Behavioural Economics to Improve Antihypertensive Therapy Adherence (BETA): protocol for a pilot randomised controlled trial in Los Angeles

Introduction Non-adherence to antihypertensive therapy is one of the major barriers to reducing the risk of cardiovascular disease. Several interventions have targeted higher medication adherence, yet most do not result in sustained adherence. Routinisation has emerged as a potential method for mitigating this problem, but requires high motivation during the relatively long habit formation phase. This pilot randomised controlled trial aims to test the feasibility, acceptability, and preliminary efficacy of behavioural economics-based incentives and text messages to support the routinisation of the medication-taking behaviour for promoting long-term medication adherence.Methods and analysis This study will recruit and randomly assign 60 adult patients seeking care for hypertension at the Cedars-Sinai Medical Center in Los Angeles to one of the three groups, Control (n=20), Messages (n=20) and Incentives (n=20) in a 1:1:1 ratio. All participants will receive information about the importance of routinisation and will select an existing behavioural routine (‘anchor’) to which they will tie their pill-taking to, and the corresponding time. Additionally, participants in the Messages group will receive daily text messages reminding them of the importance of routines, while those in the Incentives group will receive daily text messages and conditional prize drawings. The interventions will be delivered over three months. Participants will be followed for six months post-intervention to measure behavioural persistence. Surveys will be administered at baseline, month-3 and month-9 visits. Primary outcomes include: (1) electronically measured mean medication adherence during the intervention period and (2) post-intervention period; and (3) mean timely medication adherence based around the time of the participants’ anchor during the intervention period, and (4) post-intervention period.Ethics and dissemination The study was approved by the Cedars-Sinai Institutional Review Board (Study ID: Pro00057764). Findings will be published in scientific peer-reviewed journals.Trial registration number NCT04029883

Progression of coronary microvascular dysfunction to heart failure with preserved ejection fraction: a case report

Abstract Background In women with evidence of ischemia and no obstructive coronary artery disease the underlying mechanism is most often attributed to coronary microvascular dysfunction. Higher rates of adverse cardiovascular events, specifically heart failure with preserved ejection fraction, are present in women with coronary microvascular dysfunction, leading to the hypothesis that coronary microvascular dysfunction may contribute to the progression of heart failure with preserved ejection fraction. Case summary A 55-year-old, Caucasian woman with a past medical history of chest pain and shortness of breath was referred to our tertiary care center and diagnosed as having coronary microvascular dysfunction by invasive coronary reactivity testing. After 10 years of follow-up care for coronary microvascular dysfunction, she presented to an emergency room in acute heart failure and was diagnosed as having heart failure with preserved ejection fraction. Discussion The current case report provides a specific example in support of existing studies that demonstrate that coronary microvascular dysfunction may be a precursor of heart failure with preserved ejection fraction. Further research is needed to establish causality and management. Trial registration Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT02582021

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data

Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers

Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers

Telemedicine has been used to remotely diagnose and treat patients, yet previously applied telemonitoring approaches have been fraught with adherence issues. The primary goal of this study was to evaluate the adherence rates using a consumer-grade continuous-time heart rate and activity tracker in a mid-risk cardiovascular patient population. As a secondary analysis, we show the ability to utilize the information provided by this device to identify information about a patient's state by correlating tracker information with patient-reported outcome survey scores. We showed that using continuous-time activity trackers with heart rate monitors can be effective in a telemonitoring application, as patients had a high level of adherence (90.0% median usage) and low attrition (0.09% decrease per day) over a 90-day period. Furthermore, data collected correlated significantly with clinically relevant patient surveys (r2=0.15 for PROMIS global health scores, p < .00001), and therefore might provide an effective signal for identifying patients in need of intervention

Clinical Utility of SARS-CoV-2 Serological Testing and Defining a Correlate of Protection.

Herein, we review established clinical use cases for SARS-CoV-2 antibody measures, which include diagnosis of recent prior infection, isolating high titer convalescent plasma, diagnosing multisystem inflammatory syndrome in children (MIS-C), and booster dosing in the immunosuppressed and other populations. We then address whether an antibody correlate of protection (CoP) for SARS-CoV-2 has been successfully defined with the following considerations: Antibody responses in the immunocompetent, vaccine type, variants, use of binding antibody tests vs. neutralization tests, and endpoint measures. In the transition from the COVID-19 pandemic to endemic, there has been much interest in defining an antibody CoP. Due to the high mutability of respiratory viruses and our current knowledge of SARS-CoV-2 variants defining a CoP for prevention of infection is unrealistic. However, a CoP may be defined for prevention of severe disease requiring hospitalization and/or death. Most SARS-CoV-2 CoP research has focused on neutralization measurements. However, there can be significant differences in neutralization test methods, and disparate responses to new variants depending on format. Furthermore, neutralization assays are often impractical for high throughput applications (e.g., assessing humoral immune response in populations or large cohorts). Nevertheless, CoP studies using neutralization measures are reviewed to determine where there is consensus. Alternatively, binding antibody tests could be used to define a CoP. Binding antibody assays tend to be highly automatable, high throughput, and therefore practical for large population applications. Again, we review studies for consensus on binding antibody responses to vaccines, focusing on standardized results. Binding antibodies directed against the S1 receptor binding domain (S1-RBD) of the viral spike protein can provide a practical, indirect measure of neutralization. Initially, a response for S1-RBD antibodies may be selected that reflects the peak response in immunocompetent populations and may serve as a target for booster dosing in the immunocompromised. From existing studies reporting peak S1-RBD responses in standardized units, an approximate range of 1372-2744 BAU/mL for mRNA and recombinant protein vaccines was extracted that could serve as an initial CoP target. This target would need to be confirmed and potentially adjusted for updated vaccines, and almost certainly for other vaccine formats (i.e., viral vector). Alternatively, a threshold or response could be defined based on outcomes over time (i.e., prevention of severe disease). We also discuss the precedent for clinical measurement of antibodies for vaccine-preventable diseases (e.g., hepatitis B). Lastly, cellular immunity is briefly addressed for its importance in the nature and durability of protection

A protocol integrating remote patient monitoring patient reported outcomes and cardiovascular biomarkers.

We describe the protocol, design, and methodology of the Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study as a multicomponent remote patient monitoring in cardiology. Using biosensor, biomarkers, and patient-reported outcomes in participants with stable ischemic heart disease, the PRE-MACE study is designed to measure cross-sectional correlations and establish the ability of remote monitoring to predict major adverse cardiovascular event (MACE) biomarkers and incident MACE at baseline and 12-month follow-up. It will further assess the adherence and cost-effectiveness of remote monitoring and blood sampling over the initial months. Despite medication and lifestyle changes, patients with cardiovascular disease can experience MACE due to undertreatment, poor adherence, or failure to recognize clinical or biochemical changes that presage MACE. Identifying patients using remote monitoring to detect MACE forerunners has potential to improve outcomes, avoid MACE, and reduce resource utilization. Data collection will include: (1) continuous remote monitoring using wearable biosensors; (2) biomarker measurements using plasma and at-home micro-sampling blood collection; and (3) patient-reported outcomes to monitor perceived stress, anxiety, depression, and health-related quality of life. Two hundred participants will be followed for 90 days with a subset (n = 80) monitored for 180 days. All participants will be followed up for MACE at 12 months.The PRE-MACE study will utilize remote monitoring with biosensors, biomarkers, and patient-reported outcomes to identify intermediate biomarkers of MACE in patients with stable ischemic heart disease. If shown to be effective, this intervention can be utilized between health visits to predict MACE and reduce financial impact of MACE

Clinical Utility of SARS-CoV-2 Serological Testing and Defining a Correlate of Protection

Herein, we review established clinical use cases for SARS-CoV-2 antibody measures, which include diagnosis of recent prior infection, isolating high titer convalescent plasma, diagnosing multisystem inflammatory syndrome in children (MIS-C), and booster dosing in the immunosuppressed and other populations. We then address whether an antibody correlate of protection (CoP) for SARS-CoV-2 has been successfully defined with the following considerations: Antibody responses in the immunocompetent, vaccine type, variants, use of binding antibody tests vs. neutralization tests, and endpoint measures. In the transition from the COVID-19 pandemic to endemic, there has been much interest in defining an antibody CoP. Due to the high mutability of respiratory viruses and our current knowledge of SARS-CoV-2 variants defining a CoP for prevention of infection is unrealistic. However, a CoP may be defined for prevention of severe disease requiring hospitalization and/or death. Most SARS-CoV-2 CoP research has focused on neutralization measurements. However, there can be significant differences in neutralization test methods, and disparate responses to new variants depending on format. Furthermore, neutralization assays are often impractical for high throughput applications (e.g., assessing humoral immune response in populations or large cohorts). Nevertheless, CoP studies using neutralization measures are reviewed to determine where there is consensus. Alternatively, binding antibody tests could be used to define a CoP. Binding antibody assays tend to be highly automatable, high throughput, and therefore practical for large population applications. Again, we review studies for consensus on binding antibody responses to vaccines, focusing on standardized results. Binding antibodies directed against the S1 receptor binding domain (S1-RBD) of the viral spike protein can provide a practical, indirect measure of neutralization. Initially, a response for S1-RBD antibodies may be selected that reflects the peak response in immunocompetent populations and may serve as a target for booster dosing in the immunocompromised. From existing studies reporting peak S1-RBD responses in standardized units, an approximate range of 1372–2744 BAU/mL for mRNA and recombinant protein vaccines was extracted that could serve as an initial CoP target. This target would need to be confirmed and potentially adjusted for updated vaccines, and almost certainly for other vaccine formats (i.e., viral vector). Alternatively, a threshold or response could be defined based on outcomes over time (i.e., prevention of severe disease). We also discuss the precedent for clinical measurement of antibodies for vaccine-preventable diseases (e.g., hepatitis B). Lastly, cellular immunity is briefly addressed for its importance in the nature and durability of protection

A protocol integrating remote patient monitoring patient reported outcomes and cardiovascular biomarkers.

We describe the protocol, design, and methodology of the Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study as a multicomponent remote patient monitoring in cardiology. Using biosensor, biomarkers, and patient-reported outcomes in participants with stable ischemic heart disease, the PRE-MACE study is designed to measure cross-sectional correlations and establish the ability of remote monitoring to predict major adverse cardiovascular event (MACE) biomarkers and incident MACE at baseline and 12-month follow-up. It will further assess the adherence and cost-effectiveness of remote monitoring and blood sampling over the initial months. Despite medication and lifestyle changes, patients with cardiovascular disease can experience MACE due to undertreatment, poor adherence, or failure to recognize clinical or biochemical changes that presage MACE. Identifying patients using remote monitoring to detect MACE forerunners has potential to improve outcomes, avoid MACE, and reduce resource utilization. Data collection will include: (1) continuous remote monitoring using wearable biosensors; (2) biomarker measurements using plasma and at-home micro-sampling blood collection; and (3) patient-reported outcomes to monitor perceived stress, anxiety, depression, and health-related quality of life. Two hundred participants will be followed for 90 days with a subset (n = 80) monitored for 180 days. All participants will be followed up for MACE at 12 months.The PRE-MACE study will utilize remote monitoring with biosensors, biomarkers, and patient-reported outcomes to identify intermediate biomarkers of MACE in patients with stable ischemic heart disease. If shown to be effective, this intervention can be utilized between health visits to predict MACE and reduce financial impact of MACE

Biometric and Psychometric Remote Monitoring and Cardiovascular Risk Biomarkers in Ischemic Heart Disease.

Background Patients with stable ischemic heart disease represent a heterogeneous population at variable risk for major adverse cardiac events (MACE). Because MACE typically occurs outside the hospital, we studied whether biometric and psychometric remote patient monitoring are associated with MACE risk biomarkers. Methods and Results In 198 patients with stable ischemic heart disease (mean age 65±11 years, 60% women), we evaluated baseline measures, including biometric (FitBit 2) and psychometric (acquired via smartphone-administered patient-reported outcomes) remote monitoring, in the PRE-MACE (Prediction, Risk, and Evaluation of Major Adverse Cardiac Events) study. In multivariable adjusted regression analyses, we examined the association of these measures with biomarkers of MACE risk, including NT-proBNP (N-terminal pro-b-type natriuretic peptide), u-hs-cTnI (ultra-high sensitivity cardiac-specific troponin I), and hs-CRP (high-sensitivity C-reactive) protein. Both biometric and psychometric measures were associated with NT-proBNP. Specifically, step count, heart rate, physical activity, global health score, and physical function score were all inversely related, whereas physical limitation score was directly related (P≤0.05 for all). However, only biometric measures (step count and heart rate) were associated with u-hs-cTnI (inversely related, P<0.05), while only the psychometric measures of physical limitation were associated with hs-CRP (directly related, P≤0.05). Conclusions In stable ischemic heart disease patients, remotely monitored measures were associated with MACE risk biomarkers. Both biometric and psychometric measures were related to NT-proBNP. In contrast, biometric measures were uniquely related to u-hs-cTnI, while psychometric indices were uniquely related to hs-CRP. Further investigation could assess the predictive value of these metrics for MACE in ischemic heart disease

Biometric and Psychometric Remote Monitoring and Cardiovascular Risk Biomarkers in Ischemic Heart Disease.

Background Patients with stable ischemic heart disease represent a heterogeneous population at variable risk for major adverse cardiac events (MACE). Because MACE typically occurs outside the hospital, we studied whether biometric and psychometric remote patient monitoring are associated with MACE risk biomarkers. Methods and Results In 198 patients with stable ischemic heart disease (mean age 65±11 years, 60% women), we evaluated baseline measures, including biometric (FitBit 2) and psychometric (acquired via smartphone-administered patient-reported outcomes) remote monitoring, in the PRE-MACE (Prediction, Risk, and Evaluation of Major Adverse Cardiac Events) study. In multivariable adjusted regression analyses, we examined the association of these measures with biomarkers of MACE risk, including NT-proBNP (N-terminal pro-b-type natriuretic peptide), u-hs-cTnI (ultra-high sensitivity cardiac-specific troponin I), and hs-CRP (high-sensitivity C-reactive) protein. Both biometric and psychometric measures were associated with NT-proBNP. Specifically, step count, heart rate, physical activity, global health score, and physical function score were all inversely related, whereas physical limitation score was directly related (P≤0.05 for all). However, only biometric measures (step count and heart rate) were associated with u-hs-cTnI (inversely related, P<0.05), while only the psychometric measures of physical limitation were associated with hs-CRP (directly related, P≤0.05). Conclusions In stable ischemic heart disease patients, remotely monitored measures were associated with MACE risk biomarkers. Both biometric and psychometric measures were related to NT-proBNP. In contrast, biometric measures were uniquely related to u-hs-cTnI, while psychometric indices were uniquely related to hs-CRP. Further investigation could assess the predictive value of these metrics for MACE in ischemic heart disease