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Enregistrement W4402512108 · doi:10.1097/io9.0000000000000103

Optimizing colorectal cancer screening intervals using fecal hemoglobin concentration: a personalized approach

2024· article· en· W4402512108 sur OpenAlexaff
Hamza Sajjad, Amogh Verma, Mahalaqua Nazli Khatib, Quazi Syed Zahiruddin, Abhay Gaidhane, Rakesh Sharma, Sarvesh Rustagi, Mahendra Pratap Singh, Amanuel M. Tirukelem

Notice bibliographique

RevueInternational Journal of Surgery Open · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueColorectal Cancer Screening and Detection
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésMedicineColorectal cancerColorectal cancer screeningHemoglobinFecesFecal occult bloodInternal medicineOncologyColonoscopyCancerGastroenterology

Résumé

récupéré en direct d'OpenAlex

Dear Editor, Colorectal carcinoma (CRC) is a major cancer burden and efforts are being made to reduce its devastating effects globally. These efforts have focused on developing screening programs to identify precancerous lesions, such as adenomas and polyps, before they become neoplastic. One of the tools developed to improve colorectal cancer screening is fecal immunologic testing (FIT), a test based on fecal hemoglobin (f-Hb) concentration1. A direct relationship between f-Hb concentration and CRC has already been established in previous studies. Recently, this gradient relationship between f-Hb concentration and CRC has become a major topic of research for developing a screening program that is more personalized and based on individual risk levels2–4. A recent study showed strong evidence that screening intervals could be adjusted based on fecal Hb levels5. The data for this retrospective cohort study were obtained from a Taiwanese cancer screening program that used FIT and colonoscopy biennially. More than three million people with a mean age of 57.8 years participated in this screening program. This study aimed to develop guidelines for optimal screening intervals based on f-Hb levels. An increase in baseline f-Hb levels related to colorectal neoplasia and mortality was observed. The CRC incidence rate (per 1000 person-years) increased with f-Hb from 0.94 for undetected f-Hb to 10.25 for f-Hb ≥150 μg Hb/g5. Another finding was an increase in the incidence of advanced colorectal cancer with an increase in f-Hb. Through careful analysis of these data, participants were stratified into different groups based on f-Hb levels, and various screening intervals were developed. A reduction of 49 and 28% in FIT tests and colonoscopies, respectively, was attributed to the use of personalized f-Hb-based screening intervals compared with biennial screening. This study suggests that adjustment of screening intervals can be performed using f-Hb metrics. The findings of this study have a meaningful impact on clinical practice. First, a decrease in the number of colonoscopies among patients stratified as low-risk using f-Hb concentration can save them from unwanted adverse events such as bleeding and perforations. Additionally, it reduces psychological stress related to repeated testing, according to established screening recommendations. Second, by personalizing the screening interval by f-Hb levels at the individual level, the optimal allocation of healthcare resources can be achieved. Third, the principle of decreasing tests using a personalized screening method is also supported by the minimization of costs, which also diminishes the risk of over-detection among low-risk populations. The development of a precision screening interval based on f-Hb can also guide the judicious use of other more specific tests, such as colonoscopy. The study’s findings and large sample size provide compelling evidence of its credibility; however, some limitations must be considered. Although this method of precision screening intervals can be used in other populations, adjustments for f-Hb levels are required because of the differences in incidence and mortality rates among specific populations. This analysis did not include various individual characteristics such as BMI, family history, and smoking, which are also risk factors for CRC. The inclusion of these risk factors, along with f-Hb levels, can increase the precision of risk detection. Integration of these findings into clinical practice can be advantageous; however, research is required to determine ways to implement such methods in practice. Regulatory bodies should consider adding this method of precision individualized screening to reduce the testing burden among low-risk populations and accurately identify pathology before it reaches an advanced stage. The incorporation of such screening methods can improve the cost-effectiveness of CRC surveillance. In conclusion, this study demonstrated a relationship between f-Hb levels and CRC incidence and mortality. It also showed how f-Hb concentrations can be used to develop a precise screening interval program for different risk groups, thereby reducing the number of FIT tests and colonoscopies. Ethical approval Not applicable. Consent Not applicable. Sources of funding None. Author contribution H.S.: conceptualization, writing – original draft, and writing – review and editing; A.V.: validation, writing – original draft, and writing – review and editing; M.N.K., Q.S.Z., A.M.G., R.K.S., S.R., M.P.S., and A.M.T.: writing – original draft and writing – review and editing. All authors are accountable for all the aspects of this work. Conflicts of interest disclosure The authors declares no conflicts of interest. Research registration unique identifying number (UIN) Not applicable. Guarantor Hamza Sajjad. Data availability statement Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Provenance and peer review Not commissioned, externally peer-reviewed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,723
Score d'incertitude au seuil0,547

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,109
Tête enseignante GPT0,377
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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