Impact of Comorbidity Burden on Clinical Outcomes in Older Adults With Metastatic Colorectal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Older adults represent most patients with metastatic colorectal cancer (mCRC), yet their management is often complicated by multimorbidity. Comorbid conditions may influence treatment selection, tolerance, and survival, but the prognostic role of comorbidity burden in mCRC remains unclear. We conducted a systematic review and meta-analysis to assess the association between comorbidity, measured by the Charlson Comorbidity Index (CCI), and clinical outcomes in older patients with mCRC. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we systematically searched PubMed, Embase, and Web of Science (2000 to April 2025) for studies of adults aged ≥65 years with mCRC that assessed clinical outcomes according to baseline comorbidity, as measured by the CCI. Eligible endpoints included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Data were extracted in duplicate, and study quality was appraised using the Newcastle-Ottawa Scale. Random-effects models were applied to pool hazard ratios (HRs). Fourteen studies involving 16,736 patients met the inclusion criteria. Thirteen studies reported OS, two reported PFS, and two reported AEs. Higher comorbidity burden was significantly associated with worse OS (pooled HR = 1.19, 95% CI: 1.02-1.39; I² = 84.1%). No significant difference was observed for PFS (pooled HR = 1.00, 95% CI: 0.89-1.14; I² = 4.9%). For AEs, estimates were imprecise, with wide confidence intervals suggesting uncertainty about the association between high CCI and increased risk (pooled HR = 1.73, 95% CI: 0.79-3.79; I² = 86.4%). Multimorbidity, as measured by the CCI, is modestly associated with poorer overall survival in older mCRC patients (HR ≈ 1.19), but does not appear to influence progression-free survival or treatment-related toxicity consistently. Given the substantial heterogeneity across studies and the limited data on progression-free survival and adverse events, these findings should be interpreted with caution. Nevertheless, they suggest that comorbidity should guide, but not preclude, standard therapy, underscoring the importance of individualized, non-ageist treatment strategies.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".