Global Prospective Cohort Study of Factors Associated with Venous Thromboembolism in Patients with Lung Cancer Receiving Cancer Therapy
Bibliographic record
Abstract
Abstract BACKGROUND: Patients with lung cancer are known to be at increased risk for venous thromboembolism (VTE). However, there have been few studies of risk factors for VTE in lung cancer patients undergoing systemic chemotherapy. METHODS: CANTARISK was a prospective, non-interventional, global cohort study including patients with lung cancer initiating a new chemotherapy regimen. Clinical data were collected at baseline and at 2, 4 and 6 months follow-up. The impact of patient-, disease- and treatment-related factors on the occurrence of VTE in the first 6 months was evaluated in univariable and multivariable Cox regression analyses. RESULTS: A total of 1,980 patients with lung cancer were enrolled from 2011-12 of which 84% were diagnosed with non-small cell lung cancer (NSCLC). Median age was 63 years (range, 25-91) and 63% were male while 82% were active or former smokers. Race was white (70%), Asian (22%), or black (4%) with similar numbers from North America, Europe, and other regions including Asia. Metastatic disease was reported in 70% and ECOG PS was ≥2 in 13%. During the first six months, 121 patients developed a VTE (6.1%), of which 47.1% had pulmonary embolism (PE), 45.5% deep venous thrombosis (DVT), 3.3% catheter-associated thrombosis, and 4.1% visceral thrombosis. Among significant factors in univariable analysis, independent predictors for VTE in multivariable Cox regression analysis included female gender, US geographic region, leg immobilization, and presence of a central venous catheter (Table) with a trend toward greater risk for higher grade histology. Although predictive of early all-cause mortality in this study population (Kuderer et al ASCO 2016), the previously validated risk score for VTE in ambulatory cancer patients (Khorana et al: Blood 2008) was not significantly associated with VTE in either univariable or multivariable analysis. CONCLUSIONS: Several demographic, geographic, and clinical factors are significantly associated with an increased risk of VTE in patients with lung cancer receiving systemic chemotherapy. Future analysis will attempt to assess how novel targeted treatment options might impact the Khorana score's predictive ability across all lung cancer patients. Disclosures Kuderer: Janssen Scientific Affairs, LLC: Consultancy, Honoraria. Lyman:Amgen: Research Funding. Khorana:Bayer: Consultancy, Honoraria; Leo: Consultancy, Honoraria, Research Funding; Halozyme: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Janssen Scientific Affairs, LLC: Consultancy, Honoraria, Research Funding.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".