ArticleDigestive diseases and sciences2024
Development and Validation of a Colorectal Cancer Prediction Model: A Nationwide Cohort-Based Study.
Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Cancer risk perception among family members of patients with digestive tract cancer and associated patient- and family member-related factors: A cross-sectional study of 414 patient-family member pairs.Asia-Pacific journal of oncology nursing · 2026Article
- Mobile AI-Assisted Oral Lesion Triage for Community Oral Cancer Screening: Prospective Field Evaluation Study.JMIR mHealth and uHealth · 2026Article
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of colorectal cancer (CRC) is critical to increasing survival rates. Computerized risk prediction models hold great promise for identifying individuals at high risk for CRC. In order to utilize such models effectively in a population-wide screening setting, development and validation should be based on cohorts that are similar to the target population.
aimEstablish a risk prediction model for CRC diagnosis based on electronic health records (EHR) from subjects eligible for CRC screening.
methodsA retrospective cohort study utilizing the EHR data of Clalit Health Services (CHS). The study includes CHS members aged 50-74 who were eligible for CRC screening from January 2013 to January 2019. The model was trained to predict receiving a CRC diagnosis within 2 years of the index date. Approximately 20,000 EHR demographic and clinical features were considered.
resultsThe study includes 2935 subjects with CRC diagnosis, and 1,133,457 subjects without CRC diagnosis. Incidence values of CRC among subjects in the top 1% risk scores were higher than baseline (2.3% vs 0.3%; lift 8.38; P value < 0.001). Cumulative event probabilities increased with higher model scores. Model-based risk stratification among subjects with a positive FOBT, identified subjects with more than twice the risk for CRC compared to FOBT alone.
conclusionsWe developed an individualized risk prediction model for CRC that can be utilized as a complementary decision support tool for healthcare providers to precisely identify subjects at high risk for CRC and refer them for confirmatory testing.
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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.