An Individual Participant Data Meta-Analysis of 13 Randomized Trials to Evaluate the Impact of Prophylactic Use of Heparin in Oncological Patients
Bibliographic record
Abstract
Abstract Background: Parenteral anticoagulants may improve outcomes in patients with cancer by reducing the risk of venous thromboembolism (VTE) and through a direct anti-tumour effect. Study-level meta-analysis indicates a reduction in VTE and provide moderate certainty that a small survival benefit exists; it is unclear if patients with specific cancers benefit more or less. Utilizing data from randomized controlled trials (RCT), this individual participant data meta-analysis examines the impact of heparin on survival, VTE and major bleeding in oncological patients randomized to low-molecular weight heparin (LMWH) or no LMWH. Methods: We performed a comprehensive systematic search for all RCTS (last search date March 2017) and contacted authors and sponsors to obtain individual participant data of patients with solid cancers and no other indication for prophylactic or therapeutic anticoagulation. We utilized the GRADE approach to evaluate the certainty of evidence and produce an evidence profile. All analyses followed the intention-to-treat principle. We calculated the impact on mortality through multivariable hierarchical models with patient-level variables as fixed effects and a categorical trial variable as a random effect. We adjusted the analysis for age, cancer type and metastasis status. To investigate whether intervention effects vary by predefined subgroups, including type of cancer, we tested interaction terms in the statistical model. Results: A total of 18 RCTs (n=10,041 participants) were eligible for inclusion and we obtained data from 82.4% of the participants (13 RCTs, n=8,278; n=4,139 for LMWH and n=4,139 for no LMWH). The meta-analysis revealed an adjusted relative risk of mortality within one year of 0.99 (95% CI: 0.95, 1.03) and a hazard ratio of 0.97 (95% CI: 0.82, 1.14) after one year. The relative risk for VTE was 0.58 (95% CI: 0.48, 0.71), 0.57 (95% CI: 0.44, 0.74) for symptomatic deep vein thrombosis and 0.58 (95%CI: 0.44, 0.77) for symptomatic pulmonary embolism, separately. For every 1,000 patients treated, approximately 16 fewer would experience symptomatic DVT and 16 fewer would experience any PE. The adjusted relative risk for major bleeding throughout trial duration was 1.24 (95% CI: 0.91, 1.69; P=0.17). Subgroup analysis, by cancer type, of VTE occurrence throughout trial duration identified lung cancer OR=0.52 (95% CI: 0.39, 0.68; P<0.001) and pancreatic cancer OR=0.55 (95% CI: 0.35, 0.88; P=0.01) patients as experiencing the greatest benefit from LMWH treatment. The certainty of the evidence for the outcomes was moderate to high. Conclusion: LMWH reduces risk of VTE without increasing risk of bleeding but does not improve survival across all patients. Funding: Canadian Institutes of Health Research knowledge synthesis grant, KRS 126594 Registration: International Prospective Register for Systematic Reviews (PROSPERO), CRD42013003526. Disclosures Schünemann: Canadian Institutes of Health Research: Research Funding. Crowther: Alexion: Speakers Bureau; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Boehringer Ingelheim: Speakers Bureau; Leo Pharma: Research Funding; Pfizer: Honoraria; Portola: Consultancy; Shinogi: Consultancy. Macbeth: Pfizer: Other: Provision of Dalteparin for FRAGMATIC trial; Cancer Research UK: Research Funding. Griffiths: Pfizer: Consultancy, Other: Comment: I run an academic clinical trials unit, have received educational/investigator intiated research grants from companies that make these heparin agents. As consultant > 3 years ago, advised Pfizer on clinical trial designs unrelated to this study., Research Funding. Van Es: Pfizer: Employment, Other: Comment: Dr. van Es reports personal fees from Pfizer as a member of their advisory board. These fees are unrelated to this project.. Streiff: Roche: Research Funding; Portola: Research Funding; Janssen Scientific Affairs, LLC: Consultancy, Research Funding; CSL Behring: Consultancy, Research Funding. Ageno: BMS-Pfizer: Consultancy, Honoraria; Bayer AG: Consultancy, Honoraria, Research Funding; Boehringer Ingelheim: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria. Bozas: PharmaMar: Honoraria. McBane: Bristol Myers Squibb: Other: Research grant for cancer associated VTE. Maraveyas: Bayer: Other: Personal fees and conference attendance; Bristol-Myers Squibb: Other: Grants and personal fees; Leo Pharma: Other: Grants, personal fees and conference attendance; Pfizer: Other: Personal fees. Loprinzi: Bristol Myers: Other: Grant - unrelated to this project; Janssen: Other: Grant - unrelated to this project.
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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 | 0.033 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".