This trial will determine the clinical effectiveness of polygenic risk score testing among patients at high genetic risk for at least one of six diseases (coronary artery disease, atrial fibrillation, type 2 diabetes mellitus, colorectal cancer, breast cancer, or prostate cancer), measured by time-to-diagnosis of prevalent or incident disease over 24 months.
One of the most pressing controversies in genomics today is the clinical utility of polygenic risk scores (PRS). Broadening the scope of genomic risk testing beyond monogenic diseases, PRS combine information from hundreds or even millions of genetic loci, each with a very small effect size on the risk of common complex disease. The result is a continuous quantitative risk factor for susceptibility to conditions such as coronary artery disease (CAD), type 2 diabetes (T2D), and breast cancer. Compared to rarer monogenic disease variants, PRS have greater transformative potential for public health and healthcare in their ability to identify much larger proportions of the population at significantly elevated risk for disease, facilitating evidence-based prevention and management. Moreover, their prediction ability has vastly improved compared to earlier PRS that included only a limited number of genetic variants. However, while the associations between PRS and a wide range of common diseases are well established (clinical validity), the potential impact of this information on patient health outcomes (clinical utility) remains contested and understudied. This study will examine the effectiveness and implementation outcomes from the use of PRS for 6 common diseases that are screened for by PCPs and have established prevention strategies: CAD, AFib, T2D, colorectal cancer, prostate cancer, and breast cancer. This trial has two aims: Aim 1: Conduct a randomized controlled trial (RCT) to determine the clinical effectiveness of PRS among patients at high genetic risk for at least one disease, measured by changes in clinical management (process outcomes) and time to diagnosis of prevalent or incident disease (clinical outcome) over 24 months. Aim 2: Measure high-priority genomic medicine implementation outcomes, including primary care provider (PCP) knowledge and beliefs about PRS, patient activation in healthcare, medication adherence, and costs.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
Polygenic risk score report from a Clinical Laboratory Improvement Amendment (CLIA)-certified laboratory for coronary artery disease, atrial fibrillation, type 2 diabetes, colorectal cancer, breast cancer (for women only), and prostate cancer (for men only), delivered along with patient- and provider-level educational material.
VA Boston Healthcare System
Boston, Massachusetts, United States
RECRUITINGTime-to-new diagnosis of common complex disease
The primary outcome of the study is time-to-diagnosis both of undiagnosed prevalent cases of the 6 target conditions and incident cases during the study period. This composite outcome will only include clinically significant diagnoses, as adjudicated by expert clinical chart review.
Time frame: 24 months after enrollment
Diagnostic testing
Any evidence that the patient-participant underwent additional diagnostic testing for the six target diseases since enrollment: coronary artery disease (stress testing, cardiac CT for coronary artery calcium (CAC), coronary angiography), atrial fibrillation (ECG, heart rhythm monitoring), type 2 diabetes (hemoglobin A1c, blood glucose), colorectal cancer (colonoscopy, sigmoidoscopy, fecal blood testing, CT colonography), breast cancer (mammography, breast MRI, breast ultrasound, breast biopsy), and prostate cancer (PSA testing, prostate biopsy).
Time frame: 24 months after enrollment
Patient activation
Self-reported understanding, competence, and willingness to participate health care decisions and processes assessed on the baseline and end-of-study surveys, using the 13-item short form of the Patient Activation Measure (Hibbard, Health Services Research 2005).
Time frame: Baseline and 24 months after enrollment
Healthcare costs
A combination of administrative data and microcosting approaches will be used to estimate the costs of the intervention and the subsequent patient-level healthcare costs over the 24 months after enrollment. Estimates of the infrastructure and personnel needed to deliver the intervention will be derived empirically from the study. Healthcare costs will be abstracted from billing and administrative data.
Time frame: 24 months after enrollment
Medication adherence
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SCREENING
Masking
SINGLE
Enrollment
1,076
Self-report of taking medications as prescribed assessed on the baseline and end-of-study surveys, using the 3-item Voils Medication Adherence Survey (Voils, Medical Care, 2012).
Time frame: Baseline and 24 months after enrollment