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Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia

Overview of attention for article published in BMC Pulmonary Medicine, November 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#18 of 1,950)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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Title
Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia
Published in
BMC Pulmonary Medicine, November 2017
DOI 10.1186/s12890-017-0485-4
Pubmed ID
Authors

Yoonha Choi, Jiayi Lu, Zhanzhi Hu, Daniel G. Pankratz, Huimin Jiang, Manqiu Cao, Cristina Marchisano, Jennifer Huiras, Grazyna Fedorowicz, Mei G. Wong, Jessica R. Anderson, Edward Y. Tom, Joshua Babiarz, Urooj Imtiaz, Neil M. Barth, P. Sean Walsh, Giulia C. Kennedy, Jing Huang

Abstract

Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 24%
Other 7 13%
Student > Bachelor 6 11%
Student > Master 6 11%
Student > Ph. D. Student 3 5%
Other 4 7%
Unknown 16 29%
Readers by discipline Count As %
Medicine and Dentistry 23 42%
Biochemistry, Genetics and Molecular Biology 6 11%
Computer Science 2 4%
Agricultural and Biological Sciences 1 2%
Economics, Econometrics and Finance 1 2%
Other 3 5%
Unknown 19 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 56. 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 14 July 2018.
All research outputs
#647,111
of 23,008,860 outputs
Outputs from BMC Pulmonary Medicine
#18
of 1,950 outputs
Outputs of similar age
#15,883
of 431,651 outputs
Outputs of similar age from BMC Pulmonary Medicine
#1
of 82 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,950 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 99% 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 431,651 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.