Evaluating A Mobile Health Monitoring Platform For Capturing Patient-centered Outcomes Among Heart Failure Patients

Overview

About this study

The Food and Drug Administration (FDA) recognizes the importance of developing patient-centric endpoints that are relevant to patients beyond mortality and hospitalizations. Patients with HF have reduced function capacity and quality of life (QoL) and hence it is imperative to explore interventions that impact endpoints that directly measure how a patient feels or functions on a daily basis. Recently, new mobile health technologies have emerged as clinical tools and offer an opportunity to overcome these challenges in measuring functional capacity and recording symptoms. These technologies are able to capture and integrate data from disparate sources from individual patients reflecting their functional status and symptomatology. These data can potentially serve as surrogate endpoints for approval of new HF therapies. In this study, the investigators will test the feasibility of a novel mobile health monitoring platform to capture patients' physiology, functional capacity and assessment of quality of life.

Participation eligibility

Participant eligibility includes age, gender, type and stage of disease, and previous treatments or health concerns. Guidelines differ from study to study, and identify who can or cannot participate. There is no guarantee that every individual who qualifies and wants to participate in a trial will be enrolled. Contact the study team to discuss study eligibility and potential participation.

Inclusion Criteria: -Age 21 to 85 years at signing of informed consent. -History of chronic heart failure, defined as requiring a pharmacologic treatment for heart failure for a minimum of 30 days -Diagnosis of heart failure. -NYHA class II to class III at most recent screening assessment. -Onboarding within 14 days after hospitalization with primary reason of heart failure or emergency department admission with primary reason of heart failure. -History of (within the past 6 months) or current use of diuretics Exclusion Criteria: -Acute coronary syndrome (ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, unstable angina) stroke, or transient ischemic attack, major cardiac surgery, percutaneous coronary intervention, or valvuloplasty within the 30 days prior to randomization. -Uncontrolled hypertension defined as sitting systolic blood pressure (SBP) more than180 mm Hg or diastolic BP (DBP) more than 110 mm Hg. -Untreated severe ventricular arrhythmia (e.g., ventricular tachycardia or ventricular fibrillation). -Symptomatic bradycardia or second or third-degree heart block without a pacemaker Other Medical Conditions -Malignancy except non-melanoma skin cancers, cervical or breast ductal carcinoma in situ within the last 5 years -Hospitalization for any reason other than heart failure within the 30 days prior to randomization -Estimated glomerular filtration rate (eGFR) < 20 mL/min/1.73 m2 or receiving dialysis at screening.

Participating Mayo Clinic locations

Study statuses change often. Please contact the study team for the most up-to-date information regarding possible participation.

Mayo Clinic Location	Status
Jacksonville, Fla. Mayo Clinic principal investigator Christopher McLeod, M.B., Ch.B., Ph.D.	Closed for enrollment
Rochester, Minn. Mayo Clinic principal investigator Molly Jeffery, Ph.D.	Closed for enrollment

Mayo Clinic Location

Status

Jacksonville, Fla.

Mayo Clinic principal investigator

Christopher McLeod, M.B., Ch.B., Ph.D.

Closed for enrollment

Rochester, Minn.

Mayo Clinic principal investigator

Molly Jeffery, Ph.D.

Closed for enrollment

More information

Publications

About 50% of patients with heart failure (HF) have preserved ejection fraction (HFpEF) which is especially common in elderly people with highly prevalent co-morbid conditions. HFpEF is usually defined as an ejection fraction equal to or greater than 50%, although some studies have used a limit as low as 40%. The prevalence of this syndrome is expected to increase over the next decades. The associated impact on mortality and hospital readmissions has made of this entity a major public health issue. Despite the fact that mortality and re-hospitalisation rates of HFpEF are similar to the syndrome of HF with reduced ejection fraction (HFrEF), currently there is no available evidence-based therapy as effective as is the case for HFrEF. Exercise intolerance is the principal clinical feature in HFpEF. The pathophysiological mechanisms behind impaired exercise capacity in these patients are complex and not yet fully elucidated. Current guidelines and consensus documents recommend the implementation of exercise training in HFpEF; however, they are based mostly on results from a few small trials evaluating surrogate endpoints such as exercise capacity and quality of life. The aim of this work was to review the current evidence that supports the effect of the different modalities of physical therapies in HFpEF. Read More on PubMed
Examination of patients with reduced and preserved ejection fraction in the DIG (Digitalis Investigation Group) trials and the CHARM (Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity) trials provides comparisons of outcomes in each of these types of heart failure. Comparison of the patients in these trials, along with the I-PRESERVE (Irbesartan in Heart Failure with Preserved Systolic Function Trial), with patients of similar age, sex distribution, and comorbidity in trials of hypertension, diabetes mellitus, angina pectoris, and atrial fibrillation provides even more interesting insights into the relation between phenotype and rates of death and heart failure hospitalization. The poor clinical outcomes in patients with heart failure and preserved ejection fraction do not seem easily explained on the basis of age, sex, comorbidity, blood pressure, or left ventricular structural remodeling but do seem to be explained by the presence of the syndrome of heart failure. Read More on PubMed
A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a variance inflation factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods. Read More on PubMed

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