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Record W3027703925 · doi:10.1093/cid/ciaa603

Invasive Fungal Infections in Lung Transplant Recipients

2020· letter· en· W3027703925 on OpenAlexaff
Tina Marinelli, Coleman Rotstein

Bibliographic record

VenueClinical Infectious Diseases · 2020
Typeletter
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineLungLung transplantationMycosisIntensive care medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

(See Reply by Baker and Alexander on Volume 72, Issue 2, 15 January 2021, Pages 366–7.) To the Editor—We would like to thank Baker and colleagues for their prospective single-center study of invasive fungal infections (IFIs) in lung transplant recipients during the first 180 days posttransplant [1]. This study clearly demonstrated the incidence and timing of IFIs with a standard prophylaxis regimen of aerosolized amphotericin B lipid complex (ABLC); however, clinical applicability and generalizability of these results remain limited. We noted with interest that invasive candidiasis (IC) was the most common IFI in this study cohort. This finding diverges from the results of previous retrospective single and multicenter studies of lung transplant recipients, where Aspergillus species have consistently been identified as the most frequent causative pathogens [2–5]. In addition, surprisingly, Baker and colleagues demonstrated that most cases of IC (n = 58 [62%]) involved the pleural space or deep surgical incision site with a median time to diagnosis of just 31 days. These findings may suggest that seeding of Candida occurred at the time of operation. Further details about whether there were any identifiable risk factors, surgical or otherwise, such as Candida colonization of the donor or recipient lungs prior to transplant or use of extracorporeal membrane oxygenation (ECMO), that increased the risk of IC may certainly contribute to understanding of the pathogenesis of these findings. Identification of specific risk factors mayalso allow for a targeted systemic antifungal strategy to prevent such infections. We were struck by the fact that 58 of the 93 (62%) episodes of IC were due to pleural (n = 43) and deep surgical (n = 15) infections. One may opine that aerosolized ABLC was ineffective in preventing infections arising at these anatomical sites. We also noted that in addition to aerosolized ABLC, micafungin prophylaxis was given to patients who had delayed chest closure after lung transplantation and those on ECMO. The authors document that 18 cases of breakthrough IC occurred on prophylactic systemic antifungal therapy, including 16 of 18 (88%) cases that broke through micafungin therapy. The authors cite micafungin extraction by the ECMO circuit as a potential explanation for breakthrough IFIs but do not report on the number of lung transplant recipients requiring ECMO and what proportion of IFIs occurred in in the context of ECMO use. In vitro studies have demonstrated that micafunginas well as voriconazole, but not caspofungin orfluconazole, are extracted by the EMCO circuit [6, 7]. In critically ill patients, the use of ECMO results in a 23% reduction in the area under the curve concentration of micafungin [8]. As therapeutic drug monitoring is not routinely available for micafungin, its use in patients on ECMO should be avoided; if this is not possible, it would be prudent to consider an increased dose. Antifungal prophylaxis is advantageous in preventing IFIs in lung transplant recipients. However, universal aerosolized ABLC prophylaxis with the addition of micafungin for specific indications may not offer optimum coverage. Perhaps an alternative strategy, preemptive targeted therapy, may prove to be more effective, as demonstrated by others [9]. Potential conflicts of interest. C. R. reports grants from Astellas Pharma Canada, Chimerix Inc, Cidara, Merck Canada Inc, and Pfizer Canada Inc; personal fees from Merck Canada Inc, Pfizer Canada Inc, Avir Pharma, Pendopharm, Roche Pharma Canada, Sunovion Pharmaceuticals Canada Inc, and Teva Pharmaceutical Industries Ltd, outside the submitted work. T. M. reports no potential conflicts of interest. Both 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.008
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0290.015
Insufficient payload (model declined to judge)0.0070.002

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 head0.046
GPT teacher head0.356
Teacher spread0.310 · 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
GenreCommentary

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
Published2020
Admission routes1
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