Evidence map›Paper›PMID 41013583›Full record

ReviewBMC medical informatics and decision making2025

Computational definition of medical exclusion and feasibility of excluding people not eligible for French population-based colorectal cancer screening from the French medico-administrative database.

Akoï Koïvogui, Christian Balamou, Robert Benamouzig, Catherine Duclos

Abstract readReview
In one paragraph

Review in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Akoï KoïvoguiLaboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, INSERM, Université Sorbonne Paris Nord, Bobigny, 93000, France. akoi.koivogui@inserm.fr.
Christian BalamouCRCDC-AURA, Site de l'Ain Bourg-en-Bresse, Bourg-en-Bresse, 01000, France.
Robert BenamouzigService d'Hépato Gastro Entérologie, AP-HP, Hôpital Avicenne, Bobigny, 93000, France.
Catherine DuclosLaboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, INSERM, Université Sorbonne Paris Nord, Bobigny, 93000, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the French population-based colorectal cancer screening program (CRCSP), the fact that the medical-exclusion rate was estimated only after a collection of voluntary statements from subjects could compromise an exhaustive collection of potential cases of medical-exclusion. The health insurance medico-administrative database (SNDS) that contains medical and healthcare consumption information have to date never been used to refine the target population of the CRCSP.

objectiveTo identify in the SNDS, from published and disparate algorithms, the computational definitions of morbid situations that could justify medical exclusion from the CRCSP.

methodsThe non-systematic review of the literature synthetised an exhaustive list of algorithms targeting in SNDS, the morbid situations (CCR, colorectal adenoma/polyp, chronic inflammatory bowel disease, familial adenomatous polyposis, or Lynch syndrome colonoscopy, coloscanner, polypectomy) which may justify temporary or permanent medical exclusion from the CRCSP campaigns. Secondly, the discovered codes of morbid situations were searched on statistical reports to estimate their frequencies of use in SNDS (in 2021), and their interest in the computational phenotypes' algorithm.

resultsThe analysis of the literature (28 articles/studies) highlights the existence of diagnostic or therapeutic codes that can define in the SNDS database, the morbid situations justifying medical exclusion from the CRCSP. Except for personal or family history of CRC classifiable in the Z85.0 or Z80.0 codes of the ICD-10, almost all the morbid situations have a requestable definition in the SNDS. The target favoured by the search algorithms was the ICD-10 code (i.e., C18-C20, K50, K51). The definition codes listed were frequently used in SNDS in 2021, except for a few codes (D12.6 + 6, M07.5). From this definition of morbid situations by the only codes of the ICD-10 or the procedure codes emerges a feasibility and a decision-making algorithm for the choice of the person to be excluded from CRCSP campaign, using the SNDS. Age is the first discriminating variable in this decision-making algorithm because the CRCSP targeted people aged 50 to 74 years old and a restriction on age was made in several included SNDS's studies. The second discrimination based on diagnostic evidence derives its relevance from the quasi-systematic search for ICD-10 diagnostic codes in SNDS's studies.

conclusionIn addition to being widely used in the context of medico-economic and epidemiological studies, the SNDS currently contains almost all the data essential for estimating the rate of medical-exclusion during colorectal cancer screening campaigns. While initiating the answer to the question of the choice of the most appropriate algorithm in each context, this review of the literature also emphasizes the need for validation studies because the quality of the algorithms used conditions the quality of the studies carried out in the medico-administrative databases.

Indexed as

Colorectal NeoplasmsDatabases, FactualEarly Detection of CancerPatient SelectionAlgorithmsFeasibility StudiesFranceHumansColorectal cancer screeningColorectal disease phenotypeMedical exclusion rateMedico-administrative databaseParticipation rate

Identifiers

PMID41013583
PMCPMC12465781

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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.