A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure.

Background: Individuals with acute decompensated heart failure (ADHF) have a varying response to diuretic therapy. Strategies for the early identification of low diuretic efficiency to inform decongestion therapies are lacking.

Objectives: The authors sought to develop and externally validate a machine learning-based phenomapping approach and integer-based diuresis score to identify patients with low diuretic efficiency.

Community-Level Economic Distress, Race, and Risk of Adverse Outcomes After Heart Failure Hospitalization Among Medicare Beneficiaries.

Background: Socioeconomic disadvantage is a strong determinant of adverse outcomes in patients with heart failure. However, the contribution of community-level economic distress to adverse outcomes in heart failure may differ across races and ethnicities.

Methods: Patients of self-reported Black, White, and Hispanic race and ethnicity hospitalized with heart failure between 2014 and 2019 were identified from the Medicare MedPAR Part A 100% Files.

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine.

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Background: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke.

Recommendations for Reporting Machine Learning Analyses in Clinical Research.

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses.

The Role of Government in Precision Medicine, Precision Public Health and the Intersection With Healthy Living.

This paper focuses on the significant role of government in promoting precision medicine and public health and the potential intersection with healthy living (HL) and population health. Recent research has highlighted the interplay between genes, environments and different exposures individuals and populations experience over a lifetime. These interactions between longitudinal behaviors, epigenetics, and expression of the human genome have the potential to transform health and well-being, even within a single generation.

Smooth muscle glucose metabolism promotes monocyte recruitment and atherosclerosis in a mouse model of metabolic syndrome.

Metabolic syndrome contributes to cardiovascular disease partly through systemic risk factors. However, local processes in the artery wall are becoming increasingly recognized to exacerbate atherosclerosis both in mice and humans. We show that arterial smooth muscle cell (SMC) glucose metabolism markedly synergizes with metabolic syndrome in accelerating atherosclerosis progression, using a low-density lipoprotein receptor-deficient mouse model.

American Heart Association Precision Medicine Platform.

Integrating the open science movement with impactful discoveries in science, velocity of technology, and raw power of cloud computing has led to an unprecedented opportunity for scientific discovery. The American Heart Association recently established the Precision Medicine Platform through the efforts of multiple American Heart Association volunteers and a collaboration with Amazon Web Services. The cloud-based platform, powered by Amazon Web Services and available at https://precision.