Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

Background: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown.

Objective: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP).

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Introduction: Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.

Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning.

Background: Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress.

Comparison of Fractional Flow Reserve Assessment With Demand Stress Myocardial Contrast Echocardiography in Angiographically Intermediate Coronary Stenoses.

Background: Real-time myocardial contrast echocardiography (RTMCE) directly measures capillary flow (CBF), which in turn is a major regulator of coronary flow and resistance during demand or hyperemic stress. Although fractional flow reserve (FFR) was developed to assess the physiological relevance of an epicardial stenosis, it assumes maximal microvascular vasodilation and minimal resistance during vasodilator stress. Therefore, we sought to determine the relationship between CBF assessed with RTMCE during stress echocardiography and FFR in intermediate coronary lesions.

Utility of prechemotherapy evaluation of left ventricular function for patients with lymphoma.

Background: Published guidelines recommend baseline cardiac function testing before initiating anthracycline-based chemotherapy. These recommendations are based largely on consensus, and there is little information regarding how often testing leads to alterations in therapy or whether testing is able to predict subsequent cardiac toxicity.

Patients And Methods: We performed a retrospective analysis of patients with Hodgkin lymphoma and non-Hodgkin lymphoma to determine whether there was a prechemotherapy evaluation of left ventricular function and whether findings from the evaluation led to alterations in therapy.

Quantitative dissection of clone-specific growth rates in cultured malaria parasites.

Measurement of parasite proliferation in cultured red blood cells underpins many facets of malaria research, from drug sensitivity assays to assessing the impact of experimentally altered genes on parasite growth, virulence and fitness. Pioneering efforts to grow Plasmodium falciparum in cultured red blood cells revolutionised malaria research and spurred the development of semi-high-throughput growth assays using radio-labelled hypoxanthine (Hx), an essential nucleic acid precursor, as a reporter of whole-cycle proliferation [Trager, W., Jensen, J.