Early Prediction of Major Adverse Cardiovascular Events Using Remote Monitoring
Condition: Ischemic Heart Disease
MeSH terms: Heart Diseases; Myocardial Ischemia; Coronary Artery Disease
Study type: Observational

Summary: 
      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: 
      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.
    

Intervention name(s): Laboratory Biomarker Analysis; Patient Activity; Questionnaires
Intervention type(s): Other; Device; Behavioral
Intervention description(s): Blood drawn for biomarker analysis at baseline and study exit. Finger sticks at baseline, interim, and study exit.; Continuous monitoring of Patient Reported Informatics (PRIs) at study entry to study completion.; Symptom and quality of life questionnaire at baseline, and every week following to study completion
Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Patients with history of Ischemic Heart Disease.