Impact of cardiac comorbidity on use and outcomes of adjuvant chemotherapy (ADJ) for colorectal cancer (CRC): A real-world population-based study.
Bibliographic record
Abstract
491 Background: Cardiac comorbidities such as myocardial infarction (MI) and congestive heart failure (CHF) may pose challenges in the treatment of CRC. As the population ages, cancer patients (pts) will be increasingly affected by cardiac comorbidities. We performed a population-based analysis of CRC to evaluate the prevalence of MI and CHF, use of ADJ, and survival outcomes. Methods: We evaluated 8601 pts diagnosed with resected stage 2 or 3 CRC from 2004 to 2015 in Alberta, Canada. Baseline patient, tumor, and treatment characteristics were compared between those with and without MI or CHF. Survival analysis was conducted using Kaplan-Meier methods and Cox regression models. Results: In total, 506 (5.9%) patients (pts) had MI and 440 (5.1%) had CHF. CRC patients with prior MI or CHF were older (median 76 and 79 years, respectively) and had worse Charlson Comorbidity Index (median CCI 2 for both) than those without cardiac comorbidities (median age 67 and CCI 0) (p < 0.001). Only 24% and 15% of pts with a MI or CHF history, respectively, received ADJ when compared to their counterparts (52% and 53%, respectively, p < 0.001). Among those who received ADJ (N = 3409), an oxaliplatin-based regimen was used in 26% of MI pts versus 42% of those without MI (p = 0.002), and in 31% of CHF pts versus 42% of those without CHF. Kaplan-Meier analysis revealed significantly worse overall survival (OS) in pts with prior MI (9.1 vs 4.3 years, p < 0.001) or CHF (9.2 vs. 2.7 years, p < 0.001) when compared to those without. However, cancer-specific survival (CSS) was not statistically different with or without MI (p = 0.348) and with or without CHF (p = 0.611). In Cox regression that adjusted for use of ADJ, MI was no longer a significant predictor of OS (HR = 1.01, 95% confidence interval (CI) 0.88-1.15), but CHF remained significant (HR 0.65, 95% CI 0.57-0.74). Neither MI nor CHF were predictors of CSS (HR 1.09, 95% CI 0.98-1.33, and HR 0.94, 95% CI 0.77-1.15). Conclusions: CRC pts with MI or CHF experienced lower use of ADJ and worse OS, but no difference in CSS was observed. ADJ-treated pts with prior MI appeared to benefit while worse outcomes in pts with prior CHF appear to be driven by non-cancer related causes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".