Artificial Intelligence in Cardiovascular Clinical Trials.

Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations.

Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.

Background: Artificial intelligence-machine learning (AI-ML) has demonstrated the ability to extract clinically useful information from electrocardiograms (ECGs) not available using traditional interpretation methods. There exists an extensive body of AI-ML research in fields outside of cardiology including several open-source AI-ML architectures that can be translated to new problems in an "off-the-shelf" manner.

Objective: We sought to address the limited investigation of which if any of these off-the-shelf architectures could be useful in ECG analysis as well as how and when these AI-ML approaches fail.

Clinical Trial Participation and COVID-19: a Descriptive Analysis from the American Heart Association's Get With The Guidelines Registry.

As COVID-19 cases begin to decrease in the USA, learning from the pandemic experience will provide insights regarding disparities of care delivery. We sought to determine if specific populations hospitalized with COVID-19 are equally likely to be enrolled in clinical trials. We examined patients hospitalized with COVID-19 at centers participating in the American Heart Association's COVID-19 CVD Registry.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications.

Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure.

Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts.

Bimodal distribution of atrial fibrillation burden in 3 distinct cohorts: What is 'paroxysmal' atrial fibrillation?

Background: Burden of atrial fibrillation (AF), as a continuous measure, is an emerging alternative classification often assumed to increase linearly with progression of disease. Yet there are no descriptions of AF burden distributions across populations.

Methods: We examined patterns of AF burden (% time in AF) across 3 different cohorts: outpatients with AF undergoing Holter monitoring in a national registry (ORBIT-AF II), routine outpatients undergoing Holter monitoring in a tertiary healthcare system (UHealth), and patients >= 65 years with cardiac implantable electronic devices (Merlin.

Repeated cross-sectional analysis of hydroxychloroquine deimplementation in the AHA COVID-19 CVD Registry.

There is little data describing trends in the use of hydroxychloroquine for COVID-19 following publication of randomized trials that failed to demonstrate a benefit of this therapy. We identified 13,957 patients admitted for active COVID-19 at 85 U.S.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort.

Regional Systems of Care in ST Elevation Myocardial Infarction.

ST-segment elevation myocardial infarction is a medical emergency with significant health care delivery challenges to ensure rapid triage and treatment. Several developments over the past decades have led to improved care delivery, decreased time to reperfusion, and decreased mortality. Still, significant challenges remain to further optimize the delivery of care for this patient population.

Association of Wearable Device Use With Pulse Rate and Health Care Use in Adults With Atrial Fibrillation.

Importance: Increasingly, individuals with atrial fibrillation (AF) use wearable devices (hereafter wearables) that measure pulse rate and detect arrhythmia. The associations of wearables with health outcomes and health care use are unknown.

Objective: To characterize patients with AF who use wearables and compare pulse rate and health care use between individuals who use wearables and those who do not.

Quality of Life in Patients With Heart Failure With Recovered Ejection Fraction.

Importance: Heart failure with recovered ejection fraction (HFrecEF) is a recently recognized phenotype of patients with a history of reduced left ventricular ejection fraction (LVEF) that has subsequently normalized. It is unknown whether such LVEF improvement is associated with improvements in health status.

Objective: To examine changes in health-related quality of life in patients with heart failure with reduced ejection fraction (HFrEF) whose LVEF normalized, compared with those whose LVEF remains reduced and those with HF with preserved EF (HFpEF).

Management of Women With Acquired Cardiovascular Disease From Pre-Conception Through Pregnancy and Postpartum: JACC Focus Seminar 3/5.

Acquired cardiovascular conditions are a leading cause of maternal morbidity and mortality. A growing number of pregnant women have acquired and heritable cardiovascular conditions and cardiovascular risk factors. As the average age of childbearing women increases, the prevalence of acute coronary syndromes, cardiomyopathy, and other cardiovascular complications in pregnancy are also expected to increase.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

Background: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics.

Robust Testing in Outpatient Settings to Explore COVID-19 Epidemiology: Disparities in Race/Ethnicity and Age, Salt Lake County, Utah, 2020.

Objective: US-based descriptions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have focused on patients with severe disease. Our objective was to describe characteristics of a predominantly outpatient population tested for SARS-CoV-2 in an area receiving comprehensive testing.

Methods: We extracted data on demographic characteristics and clinical data for all patients (91% outpatient) tested for SARS-CoV-2 at University of Utah Health clinics in Salt Lake County, Utah, from March 10 through April 24, 2020.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.

Innovation in Cardiology: The ACC Innovation Program.

The last half century has seen extraordinary advances in the field of cardiology, including innovations in medications, diagnostic modalities, and therapeutics. Even so, cardiovascular disease remains the leading cause of morbidity and mortality globally, with suboptimal quality of care, inconsistent health outcomes, and unsustainable costs. It is clear that cardiovascular medicine must undergo a digital transformation to enhance the delivery of quality care and to improve outcomes.

Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes.

Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians.