The importance of LDL-C lowering in atherosclerotic cardiovascular disease prevention: Lower for longer is better

Cumulative exposure to low-density lipoprotein cholesterol (LDL-C) is a key driver of atherosclerotic cardiovascular disease (ASCVD) risk. An armamentarium of therapies to achieve robust and sustained reduction in LDL-C can reduce ASCVD risk. The gold standard for LDL-C assessment is ultracentrifugation but in routine clinical practice LDL-C is usually calculated and the most accurate calculation is the Martin/Hopkins equation. For primary prevention, consideration of estimated ASCVD risk frames decision making regarding use of statins and other therapies, and tools such as risk enhancing factors and coronary artery calcium enable tailoring of risk assessment and decision making. In patients with diabetes, lipid lowering therapy is recommended in most patients to reduce ASCVD risk with an opportunity to tailor therapy based on other risk factors. Patients with primary hypercholesterolemia and familial hypercholesterolemia (FH) with baseline LDL-C greater than or equal to 190 mg/dL are at elevated risk, and LDL-C lowering with high-intensity statin therapy is often combined with non-statin therapies to prevent ASCVD. Secondary prevention of ASCVD, including in patients with prior myocardial infarction or stroke, requires intensive lipid lowering therapy and lifestyle modification approaches. There is no established LDL-C level below which benefit ceases or safety concerns arise. When further LDL-C lowering is required beyond lifestyle modifications and statin therapy, additional medications include oral ezetimibe and bempedoic acid, or injectables such as PCSK9 monoclonal antibodies or siRNA therapy. A novel agent that acts independently of hepatic LDL receptors is evinacumab, which is approved for patients with homozygous FH. Other emerging agents are targeted at Lp(a) and CETP. In light of the expanding lipid treatment landscape, this manuscript reviews the importance of early, intensive, and sustained LDL-C-lowering for primary and secondary prevention of ASCVD

AN ELECTRONIC MEDICAL RECORD BASED ALGORITHM TO TAILOR CARDIOVASCULAR DISEASE PREVENTION USING LIPOPROTEIN(A), APOLIPOPROTEIN B, CHOLESTEROL AND MYOCARDIAL INFARCTION DIAGNOSIS: ABCDS PREVENTION PROGRAM

Therapeutic Area: CVD Prevention – Primary and Secondary; ASCVD/CVD Risk Assessment; Preventive Cardiology Best Practices Background: According to the 2022 American Heart Association Heart Disease and Stroke Statistics, coronary heart disease remains the leading cause of death attributable to cardiovascular disease (CVD). Opportunity exists to utilize electronic medical records (EMRs) and biomarkers to facilitate early identification of patients at high risk for CVD. Additionally, automatic or opt-out orders are EMR-based tools that have the potential to improve referral rates to prevention programs. The role of cardiovascular biomarkers and electronic medical records (EMRs) in optimizing identification and referral of patients at risk for CVD are explored in the ABCDs PREVENTION program. Methods: A multidisciplinary team of cardiologists, internists, engineers, and clinical informaticists defined the logic for the guideline based ABCDs PREVENTION tool. The EMR algorithm used the cardiovascular risk biomarker thresholds of lipoprotein(a) > 70 nmol/L, apolipoprotein B > 90 mg/dL, low-density lipoprotein cholesterol  > 150 mg/dL, and triglycerides > 200 mg/dL, and/or a diagnosis of ST-elevation myocardial infarction (STEMI) or non-ST-elevation MI (NSTEMI) based on ICD-10 codes to generate automatic referrals to (1) cardiac rehabilitation (CR), (2) the advanced lipid disorders clinic, and/or (3) Corrie Cardiovascular Health Program (Figure 1). Results: In a test environment, the algorithm was applied to 27 patients identified by the clinical team with STEMI or NSTEMI. The algorithm was 90% successful in triggering automatic referrals to CR and Corrie. Fail rates can be attributed to our current algorithm not detecting some ICD codes related to NSTEMI. The automatic referral to lipid disorders clinic based on abnormal lipid biomarkers is now live and undergoing automation optimization to validate accuracy. Conclusion: Building an EMR-based algorithm to individualize CVD prevention using cardiovascular risk biomarkers and diagnoses may enable early identification and intervention on high-risk patients. Future directions include applying the algorithm to clinical decision support tools as well as an automated order set to increase referral rates to evidenced-based programs focused on primary and secondary CVD prevention. Ultimately, use analysis will determine if the algorithm improves referral rates to CR, lipid clinic, and the Corrie Cardiovascular Health Program to improve access to these evidence-based services

The Johns Hopkins Ciccarone Center's expanded ‘ABC's approach to highlight 2020 updates in cardiovascular disease prevention

In recent years, improvement in outcomes related to cardiovascular disease is in part due to the prioritization and progress of primary and secondary prevention efforts. The Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease expanded ‘ABC's approach is used to highlight key findings in Preventive Cardiology from 2020 and further emphasize the importance of cardiovascular prevention. This simplified approach helps clinicians focus on the most relevant and up to date recommendations for optimizing cardiovascular disease risk through accurate risk assessment and appropriate implementation of lifestyle, behavioral and pharmacologic interventions. While 2020 not only provided practice changing updates by way of clinical guidelines and randomized controlled trials on topics related to antithrombotic and lipid lowering therapy, diabetes management and risk assessment, it also provided promising data on how to improve dietary and exercise adherence and manage genetic risk. By providing clinicians with a systematic approach to cardiovascular prevention and key highlights from the prior year, the goal of significantly reducing the burden of cardiovascular disease worldwide can be achieved

Smart wearable devices in cardiovascular care: where we are and how to move forward.

Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care

Smart wearable devices in cardiovascular care: where we are and how to move forward.

Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care

HYBRID CARDIAC REHABILITATION: EARLY EXPERIENCE FROM RECRUITMENT TO GRADUATION

Disclosures: EMS serves as a consultant to Corrie Health. Under a license agreement between Corrie Health and the Johns Hopkins University, the University owns equity in Corrie Health and the University, FAM, and SSM are entitled to royalty distributions related to technology described in the study discussed in this publication. Additionally, FAM and SSM are founders of and hold equity in Corrie Health. ML and JS have equity ownership in Corrie Health. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. Therapeutic Area: Cardiac Rehabilitation; Secondary Prevention of CVD; Digital Health Background: Low participation in cardiac rehabilitation (CR) is a missed opportunity to improve outcomes in secondary prevention of cardiovascular disease. To increase participation in CR, a multidisciplinary team launched a 12-week hybrid CR program utilizing in-center and virtual CR sessions via an evidence-based digital health platform. Our early experience with >40 participants provides valuable lessons learned for creating a scalable hybrid CR program. Methods: We established a 5-phase approach from recruitment to graduation. Phase 1 - Pre-Bedside: We used the electronic medical record system, Epic, to identify low to moderate risk CR-eligible inpatients. Phase 2 - Bedside: Patient navigators approached inpatients to inform them about hybrid CR, conduct further screening, and for enrollment. Patients were coached how to use the digital health platform including a smartphone application, smartwatch, and blood pressure monitor. Phase 3 - Pre-CR: During hospitalization and after discharge, patients tracked medication use and vital signs, engaged with educational videos, and entered lab values (ex. LDL). Coaches conducted weekly check-in sessions to review progress, safety, and address any technical issues. Phase 4 - CR: Patients completed 2 in-center safety assessments prior to starting home-exercise. Phase 5 - Graduation: Patients received a graduation coaching session at week 12 to celebrate and plan for sustainable healthy lifestyle habits. Results: We identified multiple areas for improvement and enhanced our program. Phase 1: We streamlined patient identification using Epic reports and auto-referral ordersets for eligible patients. Phase 2: We developed flexible onboarding methods with instructional videos tailored for different levels of digital literacy. We also found that assembling a diverse CR team was key for recruiting patients traditionally underrepresented in CR. Phase 3: We created a structured weekly coaching curriculum to promote engagement. Phase 4: We refined low to moderate risk criteria to adjust for lack of exercise data during hospitalization and implemented 2 in-person safety assessments. Phase 5: Some patients expressed interest in advocacy after graduating, possibly through social media support groups. Conclusion: Drawing on lessons learned, we developed a hybrid CR program that adapted to patients’ experiences and provided a scalable solution for patients who cannot attend CR on a regular basis

The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study

BackgroundSmartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity. ObjectiveWe aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions. MethodsWe conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. ResultsThe top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. ConclusionsWe co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators