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.

A Quality Improvement Project to Improve After-visit Summary Patient Instructions in a Pediatric Multidisciplinary Neuromuscular Program.

Introduction: Multidisciplinary clinics aim to coordinate care between multiple specialties for children with medical complexity yet may result in information overload for caregivers. The after-visit summary (AVS) patient instruction section offers a solution by summarizing visit details and recommendations. No known studies address patient instruction optimization and integration within a multidisciplinary clinic setting.

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.

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.

Qualitative feedback from caregivers in a multidisciplinary pediatric neuromuscular clinic.

Objective: This study explored family satisfaction and perceived quality of care in a pediatric neuromuscular care clinic to assess the value of the multidisciplinary clinic (MDC) model in delivering coordinated care to children with neuromuscular disorders, such as cerebral palsy.

Methods: Caregivers of 22 patients were administered a qualitative survey assessing their perceptions of clinic efficiency, care coordination, and communication. Surveys were audio-recorded and transcribed.

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.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.

Methods And Results: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938).

Epidemiology of pediatric trauma and fractures during and beyond the COVID-19 pandemic.

Purpose: Previous literature has shown decreases in pediatric trauma during the COVID-19 outbreak, but few have analyzed beyond the peak of the pandemic. This study assesses the epidemiology of pediatric trauma cases in a high-volume teaching hospital in New York City before, during, and after the height of the COVID-19 pandemic.

Methods: Institutional data on pediatric trauma orthopedic cases from January 1, 2018 to November 30, 2021 were extracted.

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 Impact of COVID-19 on the Timing of Rotator Cuff Repair and Method of Postoperative Follow-up.

 Rotator cuff repair (RCR) is one of the most common arthroscopic procedures. Our investigation aims to quantify the impact that the COVID-19 pandemic had on RCR, specifically on patients with acute, traumatic injuries.  Institutional records were queried to identify patients who underwent arthroscopic RCR between March 1 to October 31 of both 2019 and 2020.

The Role of Experience: How Case Volume and Endourology-Fellowship Training Impact Surgical Outcomes for Ureteroscopy.

Surgical experience is associated with superior outcomes in complex urologic cases, such as prostatectomy, nephrectomy, and cystectomy. The question remains whether experience is predictive of outcomes for less complex procedures, such as ureteroscopy (URS). Our study examined how case volume and endourology-fellowship training impacts URS outcomes.

Quantitative prediction of right ventricular and size and function from the electrocardiogram.

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored.

Methods: We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m), RV dysfunction (RVEF≤40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938).

Risk factors for 90-day readmission and prolonged length of stay after hip surgery in children with cerebral palsy.

Objective: Spastic hip dysplasia is a common complication of cerebral palsy in children, and surgical intervention is usually warranted. While current literature has primarily analyzed single institution outcomes, this study utilized a national database to describe readmission rates and factors correlated with readmission for children with cerebral palsy undergoing hip surgery in order to treat this population more effectively.

Methods: This study queried the Nationwide Readmissions Database (2014-2018) for pediatric patients with cerebral palsy who underwent hip surgery.

Building better pediatric surgeons: A sentiment analysis of online physician review websites.

Purpose: Physician review websites are a heavily utilized patient tool for finding, rating, and reviewing surgeons. Natural language processing such as sentiment analysis provides a comprehensive approach to better understand the nuances of patient perception. This study utilizes sentiment analysis to examine how specific patient sentiments correspond to positive and negative experiences in online reviews of pediatric orthopedic surgeons.

S-acylation of Sprouty and SPRED proteins by the S-acyltransferase zDHHC17 involves a novel mode of enzyme-substrate interaction.

S-acylation is an essential post-translational modification, which is mediated by a family of 23 zDHHC enzymes in humans. Several thousand proteins are modified by S-acylation; however, we lack a detailed understanding of how enzyme-substrate recognition and specificity is achieved. Previous work showed that the ankyrin repeat domain of zDHHC17 (ANK17) recognizes a short linear motif, known as the zDHHC ANK binding motif (zDABM) in substrate protein SNAP25, as a mechanism of substrate recruitment prior to S-acylation.

A national analysis of the effect alcohol use disorder has on short-term complications and readmissions following total shoulder arthroplasty.

Background: Alcohol use disorder has been associated with broad health consequences that may interfere with healing after total shoulder arthroplasty. The aim of this study was to explore the impact of alcohol use disorder on readmissions and complications following total shoulder arthroplasty.

Methods: We used data from the Healthcare Cost and Utilization Project National Readmissions Database (NRD) from 2016 to 2018.

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.

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.

Comparison of Patient Preference, Understanding, and Sentiment for Minimally Invasive Versus Open Spine Surgery.

Study Design: Retrospective questionnaire analysis.

Objective: The goal of this study was to analyze patients' understanding and preferences for minimally invasive spine (MIS) versus open spine surgery.

Summary Of Background Data: MIS surgery is increasing in prevalence.