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Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study

Overview of attention for article published in BMC Medicine, November 2016
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Mentioned by

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2 X users
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3 patents

Citations

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70 Dimensions

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69 Mendeley
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Title
Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study
Published in
BMC Medicine, November 2016
DOI 10.1186/s12916-016-0739-7
Pubmed ID
Authors

Joseph Jacob, Brian J. Bartholmai, Srinivasan Rajagopalan, Anne Laure Brun, Ryoko Egashira, Ronald Karwoski, Maria Kokosi, Athol U. Wells, David M. Hansell

Abstract

To evaluate computer-based computer tomography (CT) analysis (CALIPER) against visual CT scoring and pulmonary function tests (PFTs) when predicting mortality in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). To identify outcome differences between distinct CTD-ILD groups derived following automated stratification of CALIPER variables. A total of 203 consecutive patients with assorted CTD-ILDs had CT parenchymal patterns evaluated by CALIPER and visual CT scoring: honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume, emphysema, and traction bronchiectasis. CT scores were evaluated against pulmonary function tests: forced vital capacity, diffusing capacity for carbon monoxide, carbon monoxide transfer coefficient, and composite physiologic index for mortality analysis. Automated stratification of CALIPER-CT variables was evaluated in place of and alongside forced vital capacity and diffusing capacity for carbon monoxide in the ILD gender, age physiology (ILD-GAP) model using receiver operating characteristic curve analysis. Cox regression analyses identified four independent predictors of mortality: patient age (P < 0.0001), smoking history (P = 0.0003), carbon monoxide transfer coefficient (P = 0.003), and pulmonary vessel volume (P < 0.0001). Automated stratification of CALIPER variables identified three morphologically distinct groups which were stronger predictors of mortality than all CT and functional indices. The Stratified-CT model substituted automated stratified groups for functional indices in the ILD-GAP model and maintained model strength (area under curve (AUC) = 0.74, P < 0.0001), ILD-GAP (AUC = 0.72, P < 0.0001). Combining automated stratified groups with the ILD-GAP model (stratified CT-GAP model) strengthened predictions of 1- and 2-year mortality: ILD-GAP (AUC = 0.87 and 0.86, respectively); stratified CT-GAP (AUC = 0.89 and 0.88, respectively). CALIPER-derived pulmonary vessel volume is an independent predictor of mortality across all CTD-ILD patients. Furthermore, automated stratification of CALIPER CT variables represents a novel method of prognostication at least as robust as PFTs in CTD-ILD patients.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 22%
Other 8 12%
Student > Postgraduate 6 9%
Student > Bachelor 5 7%
Student > Master 5 7%
Other 14 20%
Unknown 16 23%
Readers by discipline Count As %
Medicine and Dentistry 35 51%
Agricultural and Biological Sciences 4 6%
Engineering 3 4%
Computer Science 3 4%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 2 3%
Unknown 20 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 September 2021.
All research outputs
#6,986,040
of 22,903,988 outputs
Outputs from BMC Medicine
#2,519
of 3,443 outputs
Outputs of similar age
#126,912
of 415,120 outputs
Outputs of similar age from BMC Medicine
#46
of 68 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 3,443 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 415,120 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 68% of its contemporaries.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.