Early Prediction of Major Adverse Cardiovascular Events Using Remote Monitoring

Study Overview
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Brief Summary
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Usual care may not identify subtle clinical changes that precede a major adverse cardiovascular event (MACE). Therefore investigators will explore the effectiveness of using biomarkers, patient reported outcomes (PROs), and patient reported informatics (PRIs) as predictors to a MACE event.

Detailed Description
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Accurate assessment of cardiovascular risk is essential for clinical decision making in that the benefits, risks, and costs of alternative strategies must be weighed ahead of choosing the best treatment for individuals. Existing multivariable risk prediction models are vital components of current practice, and remain the logical standard to which new risk markers must be added and compared.7 The study described herein applies a practical framework for assessing the value of novel risk markers identified through patient reported outcomes (PROs), patient reported informatics (PRIs),8 and biomarkers in the forms of proteins and lipids. Though the purpose of the study is largely exploratory, it does take preliminary steps toward answering the question: Do new PRO-, PRI-, and/or bio-markers add significant predictive information beyond that provided by established cardiac risk factors? STUDY AIMS Aim 1: To measure cross-sectional correlations between PRIs, PROs, MACE biomarker candidates, and established MACE biomarker surrogates known to closely predict MACE itself (e.g. ultra-high sensitive troponin I [u-hsTnI], brain natriuretic peptide [BNP], and high sensitivity C-reactive protein [hsCRP], assay 1). Hypothesis 1: PRI metrics, PRO measure scores, and Candidate Biomarkers will correlate with MACE biomarker surrogates. Justification: Usual care may not identify subtle clinical changes that precede MACE. In order to justify future efforts to employ remote monitoring at scale to predict MACE, we will first evaluate for evidence of basic, cross-sectional correlations between PRIs, PROs, and known MACE surrogate biomarkers. Aim 2: To measure the longitudinal relationship between PRI metrics, PRO measure scores, Candidate Biomarkers, and changes in MACE surrogates. Hypothesis: Changes in PRI metrics, PRO measure scores, and candidate biomarkers will predict changes in MACE biomarker surrogates. Justification: If changes in PRI metrics, PRO measure scores, and candidate biomarkers can predict longitudinal changes in MACE biomarker surrogates, then it will provide biological plausibility that remote surveillance may predict MACE itself; this would justify a larger trial of remote digital monitoring vs. usual care and suggest the concept has merit. Exploratory Aim 2b: To assess improvement in risk prediction provided by risk markers identified in the above aims. Hypothesis: Using PRI-, PRO-, and Bio- marker predictors in combination with established risk factors will provide incremental prognostic information compared to models using established risk factors alone. Additionally, we will perform in-depth proteomic and bioinformatics analysis using baseline samples to explore potential molecular mechanisms driving MACE. Specific Aim 3: To estimate the cost-effectiveness and budget impact of remote monitoring for MACE. Hypothesis: The incremental cost of remote monitoring will be offset by downstream savings engendered by early and precise prediction of unexpected and costly MACE in stable moderate-risk IHD. Justification: Precision Medicine innovations must be cost-effective in order to be scaled across health systems and receive payer support. Using summary results from this study, we will create hypothesis-generating cost-effectiveness, cost-utility, and budget impact models to estimate the projected return on investment of remote monitoring. Importantly, these models are evaluative in nature and do not involve patient-level data - let alone identifiable information - of any sort.

Official Title
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Early Prediction of Major Adverse Cardiovascular Event Surrogates Using Remote Monitoring With Biosensors, Biomarkers, and Patient-Reported Outcomes

Conditions
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Ischemic Heart Disease

Intervention / Treatment
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* Other: Laboratory Biomarker Analysis
* Device: Patient Activity
* Behavioral: Questionnaires


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patient age 18 years or older Patient with history of Ischemic Heart Disease Access to iOS or Android device Ambulatory Exclusion Criteria: Patient with planned revascularization or valve surgery Patients with acute coronary syndrome Patients with psychiatric or substance abuse

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 105 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Biomarker Testing, PROs, PRIs<br>Biomarker testing for cardiac biomarkers, B-type natriuretic peptide (BNP) and Troponin I (Tnl), symptom and quality of life questionnaires, and patient metrics (activity, sleep, heart rate, heart rate variability). | Other: Laboratory Biomarker Analysis<br>* Blood drawn for biomarker analysis at baseline and study exit. Finger sticks at baseline, interim, and study exit.<br>Device: Patient Activity<br>* Continuous monitoring of Patient Reported Informatics (PRIs) at study entry to study completion.<br>Behavioral: Questionnaires<br>* Symptom and quality of life questionnaire at baseline, and every week following to study completion<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Use of biomarkers (BNP and Troponin), PROs and PRIs, to predict a MACE event | The outcome of interest for this study is MACE, which investigators define as a composite outcome of events including: death (all cause), non-fatal MI, non-fatal stroke, or hospitalization for heart failure. Investigators will generate subject-specific monthly summary scores for the PRI and PRO metrics. Analysis of PRO's, PRIs, and biomarker surrogates will be completed using Pearson correlations. To adjust for known risk markers of MACE, investigators will run linear regressions with levels of biomarker surrogates as individual outcomes. | 18 months |