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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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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 policy source
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Citations

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24 Dimensions

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128 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, for the 3CIA collaboration

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 13%
Researcher 15 12%
Student > Bachelor 12 9%
Other 10 8%
Professor 8 6%
Other 23 18%
Unknown 43 34%
Readers by discipline Count As %
Medicine and Dentistry 35 27%
Nursing and Health Professions 10 8%
Biochemistry, Genetics and Molecular Biology 5 4%
Psychology 5 4%
Economics, Econometrics and Finance 3 2%
Other 18 14%
Unknown 52 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 29 October 2019.
All research outputs
#5,695,029
of 22,953,506 outputs
Outputs from BMC Medicine
#2,263
of 3,448 outputs
Outputs of similar age
#99,852
of 331,187 outputs
Outputs of similar age from BMC Medicine
#33
of 46 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,448 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 34th percentile – i.e., 34% 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 331,187 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 69% of its contemporaries.
We're also able to compare this research output to 46 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.