Development and validation of a prediction model for personalized Newer Oral anticoagulant (NOAC) therapy in patients with thromboembolic events by integrating Pharmacogenomics and cost-effectiveness: A prospective Interventional study
试验速览
- 阶段
- Unknown
- 状态
- 尚未招募
- 入组人数
- 96
- 试验地点
- 2
- 主要终点
- To determine the prevalence of single nucleotide polymorphisms (SNP) associated with ineffective drug response among patients receiving NOACs for anticoagulation therapy.
研究概览
简要总结
Thrombosis forms the primary pathological basis for disease conditions like stroke, venous thromboembolism (VTE) and acute coronary syndrome. Novel Oral Anticoagulants (NOACs) are used for stroke prevention in AF patients, as well as in the management of deep vein thrombosis (DVT) and pulmonary embolism (PE). Despite their widespread acceptance, challenges persist in understanding factors that influence their efficacy and safety profiles in diverse patient populations. NOACs exhibit pharmacokinetic and pharmacodynamic variability influenced by factors such as age, renal function, genetic polymorphisms, and drug interactions, posing challenges in achieving optimal anticoagulation and minimizing the risk of bleeding complications.
The existing knowledge on NOACs in stroke prevention and thrombosis management lacks comprehensive insights into pharmacogenomic variations, which are very relevant to recurrent thromboembolic and hemorrhagic events. Existing GWAS studies on the topic are not providing an adequate amount of information on genetic loci and SNPs influencing drug metabolism and inter-individual variabilities, giving scope for further research. Integration of clinical and genetic data provides a thorough assessment of NOAC effectiveness, enabling clinicians to personalize therapy based on individual patient characteristics and preferences. Pharmacogenomic insights can inform personalized dosing strategies, minimizing the risk of adverse events and better therapeutic outcomes.
The trial, which is a first of its kind in India, introduces an innovative approach by combining clinical and genetic insights to construct a personalized prediction model for NOAC therapy in patients with thromboembolic events. Prediction model will help in providing tailored treatment approaches with improved efficacy, making a significant change in clinical practice.A cost-effectiveness analysis will be performed to assess the impact of a pharmacogenomic-guided dosing strategy for Novel Oral Anticoagulants (NOACs). This will provide valuable insights into potential cost savings in the healthcare system. The analysis will include both direct medical costs (such as hospitalization, diagnostic tests, treatment costs) and indirect costs (loss of productivity, caregiver burden, etc.) among patients experiencing NOAC failure and those without failure.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18.00 Year(s) 至 90.00 Year(s)(—)
- 性别
- All
入选标准
- •Inpatients and outpatients of Department of Stroke medicine and Adult cardiology Patients with and without NOAC failure.
排除标准
- •History of any mechanical or prosthetic valve replacements Patients on haemodialysis or waiting for any renal replacement surgery Patients who are about to undergo any surgery Patients with congenital coagulation abnormalities Patients who are not willing to participate in the study Patients/caregivers who are unwilling to sign the informed consent Pregnant and lactating women.
结局指标
主要结局
To determine the prevalence of single nucleotide polymorphisms (SNP) associated with ineffective drug response among patients receiving NOACs for anticoagulation therapy.
时间窗: Six months
次要结局
- To establish genetic profiling test for appropriate NOACs for thrombosis prevention with minimal adverse complications (effective NOAC based on individual genomic conditions).
- To identify key etiological factors for ineffective anticoagulation in patients taking NOAC.
- To assess a cost-effectiveness analysis of pharmacogenomic-guided dosing strategy.(Baseline, 3months, 6 months & 1 year.)
研究者
Dr Vivek Nambiar
Amrita Institute of Medical Sciences
