The Association Between Gastroesophageal Reflux Disease and Non-Small Cell Lung Cancer: A Retrospective Case-Control Study
Bibliographic record
Abstract
Background: Lung cancer is a leading cause of mortality in the USA. Non-small cell lung cancer (NSCLC) contributes to 85% of all lung cancers. It is the most prevalent subtype amongst non-smokers, and its incidence has risen in the last 20 years. In addition, gastroesophageal reflux disease (GERD) has been associated with several lung pathologies, namely idiopathic pulmonary fibrosis and asthma. We aimed to investigate the association between GERD and NSCLC by performing a retrospective, multicenter, case-control study. This is the first study of this nature to be carried out in the USA. Methods: Data were retrieved from 17 Northwell health care facilities in the New York area between the years 2010 and 2018. Inclusion criteria were patients > 18 years of age with NSCLC (large cell, adenocarcinoma, and squamous cell). They were appropriately matched with controls based on age, gender, weight, comorbidities, and medication use. Our exposure group had a diagnosis of GERD based on the International Classification of Diseases, Ninth/10th Revision (ICD 9/10) codes and endoscopic, in addition to histological evidence if present. We excluded patients with secondary lung cancers, esophageal adenocarcinoma, other primary malignancies, Barrett's esophagus, and smokers. Logistic regression was conducted to determine the adjusted odds ratio (OR) and corresponding 95% confidence interval (CI) for the association between NSCLC and GERD. Results: A total of 1,083 subjects were included in our study: 543 (50%) patients were diagnosed with NSCLC. In this population, GERD was twice as prevalent compared to controls (20.4% vs. 11.6%, P < 0.001). Multivariate analysis demonstrated that GERD was associated with a higher risk of NSCLC compared to matched controls (OR = 1.86, 95% CI = 1.26 - 2.73). In addition, GERD patients treated with either antihistamines or proton pump inhibitors did not demonstrate an overall reduced risk of NSCLC (OR = 1.01, 95% CI = 0.48 - 2.12). Conclusions: Our study demonstrates that GERD is associated with a higher risk of NSCLC, irrespective of GERD treatment. We postulate that GERD patients suffer from chronic micro-aspirations leading to a prolonged inflammatory state within the lung parenchyma, triggering specific proliferative signaling pathways that may lead to malignant transformation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".