science0.0030.003
Research integrity0.1150.058
Insufficient payload (model declined to judge)0.0120.011

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.

Opus teacher head0.075
GPT teacher head0.445
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations1
Published2017
Admission routes1
Has abstractno

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