An Artificial Intelligence-Based Noninvasive Solution to Estimate Pulmonary Artery Pressure

Aims: Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs. Methods and results: We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 18 (10%) patients, and tested the performance of the method using 18 (10%) patients, respectively. After comparing all multi-stage algorithms on the testing cohort, the combination of the residual neural network model and wavelet scattering transform data preprocessing method attained the highest coefficient of determination R2 of 90.78% as well as the following other performance metrics and corresponding 95% confidence intervals (CIs): mean square error of 11.55 (10.22–13.5), mean absolute error of 2.42 (2.06–2.85), mean absolute percentage error of 0.91 (0.76–1.13), and explained variance score of 90.87 (85.32–93.31). Conclusion: The proposed analytical approach that combines data preprocessing, sampling method, and AI algorithm can precisely predict PAP waveform using three input signals obtained by noninvasive approaches

A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy. Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts. Results: For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%. Conclusion: In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts

Ischemic Time and Risk Factors Associated With The Incidence Of Cardiogenic Shock In Patients Presenting With ST Elevation Myocardial Infarction

Cardiogenic shock (CS) is the number one predictor of mortality in patients with ST-elevation myocardial infarction (STEMI). Emergent evaluation and treatment including revascularization when indicated is recommended upon arrival to hospital. There is variation in time to presentation and total ischemic time in this patient population. We sought to evaluate the impact of ischemic time on incidence of CS and variables which may influence its presentation

Glycemic control in insulin and non-insulin dependent diabetic patients with ST elevation predicts time to presentation

Worldwide, the annual number of people diagnosed with diabetes continues to increase. Classically, Diabetes Mellitus (DM) has been associated with silent or atypical initial symptoms and increased time to presentation in patients presenting with ST elevation myocardial infarction (STEMI) compared to non-DM patients. It is less clear whether the type of treatment or the adequacy of glycemic control impacts the time to presentation and type of symptoms. Therefore, we sought to evaluate the impact of poor glycemic control on these outcomes as measured by hemoglobin A1c

Optimal Multi-Stage Arrhythmia Classification Approach

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model. Methods: We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold. Results: The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44–99.99), weighted F1-score of 98.46 (90–100), AUC of 98.99 (96.89–100), sensitivity (SE) of 96.97 (82.54–99.89), and specificity (SP) of 100 (62.97–100). Conclusions: The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies

Status of Early-Career Academic Cardiology, A Global Perspective

Early career academic cardiologists, whom many believe are an important component of the future of cardiovascular care, face a myriad of challenges. The Early Career Section Academic Working Group of the American College of Cardiology (ACC) along with senior leadership support, assessed the progress of this cohort from 2013–2016 with a global perspective. Data consisted of accessing National Heart Lung and Blood Institute (NHLBI) public information, American Heart Association and international organizations providing data, and a membership-wide survey. Although NHBLI increased funding of career development grants, only a small number of early career ACC members have benefited as funding of the entire cohort has decreased. Personal motivation, institutional support, and collaborators continued to be positive influential factors. Surprisingly, mentoring ceased to correlate positively with obtaining external grants. Totality of findings suggests that the status of early career academic cardiologists remain challenging; therefore, we recommend a set of attainable solutions

New devices and technology in interventional cardiology.

There have been substantial improvements made in the tools and techniques used since the advent of percutaneous coronary intervention. What was primarily developed as a treatment of coronary artery disease is now used to address a variety of structural heart disease problems. The outcomes have been remarkably successful with relatively low complication rates that rival the results of open-heart surgery. This article will review some of the new devices available for management of structural cardiac conditions including congenital defects and acquired valvular abnormalities. Transcatheter treatment offers advantages over surgical intervention in recovery time, improved patient satisfaction, lower procedural risk, and avoidance of cardio-pulmonary bypass especially in high-risk patients. We will discuss different medical conditions and introduce the devices used to treat these conditions. Each device or technique has benefits and risks, and familiarity with the devices along with patient selection will best optimize the outcome