One-Time Fecal Immunochemical Screening for Advanced Colorectal Neoplasia in Patients with CKD (DETECT Study)
Bibliographic record
Abstract
Significance Statement Colorectal cancer screening using fecal immunochemical testing (FIT) is recommended for patients with CKD, whose risk of developing and dying from this malignancy is at least 1.5 times higher than that of the sex- and age-matched general population. However, FIT accuracy in this setting is unknown and is likely to be affected by occult gastrointestinal bleeding from dysfunctional platelets and increased bleeding sensitivity to aspirin. In a large, multinational study, the authors found that FIT appears to be an accurate screening test for patients with CKD, but the risk of major complications from work-up colonoscopies (1.5%) is high compared with this risk in the general population. These findings provide useful estimates of harms and test accuracies to inform colorectal cancer screening decisions across the full spectrum of CKD. Background In patients with CKD, the risk of developing colorectal cancer is high and outcomes are poor. Screening using fecal immunochemical testing (FIT) is effective in reducing mortality from colorectal cancer, but performance characteristics of FIT in CKD are unknown. Methods To determine the detection rates and performance characteristics of FIT for advanced colorectal neoplasia (ACN) in patients with CKD, we used FIT to prospectively screen patients aged 35–74 years with CKD (stages 3–5 CKD, dialysis, and renal transplant) from 11 sites in Australia, New Zealand, Canada, and Spain. All participants received clinical follow-up at 2 years. We used a two-step reference standard approach to estimate disease status. Results Overall, 369 out of 1706 patients who completed FIT (21.6%) tested positive; 323 (87.5%) underwent colonoscopies. A total of 1553 (91.0%) completed follow-up; 82 (4.8%) had died and 71 (4.2%) were lost. The detection rate of ACN using FIT was 6.0% (5.6%, 7.4%, and 5.6% for stages 3–5 CKD, dialysis, and transplant). Sensitivity, specificity, and positive and negative predictive values of FIT for ACN were 0.90, 0.83, 0.30, and 0.99, respectively. Of participants who underwent colonoscopy, five (1.5%) experienced major colonoscopy-related complications, including bowel perforation and major bleeding. Conclusions FIT appears to be an accurate screening test for patients with CKD, such that a negative test may rule out the diagnosis of colorectal cancer within 2 years. However, the risk of major complications from work-up colonoscopy are at least ten-fold higher than in the general population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".