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Development of a Multivariate Prediction Model for Early-Onset Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome in Lung Transplantation

Overview of attention for article published in Frontiers in Medicine, July 2017
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Development of a Multivariate Prediction Model for Early-Onset Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome in Lung Transplantation
Published in
Frontiers in Medicine, July 2017
DOI 10.3389/fmed.2017.00109
Pubmed ID
Authors

Angela Koutsokera, Pierre J. Royer, Jean P. Antonietti, Andreas Fritz, Christian Benden, John D. Aubert, Adrien Tissot, Karine Botturi, Antoine Roux, Martine L. Reynaud-Gaubert, Romain Kessler, Claire Dromer, Sacha Mussot, Hervé Mal, Jean-François Mornex, Romain Guillemain, Christiane Knoop, Marcel Dahan, Paola M. Soccal, Johanna Claustre, Edouard Sage, Carine Gomez, Antoine Magnan, Christophe Pison, Laurent P. Nicod, The SysCLAD Consortium

Abstract

Chronic lung allograft dysfunction and its main phenotypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), are major causes of mortality after lung transplantation (LT). RAS and early-onset BOS, developing within 3 years after LT, are associated with particularly inferior clinical outcomes. Prediction models for early-onset BOS and RAS have not been previously described. LT recipients of the French and Swiss transplant cohorts were eligible for inclusion in the SysCLAD cohort if they were alive with at least 2 years of follow-up but less than 3 years, or if they died or were retransplanted at any time less than 3 years. These patients were assessed for early-onset BOS, RAS, or stable allograft function by an adjudication committee. Baseline characteristics, data on surgery, immunosuppression, and year-1 follow-up were collected. Prediction models for BOS and RAS were developed using multivariate logistic regression and multivariate multinomial analysis. Among patients fulfilling the eligibility criteria, we identified 149 stable, 51 BOS, and 30 RAS subjects. The best prediction model for early-onset BOS and RAS included the underlying diagnosis, induction treatment, immunosuppression, and year-1 class II donor-specific antibodies (DSAs). Within this model, class II DSAs were associated with BOS and RAS, whereas pre-LT diagnoses of interstitial lung disease and chronic obstructive pulmonary disease were associated with RAS. Although these findings need further validation, results indicate that specific baseline and year-1 parameters may serve as predictors of BOS or RAS by 3 years post-LT. Their identification may allow intervention or guide risk stratification, aiming for an individualized patient management approach.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Other 6 15%
Researcher 5 13%
Student > Ph. D. Student 5 13%
Student > Doctoral Student 2 5%
Student > Bachelor 2 5%
Other 5 13%
Unknown 14 36%
Readers by discipline Count As %
Medicine and Dentistry 16 41%
Immunology and Microbiology 2 5%
Unspecified 1 3%
Computer Science 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 2 5%
Unknown 16 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 October 2018.
All research outputs
#7,534,266
of 22,988,380 outputs
Outputs from Frontiers in Medicine
#1,738
of 5,751 outputs
Outputs of similar age
#108,866
of 283,559 outputs
Outputs of similar age from Frontiers in Medicine
#26
of 73 outputs
Altmetric has tracked 22,988,380 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,751 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 283,559 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.