Early Prediction of Major Adverse Cardiovascular Events Using Remote Monitoring

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.

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