Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R squared = 0.801 the time from diagnosis to a potential adverse event (TAE) and gain accurate approximations of the counterfactual treatment effects. Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, ML4CAD identifies for every patient the therapy with the best expected outcome using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool

Selection of endurance capabilities and the trade-off between pressure and volume in the evolution of the human heart

Chimpanzees and gorillas, when not inactive, engage primarily in short bursts of resistance physical activity (RPA), such as climbing and fighting, that creates pressure stress on the cardiovascular system. In contrast, to initially hunt and gather and later to farm, it is thought that preindustrial human survival was dependent on lifelong moderate-intensity endurance physical activity (EPA), which creates a cardiovascular volume stress. Although derived musculoskeletal and thermoregulatory adaptations for EPA in humans have been documented, it is unknown if selection acted similarly on the heart. To test this hypothesis, we compared left ventricular (LV) structure and function across semiwild sanctuary chimpanzees, gorillas, and a sample of humans exposed to markedly different physical activity patterns. We show the human LV possesses derived features that help augment cardiac output (CO) thereby enabling EPA. However, the human LV also demonstrates phenotypic plasticity and, hence, variability, across a wide range of habitual physical activity. We show that the human LV’s propensity to remodel differentially in response to chronic pressure or volume stimuli associated with intense RPA and EPA as well as physical inactivity represents an evolutionary trade-off with potential implications for contemporary cardiovascular health. Specifically, the human LV trades off pressure adaptations for volume capabilities and converges on a chimpanzee-like phenotype in response to physical inactivity or sustained pressure loading. Consequently, the derived LV and lifelong low blood pressure (BP) appear to be partly sustained by regular moderate-intensity EPA whose decline in postindustrial societies likely contributes to the modern epidemic of hypertensive heart disease

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts