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Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

Overview of attention for article published in BMC Medicine, March 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)

Mentioned by

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5 tweeters

Citations

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12 Dimensions

Readers on

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87 Mendeley
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Title
Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease
Published in
BMC Medicine, March 2018
DOI 10.1186/s12916-018-1013-y
Pubmed ID
Authors

Beniamino Guerra, Sarah R. Haile, Bernd Lamprecht, Ana S. Ramírez, Pablo Martinez-Camblor, Bernhard Kaiser, Inmaculada Alfageme, Pere Almagro, Ciro Casanova, Cristóbal Esteban-González, Juan J. Soler-Cataluña, Juan P. de-Torres, Marc Miravitlles, Bartolome R. Celli, Jose M. Marin, Gerben ter Riet, Patricia Sobradillo, Peter Lange, Judith Garcia-Aymerich, Josep M. Antó, Alice M. Turner, Meilan K. Han, Arnulf Langhammer, Linda Leivseth, Per Bakke, Ane Johannessen, Toru Oga, Borja Cosio, Julio Ancochea-Bermúdez, Andres Echazarreta, Nicolas Roche, Pierre-Régis Burgel, Don D. Sin, Joan B. Soriano, Milo A. Puhan

Abstract

External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD. We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores. Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO- AUCBODE= 0.015 [95% confidence interval (CI) = -0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated- AUCBODE= 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency. Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 16%
Student > Bachelor 11 13%
Researcher 11 13%
Professor 8 9%
Other 7 8%
Other 17 20%
Unknown 19 22%
Readers by discipline Count As %
Medicine and Dentistry 30 34%
Nursing and Health Professions 9 10%
Psychology 4 5%
Biochemistry, Genetics and Molecular Biology 2 2%
Chemistry 2 2%
Other 11 13%
Unknown 29 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 June 2018.
All research outputs
#7,437,275
of 13,791,430 outputs
Outputs from BMC Medicine
#1,816
of 2,171 outputs
Outputs of similar age
#124,385
of 272,347 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 13,791,430 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 2,171 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.3. This one is in the 15th percentile – i.e., 15% 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 272,347 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 52% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them