Health and Economic Impact of Intensive Surveillance for Distant Recurrence After Curative Treatment of Colon Cancer: A Mathematical Modeling Study
Bibliographic record
Abstract
BACKGROUND: Intensive surveillance strategies are currently recommended for patients after curative treatment of colon cancer, with the aim of secondary prevention of recurrence. Yet, intensive surveillance has not yielded improvements in overall patient survival compared with minimal follow-up, and more intensive surveillance may be costlier. OBJECTIVE: The purpose of this study was to estimate the quality-adjusted life-years, economic costs, and cost-effectiveness of various surveillance strategies after curative treatment of colon cancer. DESIGN: A Markov model was calibrated to reflect the natural history of colon cancer recurrence and used to estimate surveillance costs and outcomes. SETTINGS: This was a decision-analytic model. PATIENTS: Individuals entered the model at age 60 years after curative treatment for stage I, II, or III colon cancer. Other initial age groups were assessed in secondary analyses. MAIN OUTCOME MEASURES: We estimated the gains in quality-adjusted life-years achieved by early detection and treatment of recurrence, as well as the economic costs of surveillance under various strategies. RESULTS: Cost-effective strategies for patients with stage I colon cancer improved quality-adjusted life-expectancy by 0.02 to 0.06 quality-adjusted life-years at an incremental cost of $1702 to $13,019. For stage II, they improved quality-adjusted life expectancy by 0.03 to 0.09 quality-adjusted life-years at a cost of $2300 to $14,363. For stage III, they improved quality-adjusted life expectancy by 0.03 to 0.17 quality-adjusted life-years for a cost of $1416 to $17,631. At a commonly cited willingness-to-pay threshold of $100,000 per quality-adjusted life-year, the most cost-effective strategy for patients with a history of stage I or II colon cancer was liver ultrasound and chest x-ray annually. For those with a history of stage III colon cancer, the optimal strategy was liver ultrasound and chest x-ray every 6 months with CEA measurement every 6 months. LIMITATIONS: The study was limited by model structure assumptions and uncertainty around the values of the model's parameters. CONCLUSIONS: Given currently available data and within the limitations of a model-based decision-analytic approach, the effectiveness of routine intensive surveillance for patients after treatment of colon cancer appears, on average, to be small. Compared with testing using lower cost imaging, currently recommended strategies are associated with cost-effectiveness ratios that indicate low value according to well-accepted willingness-to-pay thresholds in the United States. See Video Abstract at http://links.lww.com/DCR/A921.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".