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Development and external validation of a faecal immunochemical test-based prediction model for colorectal cancer detection in symptomatic patients

Overview of attention for article published in BMC Medicine, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 blog
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9 X users

Citations

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59 Dimensions

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Title
Development and external validation of a faecal immunochemical test-based prediction model for colorectal cancer detection in symptomatic patients
Published in
BMC Medicine, August 2016
DOI 10.1186/s12916-016-0668-5
Pubmed ID
Authors

Joaquín Cubiella, Pablo Vega, María Salve, Marta Díaz-Ondina, Maria Teresa Alves, Enrique Quintero, Victoria Álvarez-Sánchez, Fernando Fernández-Bañares, Jaume Boadas, Rafel Campo, Luis Bujanda, Joan Clofent, Ángel Ferrandez, Leyanira Torrealba, Virginia Piñol, Daniel Rodríguez-Alcalde, Vicent Hernández, Javier Fernández-Seara, on behalf of the COLONPREDICT study investigators

Abstract

Risk prediction models for colorectal cancer (CRC) detection in symptomatic patients based on available biomarkers may improve CRC diagnosis. Our aim was to develop, compare with the NICE referral criteria and externally validate a CRC prediction model, COLONPREDICT, based on clinical and laboratory variables. This prospective cross-sectional study included consecutive patients with gastrointestinal symptoms referred for colonoscopy between March 2012 and September 2013 in a derivation cohort and between March 2014 and March 2015 in a validation cohort. In the derivation cohort, we assessed symptoms and the NICE referral criteria, and determined levels of faecal haemoglobin and calprotectin, blood haemoglobin, and serum carcinoembryonic antigen before performing an anorectal examination and a colonoscopy. A multivariate logistic regression analysis was used to develop the model with diagnostic accuracy with CRC detection as the main outcome. We included 1572 patients in the derivation cohort and 1481 in the validation cohorts, with a 13.6 % and 9.1 % CRC prevalence respectively. The final prediction model included 11 variables: age (years) (odds ratio [OR] 1.04, 95 % confidence interval [CI] 1.02-1.06), male gender (OR 2.2, 95 % CI 1.5-3.4), faecal haemoglobin ≥20 μg/g (OR 17.0, 95 % CI 10.0-28.6), blood haemoglobin <10 g/dL (OR 4.8, 95 % CI 2.2-10.3), blood haemoglobin 10-12 g/dL (OR 1.8, 95 % CI 1.1-3.0), carcinoembryonic antigen ≥3 ng/mL (OR 4.5, 95 % CI 3.0-6.8), acetylsalicylic acid treatment (OR 0.4, 95 % CI 0.2-0.7), previous colonoscopy (OR 0.1, 95 % CI 0.06-0.2), rectal mass (OR 14.8, 95 % CI 5.3-41.0), benign anorectal lesion (OR 0.3, 95 % CI 0.2-0.4), rectal bleeding (OR 2.2, 95 % CI 1.4-3.4) and change in bowel habit (OR 1.7, 95 % CI 1.1-2.5). The area under the curve (AUC) was 0.92 (95 % CI 0.91-0.94), higher than the NICE referral criteria (AUC 0.59, 95 % CI 0.55-0.63; p < 0.001). On the basis of the thresholds with 90 % (5.6) and 99 % (3.5) sensitivity, we divided the derivation cohort into three risk groups for CRC detection: high (30.9 % of the cohort, positive predictive value [PPV] 40.7 %, 95 % CI 36.7-45.9 %), intermediate (29.5 %, PPV 4.4 %, 95 % CI 2.8-6.8 %) and low (39.5 %, PPV 0.2 %, 95 % CI 0.0-1.1 %). The discriminatory ability was equivalent in the validation cohort (AUC 0.92, 95 % CI 0.90-0.94; p = 0.7). COLONPREDICT is a highly accurate prediction model for CRC detection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 113 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 18%
Student > Bachelor 18 16%
Other 12 11%
Student > Ph. D. Student 12 11%
Librarian 5 4%
Other 26 23%
Unknown 20 18%
Readers by discipline Count As %
Medicine and Dentistry 61 54%
Nursing and Health Professions 5 4%
Unspecified 4 4%
Agricultural and Biological Sciences 3 3%
Computer Science 2 2%
Other 12 11%
Unknown 27 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 02 November 2021.
All research outputs
#2,538,387
of 24,138,997 outputs
Outputs from BMC Medicine
#1,631
of 3,684 outputs
Outputs of similar age
#44,096
of 343,141 outputs
Outputs of similar age from BMC Medicine
#26
of 49 outputs
Altmetric has tracked 24,138,997 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,684 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 45.4. This one has gotten more attention than average, scoring higher than 55% 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 343,141 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.