Personalized Treatment for Coronary Artery Disease

Recruiting in Palo Alto (17 mi)

+4 other locations

Overseen by Arshed Quyyumi, MD

Age: 18+

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: Emory University

Disqualifiers: Heart failure, Pregnancy, Malignancy, others

No Placebo Group

Trial Summary

What is the purpose of this trial?

People with Coronary Artery Disease (CAD) have narrow or blocked arteries that supply blood to the heart. Reduced blood flow to the heart muscle from CAD can cause chest pain or aching, especially with exercise or activity. CAD can lead to weakening of the heart muscle or heart failure, and a higher risk of heart attack or death. Certain proteins in the blood, known as biomarkers, can be found in people with CAD. Higher levels of these biomarkers are associated with a greater risk of complications from CAD. The purpose of this study is to see if a customized treatment based on biomarkers will reduce the biomarker levels and lead to lower risk of complications from CAD.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the treatment for coronary artery disease?

Research shows that personalized medicine, which includes tailoring treatments based on individual characteristics like genetics and biomarkers, can improve patient outcomes. Although the cardiovascular field has not widely used genetic markers, using prognostic variables to identify likely responders has been a common practice, suggesting potential benefits for personalized treatment in coronary artery disease.

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Is personalized treatment for coronary artery disease safe?

The safety of personalized treatment for coronary artery disease is not directly addressed in the available research articles. However, personalized medicine aims to tailor treatments to individual patients, potentially reducing adverse reactions by considering genetic and other personal factors.

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How is the personalized treatment for coronary artery disease different from other treatments?

This personalized treatment for coronary artery disease uses a patient's unique biological information, such as genetic and clinical data, to tailor therapy specifically for them, unlike standard treatments that follow a one-size-fits-all approach. It leverages machine learning models to predict and improve health outcomes by identifying the best therapy for each individual, potentially extending the time before adverse events occur.

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Eligibility Criteria

Adults aged 21-90 with stable Coronary Artery Disease (CAD) or those who've had recent heart treatments can join. They must have certain levels of calcium in their arteries or visible atherosclerosis. Excluded are pregnant individuals, those planning heart procedures, severe heart failure patients, transplant recipients, and active cancer patients.

Inclusion Criteria

Your calcium levels in the blood are very high, at 400 or more.

I had heart surgery or a heart attack and it's been weeks since my treatment.

You have blockages in your heart blood vessels shown on a special heart imaging test.

Exclusion Criteria

I was born with a heart condition.

Your heart's pumping function is less than 40%.

I am scheduled for a procedure to restore blood flow.

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

1 visit (in-person)

Treatment

Participants receive customized treatment based on biomarker levels, including medication adjustments and lifestyle changes

1 year

Regular visits for physical exams, blood tests, and questionnaires

Follow-up

Participants are monitored for safety and effectiveness after treatment, including measurements of BRS and adverse events

5 years

Follow-up visits at 1, 3, 6, 9 months and annually up to 5 years

Participant Groups

The trial is testing if personalized treatment based on blood protein levels (biomarkers) can lower these biomarker levels and reduce complications from CAD. It involves comparing standard care with a tailored medical/behavioral approach guided by the patient's specific biomarker profile.

Group I: Registry Group Experimental Treatment 1 Intervention

Participants with BRS of 0 at baseline and after 3 months will undergo follow-up including measurements of BRS at the time-points specified for the randomized subjects and also for adverse events. Laboratory results and questionnaire data will be obtained on the phone.

Group II: Optimization Group Experimental Treatment 1 Intervention

Participants with CAD and a BRS greater than 0 who are randomized to the Optimization Group have treatment goals that include achieving LDL-C\<70 mg/dL, hemoglobin A1c \<7%, blood pressure \<130/80 mmHg, smoking cessation, at least 30 minutes of moderate-intensity aerobic activity 5 days a week and weight loss to body mass index \<30 kg/m2. To achieve these goals, both pharmacological and lifestyle interventions will be considered and individualized for each patient.

Group III: Usual Care Group Active Control 1 Intervention

Participants with CAD and a BRS greater than 0 who are randomized to the usual care group will receive standard of care therapy prescribed by their primary care physician and/or cardiologist. Patients and their physicians will be informed that their BRS is ≥1 and they have been randomized to the usual care group.

Find a Clinic Near You

Research Locations Nearby Select from list below to view details:

The Emory Clinic Atlanta, GA

Emory University Hospital Atlanta, GA

Emory Saint Joseph's Hospital Atlanta, GA

Emory Johns Creek Hospiatl Atlanta, GA

More Trial Locations

Who Is Running the Clinical Trial?

Emory University Lead Sponsor

References

1. United States pubmed.ncbi.nlm.nih.gov

Personalized Cardiovascular Medicine Today: A Food and Drug Administration/Center for Drug Evaluation and Research Perspective. [2019] Over the past decade, personalized medicine has received considerable attention from researchers, drug developers, and regulatory agencies. Personalized medicine includes identifying patients most likely to benefit and those most likely to experience adverse reactions in response to a drug, and tailoring therapy based on pharmacokinetics or pharmacodynamic response, as well. Perhaps most exciting is finding ways to identify likely responders through genetic, proteomic, or other tests, so that only likely responders will be treated. However, less precise methods such as identifying historical, demographic, or other indicators of increased or reduced responsiveness are also important aspects of personalized medicine. The cardiovascular field has not used many genetic or proteomic markers, but has regularly used prognostic variables to identify likely responders. The development of biomarker-based approaches to personalized medicine in cardiovascular disease has been challenging, in part, because most cardiovascular therapies treat acquired syndromes, such as acute coronary syndrome and heart failure, which develop over many decades and represent the end result of several pathophysiological mechanisms. More precise disease classification and greater understanding of individual variations in disease pathology could drive the development of targeted therapeutics. Success in designing clinical trials for personalized medicine will require the selection of patient populations with attributes that can be targeted or that predict outcome, and the use of appropriate enrichment strategies once such attributes are identified. Here, we describe examples of personalized medicine in cardiovascular disease, discuss its impact on clinical trial design, and provide insight into the future of personalized cardiovascular medicine from a regulatory perspective.

2. United States pubmed.ncbi.nlm.nih.gov

Group sequential designs for developing and testing biomarker-guided personalized therapies in comparative effectiveness research. [2021] Biomarker-guided personalized therapies offer great promise to improve drug development and improve patient care, but also pose difficult challenges in designing clinical trials for the development and validation of these therapies. We first give a review of the existing approaches, briefly for clinical trials in new drug development and in more detail for comparative effectiveness trials involving approved treatments. We then introduce new group sequential designs to develop and test personalized treatment strategies involving approved treatments.

3. England pubmed.ncbi.nlm.nih.gov

Biomarkers of cardiac disease. [2015] The challenge of medical practice today is to identify individuals who are at risk of developing disease, determine the severity of the disease and distinguish the responders from the nonresponders to therapy (individualized medicine). Advances in molecular genetics and biology have shifted the paradigm for identification of markers from large-scale epidemiologic studies to studies on genomic- and proteomic-based techniques. Consequently, a large number of biologic markers, referred to as biomarkers, are being identified and validated to serve for risk stratification, prognostication and individualization of therapy. Identification of biomarkers for cardiovascular diseases could also provide insight into the pathogenesis of the phenotype, which is fundamental for the development of specific therapies. The list of biomarkers for cardiovascular disease is expanding rapidly. Nonetheless, the field is in the early stages of evolution and large-scale clinical studies are required to validate the utility of newly identified biomarkers in diagnosis, risk stratification and treatment of cardiovascular diseases. Selected biomarkers for coronary atherosclerosis, acute coronary syndromes and heart failure are discussed in this review.

4. England pubmed.ncbi.nlm.nih.gov

Personalized vascular medicine: individualizing drug therapy. [2022] Personalized medicine refers to the application of an individual's biological fingerprint - the comprehensive dataset of unique biological information - to optimize medical care. While the principle itself is straightforward, its implementation remains challenging. Advances in pharmacogenomics as well as functional assays of vascular biology now permit improved characterization of an individual's response to medical therapy for vascular disease. This review describes novel strategies designed to permit tailoring of four major pharmacotherapeutic drug classes within vascular medicine: antiplatelet therapy, antihypertensive therapy, lipid-lowering therapy, and antithrombotic therapy. Translation to routine clinical practice awaits the results of ongoing randomized clinical trials comparing personalized approaches with standard of care management.

5. United States pubmed.ncbi.nlm.nih.gov

Applied pharmacogenomics in cardiovascular medicine. [2021] Interindividual heterogeneity in drug response is a central feature of all drug therapies. Studies in individual patients, families, and populations over the past several decades have identified variants in genes encoding drug elimination or drug target pathways that in some cases contribute substantially to variable efficacy and toxicity. Important associations of pharmacogenomics in cardiovascular medicine include clopidogrel and risk for in-stent thrombosis, steady-state warfarin dose, myotoxicity with simvastatin, and certain drug-induced arrhythmias. This review describes methods used to accumulate and validate these findings and points to approaches--now being put in place at some centers--to implementing them in clinical care.

6. Netherlands pubmed.ncbi.nlm.nih.gov

Personalized treatment for coronary artery disease patients: a machine learning approach. [2021] Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R2 = 0.801 the outcome of interest; the time from diagnosis to a potential adverse event (TAE). Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, the goal of ML4CAD is to identify for every patient the therapy with the best expected TAE using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool.

7. United States pubmed.ncbi.nlm.nih.gov

An Overview of Genomic Biomarker Use in Cardiovascular Disease Clinical Trials. [2021] Clinical trial designs targeting patient subgroups with certain genetic characteristics may enhance the efficiency of developing drugs for cardiovascular disease (CVD). To evaluate the extent to which genetic knowledge translates to the CVD pipeline, we analyzed how genomic biomarkers are utilized in trials. Phase II and III trial protocols for investigational new drugs for CVD and risk factors were evaluated for prospective and exploratory genomic biomarker use; drug targets were evaluated for the presence of evidence that genetic variations can impact CVD risk or drug response. We identified 134 programs (73 unique drug targets) and 147 clinical trials. Less than 1% (n = 1/147) trials used a genomic biomarker prospectively for in-trial enrichment despite 32% (n = 23/73) of the drug targets having evidence of genetic variations. Additionally, 46% (n = 68/147) of the trials specified exploratory biomarker use. The results highlight an opportunity for more targeted CVD drug development by leveraging genomic biomarker knowledge.

8. Switzerland pubmed.ncbi.nlm.nih.gov

Precision Phenomapping of Acute Coronary Syndromes to Improve Patient Outcomes. [2021] Acute coronary syndromes (ACS) are a global leading cause of death. These syndromes show heterogeneity in presentation, mechanisms, outcomes and responses to treatment. Precision medicine aims to identify and synthesize unique features in individuals, translating the acquired data into improved personalised interventions. Current precision treatments of ACS include immediate coronary revascularisation driven by ECG ST-segment elevation, early coronary angiography based on elevated blood cardiac troponins in patients without ST-segment elevation, and duration of intensified antithrombotic therapy according to bleeding risk scores. Phenotypically stratified analyses of multi-omic datasets are urgently needed to further refine and couple the diagnosis and treatment of these potentially life-threatening conditions. We provide definitions, examples and possible ways to advance precision treatments of ACS.

9. Switzerland pubmed.ncbi.nlm.nih.gov

Personalized Management of Cardiovascular Disorders. [2018] Personalized management of cardiovascular disorders (CVD), also referred to as personalized or precision cardiology in accordance with general principles of personalized medicine, is selection of the best treatment for an individual patient. It involves the integration of various "omics" technologies such as genomics and proteomics as well as other new technologies such as nanobiotechnology. Molecular diagnostics and biomarkers are important for linking diagnosis with therapy and monitoring therapy. Because CVD involve perturbations of large complex biological networks, a systems biology approach to CVD risk stratification may be used for improving risk-estimating algorithms, and modeling of personalized benefit of treatment may be helpful for guiding the choice of intervention. Bioinformatics tools are helpful in analyzing and integrating large amounts of data from various sources. Personalized therapy is considered during drug development, including methods of targeted drug delivery and clinical trials. Individualized recommendations consider multiple factors - genetic as well as epigenetic - for patients' risk of heart disease. Examples of personalized treatment are those of chronic myocardial ischemia, heart failure, and hypertension. Similar approaches can be used for the management of atrial fibrillation and hypercholesterolemia, as well as the use of anticoagulants. Personalized management includes pharmacotherapy, surgery, lifestyle modifications, and combinations thereof. Further progress in understanding the pathomechanism of complex cardiovascular diseases and identification of causative factors at the individual patient level will provide opportunities for the development of personalized cardiology. Application of principles of personalized medicine will improve the care of the patients with CVD.

10. England pubmed.ncbi.nlm.nih.gov

Risk Stratification in Patients with Coronary Artery Disease: A Practical Walkthrough in the Landscape of Prognostic Risk Models. [2023] Although a combination of multiple strategies to prevent and treat coronary artery disease (CAD) has led to a relative reduction in cardiovascular mortality over recent decades, CAD remains the greatest cause of morbidity and mortality worldwide. A variety of individual factors and circumstances other than clinical presentation and treatment type contribute to determining the outcome of CAD. It is increasingly understood that personalised medicine, by taking these factors into account, achieves better results than "one-size-fitsall" approaches. In recent years, the multiplication of risk scoring systems for CAD has generated some degree of uncertainty regarding whether, when and how predictive models should be adopted when making clinical decisions. Against this background, this article reviews the most accepted risk models for patients with evidence of CAD to provide practical guidance within the current landscape of tools developed for prognostic risk stratification.