Determining incidence and predictors of deep vein thrombosis in patients with non-small cell lung cancer
Bibliographic record
Abstract
7159 Background: The risk of deep vein thrombosis (DVT) among patients with non-small cell lung cancer (NSCLC) has not been well studied. We conducted a retrospective cohort study of patients with NSCLC to determine the incidence of DVT and characterize predictors of DVT in NSCLC patients. Methods: The pulmonary oncology database of the SMBD-Jewish General Hospital contains prospectively collected clinical data on all lung cancer patients followed in the pulmonary oncology clinic since January 1, 1997. We identified all consecutive patients with a histologically confirmed new diagnosis of NSCLC between January 1, 1997 and December 31, 2004 and determined the occurrence of an objectively defined DVT. Data on age, gender, NSCLC type and stage, Eastern Cooperative Oncology Group (ECOG) performance status, exposure to surgery and chemotherapy, and death was collected and compared among patients with DVT and patients without DVT. Results: Of the 493 NSCLC patients included in the cohort for a total of 634 person-years, 67 (13.6%) patients developed an objectively confirmed DVT. We calculated an incidence rate of 110 cases (95% confidence interval (CI) 80, 130) per 1000 person-years. Risk factors associated with occurrence of DVT were advanced stage (p = 0.0006) and male sex (p = 0.04). A multivariable regression analysis adjusted for recent surgery and performance status showed that advanced stage (Rate ratio 2.55, 95% CI 1.33–4.89) and male sex (Rate ratio 1.75, 95% CI 1.03–2.94) were independent predictors of DVT. Age, type of NSCLC, and chemotherapy did not predict DVT. The risk of dying was 1.7-fold increased (Hazard Ratio 1.73, 95% CI 1.29–2.32, adjusted for age, sex, stage, surgery, performance status, and date of lung cancer diagnosis) among patients with DVT compared to patients without DVT. Conclusions: Our results show a high incidence of DVT in NSCLC patients and that advanced stage and male sex are important predictors of DVT. Moreover, NSCLC patients with DVT have a 1.7 fold increased risk of dying than patients without DVT. Confirmation of our results by prospective studies may provide the necessary evidence for targeted use of prophylactic anticoagulants in NSCLC patients to prevent development of DVT and improve related survival. No significant financial relationships to disclose.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".