Exploring the Association Between Cirrhosis and Outcomes Among Patients with Lung Cancer in Ontario Between 2007 and 2017: A Population-Based Study
Bibliographic record
Abstract
Background: Cirrhosis is a significant cause of morbidity and mortality, but its impact on outcomes in the treatment of lung cancer is not well described. Population-based data provide estimates where prospective data is lacking and may aid in the quantification of risk for patients undergoing radical and palliative treatment. Methods: All patients diagnosed with non-small cell lung cancer (NSCLC) in Ontario, Canada, from 2007 to 2017 were identified using the provincial database ICES, and those with cirrhosis were identified using validated coding. The association between cirrhosis and morbidity and mortality, both postoperatively and post-palliative systemic anti-cancer therapy, was evaluated using logistic regression, Kaplan-Meier curves, and competing risks analysis. Results: Among patients with stages I-III NSCLC who underwent lung resection (n=59,226 overall, n=1,780 (3%) with cirrhosis), patients with cirrhosis had a higher overall mortality rate at both 30 days (5% vs. 2%, p<0.001) and 90 days (8% vs. 3%, p<0.001). More patients with cirrhosis were admitted to the ICU, readmitted to the hospital within both 30 and 90 days, and stayed in the hospital longer. After multivariable logistic regression, cirrhosis was associated with death at 30 days (OR 2.35, 95% CI 1.39-3.99, p<0.001) and 90 days (OR 2.10, 95% CI 1.38-3.21, p<0.001); development of postoperative complications within 90 days (HR 1.44, 95% CI 1.14-1.81, p=0.002); and hospital readmission at both 30 (OR 1.90, 95% CI 1.41-2.56, p<0.001) and 90 days (OR 1.63, 95% CI 1.27-2.10, p=0.001). Using adjusted Cox regression among those with stage IV NSCLC, cirrhosis was associated with worse overall survival (HR 1.10, 95% CI 1.02-1.18, p=0.016). However, using competing risks analysis with liver-related morality as a competing event, there was no association between cirrhosis and cancer-specific mortality (sHR 1.04, 95% CI 0.95-1.13, p=0.395). Conclusions: Cirrhosis is associated with increased morbidity and mortality in patients with stages I-III NSCLC undergoing surgery. In stage IV NSCLC, patients with cirrhosis are less likely to receive systemic palliative treatment than their counterparts, and may experience more post-treatment complications, but are not at a significantly increased risk of death when the competing risk of liver death is accounted for.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".