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Record W4402512108 · doi:10.1097/io9.0000000000000103

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

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

Bibliographic record

VenueInternational Journal of Surgery Open · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineColorectal cancerColorectal cancer screeningHemoglobinFecesFecal occult bloodInternal medicineOncologyColonoscopyCancerGastroenterology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.109
GPT teacher head0.377
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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