Improving lung cancer outcomes: the association of high blood eosinophil counts and poor surgical outcomes in early-stage lung cancer.
Bibliographic record
Abstract
IntroductionLung cancer remains the leading cause of cancer death worldwide and in Canada despite the advances in screening, diagnosis, and therapeutic options including minimally invasive surgery. The advanced median age at diagnosis and the common risk factors of smoking contribute to the high comorbidity burden among lung cancer patients, especially those with Chronic Obstructive Pulmonary Disease. Several risk stratification methods that make use of statistical models and clinical tests and measures have been developed but provide limited usefulness in selecting high-risk patients that would benefit from interventions perioperatively. Blood eosinophil counts have been used as a surrogate marker of airway inflammation, increased risk for exacerbations, and a prognostic measure of inhaled corticosteroid responsiveness. In lung cancer patients who often have some degree of airway inflammation and airway obstruction, blood eosinophil counts have not been explored as a marker of surgical outcomes in early disease.ObjectivesWe will determine the association of high blood eosinophil counts (BECs) with surgical outcomes among patients with early-stage lung cancer. We will determine the association between high BECs and 90-day healthcare utilization after lung resection. Additionally, we will examine the association between high BECs and survival at 1 year and 3 years follow-up. MethodsThis is a retrospective cohort study of lung cancer cases treated at the McGill University Health Centre between September 2017 and July 2021. Data was obtained from the MUHC Data Warehouse, linked to Pulmonary Function data, and then complemented by rigorous chart reviews. Inclusion criteria were all stage I and II non-small cell lung cancers treated by lobectomy, segmentectomy, or wedge resection. Excluded were stage III and IV cases, and any bi-lobectomies or pneumonectomies. The primary outcome was 90-day post-surgical readmission. Secondary outcomes were 1 and 3-year survival, prolonged hospital stay, and 90-day mortality. Descriptive statistics were done and log Poisson regression with standard variance was employed as the primary method of analysis with risk ratios reported. Cox proportional hazard regression was used for survival analysis. Blood eosinophil counts were explored as a continuous variable using natural cubic spines and as a categorical variable of <200 cells/µL and greater than or equal to 200 cells/µL (high blood eosinophil group).ResultsOur cohort (n=715) was primarily female (58%), with a mean age of 67.9 ± 7.9, with COPD comorbidity present in 20%. Unscheduled readmissions within 90 days following surgery occurred in 110(15.3%) cases. Readmission was higher in the group with higher BECs,19.7% (n=28/146) compared to 14.4% (n=82/569). BECs of more than 200 cells/ µL were associated with 1.5 times the risk of readmission (RR 1.54; 95% CI, 1.04-2.28) after adjustment for age, sex, COPD status, smoking, Charlson comorbidity index, surgical approach, TNM stage, white cell counts, hemoglobin level and creatinine concentration. At one year, those with high BECs had higher hazards of mortality (HR 2.42, 95% CI: 1.08-5.37), after adjustment for covariates. This was not observed at 3 years (HR 1.59, 95 CI: 0.90-2.81) but the other well-established factors associated with poor survival such as male sex, greater tumor size, and nodal involvement remained significant. ConclusionsOur study suggests that there is a role for blood eosinophil counts to be a useful biomarker for defining patients who are likely to be readmitted and those who have poor overall survival regardless of COPD status. This study provides a background for future clinical trials that can investigate perioperative interventions such as inhaled corticosteroid treatment among patients with established airway obstruction who have imminent lung cancer resection surgery
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".