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Information maximizing component analysis of left ventricular remodeling due to myocardial infarction

Overview of attention for article published in Journal of Translational Medicine, November 2015
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
Information maximizing component analysis of left ventricular remodeling due to myocardial infarction
Published in
Journal of Translational Medicine, November 2015
DOI 10.1186/s12967-015-0709-4
Pubmed ID
Authors

Xingyu Zhang, Bharath Ambale-Venkatesh, David A. Bluemke, Brett R. Cowan, J. Paul Finn, Alan H. Kadish, Daniel C. Lee, Joao A. C. Lima, William G. Hundley, Avan Suinesiaputra, Alistair A. Young, Pau Medrano-Gracia

Abstract

Although adverse left ventricular shape changes (remodeling) after myocardial infarction (MI) are predictive of morbidity and mortality, current clinical assessment is limited to simple mass and volume measures, or dimension ratios such as length to width ratio. We hypothesized that information maximizing component analysis (IMCA), a supervised feature extraction method, can provide more efficient and sensitive indices of overall remodeling. IMCA was compared to linear discriminant analysis (LDA), both supervised methods, to extract the most discriminatory global shape changes associated with remodeling after MI. Finite element shape models from 300 patients with myocardial infarction from the DETERMINE study (age 31-86, mean age 63, 20 % women) were compared with 1991 asymptomatic cases from the MESA study (age 44-84, mean age 62, 52 % women) available from the Cardiac Atlas Project. IMCA and LDA were each used to identify a single mode of global remodeling best discriminating the two groups. Logistic regression was employed to determine the association between the remodeling index and MI. Goodness-of-fit results were compared against a baseline logistic model comprising standard clinical indices. A single IMCA mode simultaneously describing end-diastolic and end-systolic shapes achieved best results (lowest Deviance, Akaike information criterion and Bayesian information criterion, and the largest area under the receiver-operating-characteristic curve). This mode provided a continuous scale where remodeling can be quantified and visualized, showing that MI patients tend to present larger size and more spherical shape, more bulging of the apex, and thinner wall thickness. IMCA enables better characterization of global remodeling than LDA, and can be used to quantify progression of disease and the effect of treatment. These data and results are available from the Cardiac Atlas Project ( http://www.cardiacatlas.org ).

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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 %
France 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Bachelor 7 18%
Student > Ph. D. Student 6 15%
Other 3 8%
Professor > Associate Professor 2 5%
Other 3 8%
Unknown 9 23%
Readers by discipline Count As %
Engineering 12 31%
Computer Science 7 18%
Medicine and Dentistry 4 10%
Nursing and Health Professions 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 2 5%
Unknown 12 31%
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 05 November 2015.
All research outputs
#7,599,335
of 23,310,485 outputs
Outputs from Journal of Translational Medicine
#1,270
of 4,114 outputs
Outputs of similar age
#96,978
of 286,342 outputs
Outputs of similar age from Journal of Translational Medicine
#19
of 75 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 4,114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. 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 286,342 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 65% of its contemporaries.
We're also able to compare this research output to 75 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.