Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study.

: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.

Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.

Outcomes of Ultrafiltration in community-based hospitals.

Objective: We sought to examine outcomes of ultrafiltration in real world community-based hospital settings.

Background: Ultrafiltration (UF) is an accepted therapeutic option for advanced decompensated heart failure (ADHF). the feasibility of UF in a community hospital setting, by general cardiologists in a start-up program had not been objectively evaluated.

AI-based preeclampsia detection and prediction with electrocardiogram data.

Introduction: More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

Background: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy.

Objective: This study aimed to assess the efficacy of an artificial intelligence-based heart failure detection model for peripartum cardiomyopathy detection.

Study Design: We first built a deep-learning model for heart failure detection using retrospective data at the University of Tennessee Health Science Center.

A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction.

Background: Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF risk early to allow for risk modification.

Pretreatment identification of 90-day readmission among heart failure patients receiving aquapheresis treatment.

Background: The use of traditional models to predict heart failure (HF) has limitations in preventing HF hospitalizations. Artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine only have limited data published regarding HF populations, with none assessing the favorability of decongestive therapy aquapheresis (AQ). AI and ML can be leveraged to design non-traditional models to identify those who are at high risk of HF readmissions.

Incidence, mortality and disability-adjusted life years of acute myocardial infarction in Kazakhstan: data from unified national electronic healthcare system 2014-2019.

Background: Cardiovascular diseases contribute to premature mortality globally, resulting in substantial social and economic burdens. The Global Burden of Disease (GBD) Study reported that in 2019 alone, heart attack and strokes accounted for the deaths of 18.6 million individuals.

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH).

The fungal intestinal microbiota predict the development of bronchopulmonary dysplasia in very low birthweight newborns.

Rationale: Bronchopulmonary dysplasia (BPD) is the most common morbidity affecting very preterm infants. Gut fungal and bacterial microbial communities contribute to multiple lung diseases and may influence BPD pathogenesis.

Methods: We performed a prospective, observational cohort study comparing the multikingdom fecal microbiota of 144 preterm infants with or without moderate to severe BPD by sequencing the bacterial 16S and fungal ITS2 ribosomal RNA gene.

Barth Syndrome: Psychosocial Impact and Quality of Life Assessment.

Background: Barth syndrome (BTHS) is a rare X-linked genetic disease that affects multiple systems and leads to complex clinical manifestations. Although a considerable amount of research has focused on the physical aspects of the disease, less has focused on the psychosocial impact and quality of life (QoL) in BTHS.

Methods: The current study investigated caregiver- ( = 10) and self-reported ( = 16) psychological well-being and QoL in a cohort of BTHS-affected patients and families.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity.

ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure.

Aims: Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction.

Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic.

Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and increased risk of death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, the need for intensive care (admission to the Intensive Care Unit; ICU) as well as risk of mortality.

Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables.

Background: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder.

Objective: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests.

Methods: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria.

Association of dyskalemias with short-term health care utilization in patients with advanced CKD.

Patients with advanced chronic kidney disease (CKD) are at high risk for dyskalemias, which may induce arrhythmias that require immediate emergent or hospital care. The association of dyskalemias with short-term hospital/emergency room (ER) visits in advanced CKD is understudied. To assess the association of dyskalemias with short-term hospital/ER visits in an advanced CKD population.

Machine learning models improve prediction of large vessel occlusion and mechanical thrombectomy candidacy in acute ischemic stroke.

Background And Purpose: Early identification of large vessel occlusions (LVO) and timely recanalization are paramount to improved clinical outcomes in acute ischemic stroke. A stroke assessment that maximizes sensitivity and specificity for LVOs is needed to identify these cases and not overburden the health system with unnecessary transfers. Machine learning techniques are being used for predictive modeling in many aspects of stroke care and may have potential in predicting LVO presence and mechanical thrombectomy (MT) candidacy.

Association of Dyskalemias with Ischemic Stroke in Advanced Chronic Kidney Disease Patients Transitioning to Dialysis.

Introduction: Hypo- and hyperkalemia are associated with a higher risk of ischemic stroke. However, this association has not been examined in an advanced chronic kidney disease (CKD) population.

Methods: From among 102,477 US veterans transitioning to dialysis between 2007 and 2015, 21,357 patients with 2 pre-dialysis outpatient estimated glomerular filtration rates <30 mL/min/1.

Misalignment between perceptual boundaries and weight categories reflects a new normal for body size perception.

Combatting the current global epidemic of obesity requires that people have a realistic understanding of what a healthy body size looks like. This is a particular issue in different population sub-groups, where there may be increased susceptibility to obesity-related diseases. Prior research has been unable to systematically assess body size judgement due to a lack of attention to gender and race; our study aimed to identify the contribution of these factors.

Artificial Intelligence-Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors.

Purpose: Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline-recommended baseline ECG to predict cardiomyopathy.

Material And Methods: Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study.

A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery.

Scientific evidence confirm that significant racial disparities exist in healthcare, including surgery outcomes. However, the causal pathway underlying disparities at preoperative physical condition of children is not well-understood. This research aims to uncover the role of socioeconomic and environmental factors in racial disparities at the preoperative physical condition of children through multidimensional integration of several data sources at the patient and population level.