ArticleBMC medicine2016
Development and external validation of a faecal immunochemical test-based prediction model for colorectal cancer detection in symptomatic patients.
Article in BMC medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 40 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Retrospective Review of the Secondary-care Colorectal Cancer Pathway Within NHS Lothian: An Evaluation of 3 Years of Pathway Performance
Optimising Patient Pathways for Earlier Detection of Colorectal Cancer in Secondary Care: Implementation of Multiple FIT Testing
Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Quantitative faecal immunochemical tests to guide colorectal cancer pathway referral in primary care. A systematic review, meta-analysis and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2025Pooled it
- The role of faecal calprotectin in diagnosis and staging of colorectal neoplasia: a systematic review and meta-analysis.BMC gastroenterology · 2022Pooled it
- Pooled it
- Trial
- Development and external validation of a machine-learning risk model for colorectal cancer triage in Sweden's fast-track pathway.BMC medical informatics and decision making · 2026Article
- Article
- Article
- COLOFIT: Development and Internal-External Validation of Models Using Age, Sex, Faecal Immunochemical and Blood Tests to Optimise Diagnosis of Colorectal Cancer in Symptomatic Patients.Alimentary pharmacology & therapeutics · 2025Article
- Inflammatory Bowel Disease Mediates the Causal Relationship Between Gut Microbiota and Colorectal Cancer: Identification of Therapeutic Targets and Predictive Modeling.Journal of Cancer · 2025Article
- Colorectal Cancer: Risk Factors, Novel Approaches in Molecular Screening and Treatment.International journal of molecular and cellular medicine · 2025Review
- Faecal haemoglobin concentration and colorectal cancer site, stage and grade in a symptomatic cohort.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2024Article
- The performance of FIT-based and other risk prediction models for colorectal neoplasia in symptomatic patients: a systematic review.EClinicalMedicine · 2023Review
- Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis.Microorganisms · 2023Review
- Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms.Journal of personalized medicine · 2023Article
- Review
- A Novel Approach on the Use of Samples from Faecal Occult Blood Screening Kits for Metabolomics Analysis: Application in Colorectal Cancer Population.Metabolites · 2023Article
- Designing a Predictive Model for Colorectal Neoplasia Diagnosis Based on Clinical and Laboratory Findings in Colonoscopy Candidate Patients.Journal of gastrointestinal cancer · 2022Article
- Article
- A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients.Metabolites · 2022Article
- Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk 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.
methodsThis 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.
resultsWe 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).
conclusionsCOLONPREDICT is a highly accurate prediction model for CRC detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.