Impact of Pre-Existing Cardiovascular Disease on Treatment Patterns and Survival Outcomes in Patients With Lung Cancer
Bibliographic record
Abstract
Abstract Background: Baseline cardiovascular disease can impact the patterns of treatment and hence the outcomes of patients with lung cancer. This study aimed to characterize treatment trends and survival outcomes of patients with pre-existing cardiovascular disease prior to their diagnosis of lung cancer.Methods: We conducted a retrospective, population-based cohort study of patients with lung cancer diagnosed from 2004 to 2015in a large Canadian province. Multivariable logistic regression and Cox regression models were constructed to determine the associations between cardiovascular disease and treatment patterns, and its impact on overall and cancer-specific survival, respectively.Results: A total of 20,689 patients with lung cancer were eligible for the current analysis. Men comprised 55%, and the median age at diagnosis was 70 years. One-third had at least one cardiovascular disease, with the most common being congestive heart failure in 15% of patients. Pre-existing cardiovascular disease was associated with a lower likelihood of receiving chemotherapy (odds ratio [OR],0.53;95% confidence interval [CI],0.48-0.58;P < .0001), radiotherapy (OR,0.76;95% CI,0.7-0.82;P < .0001), and surgery (OR, 056; 95% CI,0.44-0.7;P < .0001). Adjusting for measured confounders, the presence of pre-existing cardiovascular disease predicted for inferior OS (hazard ratio [HR], 1.1; 95% CI, 1.1-1.2; P < .0001) and CSS (HR,1.1; 95% CI, 1.1-1.1; P < .0001). Conclusions: Patients with lung cancer and pre-existing cardiovascular disease are less likely to receive any modality of cancer treatment and have poor OS and CSS. As effective therapies such as immuno-oncology drugs are introduced, early cardio-oncology consultation may optimize management and outcomes of lung cancer.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".