Use of Heparins in patients with Cancer: Individual patient-data meta-analysis of randomized trials

Proposal
657

Title of Proposed Research

Lead Researcher

Holger Schunemann, MD, MSc, PhD

Affiliation

McMaster University

Funding Source

Canadian Institutes of Health Research
Institution paid: McMaster University
Title: Use of Heparins in Cancer: Individual patient data meta-analysis
Primary Inst.: Cancer Research
Competition Outcome: Knowledge Synthesis Grant
Decision on your application: Approved
Predictors of Prostate Cancer Progression Among Men on Active Surveillance

Potential Conflicts of Interest

None

Data Sharing Agreement Date

22 July 2014

Lay Summary

Background: Solid cancers are a common health problem worldwide and cancer related complications such as deep venous thrombosis (DVT) and pulmonary embolism (PE) are a cause of mortality and morbidity. Parenteral anticoagulants, including heparin, low molecular weight heparins (LMWH), fondaparinux, and danaparoid, may improve outcomes in cancer patients not only by reducing the risk of venous thromboembolic disease but also through a direct antitumor effect. Study level systematic reviews on this topic indicate a reduction in venous thromboembolism and provide moderate confidence that a small survival benefit exists. In addition to the remaining uncertainty about the survival benefit, it remains uncertain which, if any, patient subgroups experience that potential benefit.

Methods: We will conduct a systematic review with an individual participant data (IPD) meta-analysis (MA). First, we will perform a systematic and comprehensive electronic search using various databases (Medline, EMBASE, Cochrane Library), hand search the abstracts of relevant scientific conferences and check registries of clinical trials. We will then perform an IPDMA to explore the magnitude of the suggested survival benefit and address whether or not specific subgroups and characteristics of cancer patients are more likely to benefit from parenteral anticoagulants. All analyses will follow the intention-to-treat principle. For our primary outcome, mortality, we will use multivariable hierarchical models with patient-level variables as fixed effects and a categorical trial variable as a random effect. We will adjust the analysis for important prognostic patient baseline characteristics such as age and comorbidities. To investigate whether or not intervention effects vary by pre-defined subgroups of patients, we will additionally test appropriate interaction terms in the statistical model. Furthermore, we will develop a risk prediction model for venous thromboembolism with a focus on patients in the control groups of the randomized trials.

Results and Discussion: We anticipate data from more than 9000 participants will contribute to this IPDMA. This study will provide important evidence for the individualized or targeted treatment of patients with cancer and will directly influence recommendations in clinical practice guidelines about the use of heparins in patients with solid cancers. Furthermore, we will work with the McMaster Health Forum to prepare an evidence brief and convene a stakeholder dialogue to engage policymakers and stakeholders in acting on the findings.

Study Data Provided

[{ "PostingID": 1415, "Title": "GSK-FRX106365", "Description": "A randomized, controlled trial to evaluate the effects of nadroparin on survival and disease progression in patients with advanced malignancies of the lung, pancreas, or prostate" }]

Statistical Analysis Plan

If possible we will express the results in pooled hazard ratios (HR) for dichotomous outcomes. We will also report relative risks (RR) and mean differences for continuous outcomes (e.g. quality of life), if available, together with corresponding confidence intervals (CI). We will also report the anticipated absolute effects (e.g. risk difference or NNT) based on the estimated RR and HR and three assumed baseline risks and median or mean difference in survival. All data will be summarized in a GRADE evidence profile.We plan to use SAS version 9.1 (Cary, North Carolina) or a newer version of this software to analyze data. All analyses will follow the intention-to-treat principle with all patients analyzed according to the arm to which they were randomized. A consistency check of the data from all trials will be performed. In particular, we will check variable ranges and look for unusual randomization patterns that suggest a violation of the integrity of randomization. In addition, to ensure that the data provided correspond to the reported results, we will reproduce and recalculate the primary outcomes of the trials and several randomly chosen baseline data.We will pool results from all trials using multi-level models (14-16). Specifically, we will be using a logistic mixed model with a random intervention effect. Since a considerable number of trials (>10) may be included, we plan to use a random-effects model, representing the joint distribution of the treatment effect by trial with a bivariate normal distribution (i.e. using model (3) from Turner 2000 (16)). The primary analysis will be adjusted for comorbidities and age, that is, those covariates will be included in the model as additional fixed-effects. Residuals will be examined to assess the appropriateness of the model assumptions. We will fit corresponding models for our pre-specified secondary outcomes.In addition, we will perform several pre-specified sensitivity analyses to investigate differences in pooled effect estimates related to conduct or methods. We will also perform subgroup analyses to investigate differences in pooled effect estimates related to different patient subgroups. We will test whether there is a differential intervention effect amongst the various subgroups with an interaction-test, which is preferred to separate subgroup group-specific analyses (17, 18).Subgroup analyses will be performed for the following variables:i. Underlying diagnosis of included patients (type and stage of cancer): We hypothesize that patients with earlier stage cancer have greater benefit than those with later stage cancer. We also hypothesize that patients with lung (based on our prior analysis) or pancreatic cancer experience a greater benefit from anticoagulant therapy (13).ii. Setting: primary care versus secondary or tertiary care iii. Concomitant treatment (i.e. chemotherapy compared with no chemotherapy): In patients treated with chemotherapy we anticipate larger effects of heparin.Sensitivity analyses:We have pre-specified the following sensitivity analyses:i. Compare the results of a study-level meta-analysis including studies for which we will not receive original datasets with the main IPDMA results. We will then explore if the study-level data differ after excluding the studies for which we do not receive original datasets.ii. Higher risk of bias in the original study may be associated with a greater effect. We will conduct sensitivity analysis by comparing results of trials judged at high risk of bias compared to those judged at low risk of bias (those that are judged as not suffering from any of the methodological limitations listed in the risk of bias assessment above).For the secondary objective of developing a risk prediction model for VTE in patients with cancer we will perform logistic regression stratified by cancer type. The independent variables will be age, gender, cancer stage, the biochemical markers (P-Selectin, C-Reactive Protein and D-Dimer), smoking status, obesity (body mass index), platelet count and haemoglobin level (12). The dependent variable will be DVT and PE, respectively. The regression coefficients will be used to calculate an annual risk and corresponding 95% confidence intervals. We are aware that we may not receive all necessary data from all datasets and consider this an exploratory analysis.

Publication Citation

Schunemann, H. J., Ventresca, M., Crowther, M., Briel, M., Zhou, Q., Noble, S., Macbeth, F., Griffiths, G., Garcia, D., Lyman, G. H., Di Nisio, M., Iorio, A., Mbuagbaw, L., Neumann, I., van Es, N., Brouwers, M., Guyatt, G., Streiff, M. B., Marcucci, M., Baldeh, T., Florez, I. D., Alma, O. G., Solh, Z., Bossuyt, P. M., Kahale, L. A., Ageno, W., Bozas, G., Buller, H. R., Lebeau, B., Lecumberri, R., Loprinzi, C., McBane, R., Sideras, K., Maraveyas, A., Pelzer, U., Perry, J., Klerk, C., Agnelli, G., Akl, E. A. Evaluating prophylactic heparin in ambulatory patients with solid tumours: a systematic review and individual participant data meta-analysis. Lancet Haematol. 2020;7(10):e746-e755.
https://doi.org/10.1016/S2352-3026(20)30293-3

van Es N, Ventresca M, Di Nisio M, Zhou, Q., Noble, S., Crowther, M., Briel, M., Garcia, D., Lyman, G. H., Macbeth, F., Griffiths, G., Iorio, A., Mbuagbaw, L., Neumann, I., Brozek, J., Guyatt, G., Streiff, M. B., Baldeh, T., Florez, I. D., Gurunlu Alma, O., Agnelli, G., Ageno, W., Marcucci, M., Bozas, G., Zulian, G., Maraveyas, A., Lebeau, B., Lecumberri, R., Sideras, K., Loprinzi, C., McBane, R., Pelzer, U., Riess, H., Solh, Z., Perry, J., Kahale, L. A., Bossuyt, P. M., Klerk, C., Buller, H. R., Akl, E. A., Schunemann, H. J. IPDMA Heparin Use in Cancer Patients Research Group. The Khorana score for prediction of venous thromboembolism in cancer patients: An individual patient data meta-analysis. J Thromb Haemost. 2020;18(8):1940-1951. Submit February 26, 2020
https://onlinelibrary.wiley.com/doi/epdf/10.1111/jth.14824