Notes

Kidney final results in Cookware people receiving oral anticoagulants for non-valvular atrial fibrillation.
wn to be feasible and acceptable to heart failure patients as compared to in-person functionality testing. This approach could be implemented into clinical care pathways for evaluation of heart failure patients, as well as adopted by industry-sponsored and investigator-initiated research studies in heart failure cohorts for data collection.
Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.

To reduce patient risk and cost to the healthcare system by improving the diagnostic yield of invasive coronary angiography through optimized outpatient selection.

Retrospective analysis of 12 years of referral data from a provincial cardiac registry, including all patients referred for invasive angiography of more than 1.4 million individuals in Ontario, Canada. Stable outpatients undergoing coronary angiography during the study period were included in the analysis. The training set (80% random sample, n = 23,750) was used to develop 8 prediction models in Python using grid-search cross-validation. The test set (20% random sample, n = 5938), evaluated thiography in stable, elective outpatients, thus improving patient safety and reducing healthcare costs.
Personalized treatment of atrial fibrillation (AF) risk factors using mHealth and telehealth may improve patient outcomes.

The purpose of this study was to assess the feasibility of the Atrial Fibrillation Helping Address Care with Remote Technology (AF-HEART) intervention on the following patient outcomes (1) heart rhythm tracking; (2) weight, alcohol, blood pressure (BP), and sleep apnea reduction; (3) AF symptom reduction; and (4) quality-of-life (QOL) improvement.

A total of 20 patients with AF undergoing antiarrhythmic therapy, cardioversion, and/or catheter ablation were enrolled and followed for 6 months. The AF-HEART intervention included remote heart rhythm, weight, and BP tracking; televisits with a dietician focusing on AF risk factors; and referrals for sleep apnea and hypertension treatment.

Patients transmitted a median of 181 rhythm recordings during the 6-month follow-up period. Patients lost an average of 3.5 kilograms at 6 months (
= .005). Patients had improved SF-12 scores (
= .01), AFSS score (
= .01), EQ-5D score (
=.006), and AFEQT Global Score (
= .03). There was significant correlation between weight loss and decrease in symptom severity (r= -0.45,
= .05), and between % weight loss and decrease in symptom severity (r = -0.49,
= .03).

This study described the feasibility of the AF-HEART intervention for (1) consistent remote tracking of heart rhythm, weight, and BP; (2) achievement of weight loss; (3) reduction of symptoms; and (4) improvement in QOL. Expansion to a larger randomized study is planned.
This study described the feasibility of the AF-HEART intervention for (1) consistent remote tracking of heart rhythm, weight, and BP; (2) achievement of weight loss; (3) reduction of symptoms; and (4) improvement in QOL. Expansion to a larger randomized study is planned.
Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features.

This study aimed to develop a novel deep learning solution for analysis of contrast-enhanced CMR images that produces anatomically accurate myocardium and scar/fibrosis segmentations and uses these to calculate features of clinical interest.

Data sources were 155 2-dimensional LGE-CMR patient scans (1124 slices) and 246 synthetic "LGE-like" scans (1360 slices) obtained from cine CMR using a novel style-transfer algorithm. We trained and tested a 3-stage neural network that identified the left ventricle (LV) region of interest (ROI), segmlinical measures. Given the training set heterogeneity, our approach could be extended to multiple imaging modalities and patient pathologies.
Point-of-care testing (POCT) has applications across medical specialties and holds promise to improve patient care. While cardiovascular medicine has been attractive for POCT applications in recent years, little is known about how cardiovascular health professionals perceive them.

The objective of our study was to examine differences in perceptions and attitudes towards POCTs between cardiovascular health professionals compared to other healthcare professionals.

We surveyed healthcare professionals to assess perceptions of POCT usage and their benefits and concerns between October 2019 and March 2020. Questions regarding POCT perceptions were assessed on a 5-point Likert scale.

We received a total of 148 survey responses; of the responders, 52% were male, 59% were physicians, and 50% worked in a hospital setting. We found that cardiology professionals were less likely, compared to other specialties, to view POCTs as improving patient management or reducing errors. These cardiology professionals were not constrained by resources or a lack of investment opportunities to implement these technologies.

This study provides a better understanding of perceptions about POCTs among healthcare specialists. To improve patient outcomes through the adoption and usage of POCTs, greater collaboration is advised among key industry and healthcare stakeholders.
This study provides a better understanding of perceptions about POCTs among healthcare specialists. To improve patient outcomes through the adoption and usage of POCTs, greater collaboration is advised among key industry and healthcare stakeholders.
The impact of medical-grade wearable electrocardiographic (ECG) recording technology is increasing rapidly. Awide range of different portable smartphone-connected ECG and heart rate trackers is available on the market. Smart ECG devices are especially valuable to monitor either supraventricular arrhythmias or prolonged QT intervals to avoid drug-induced life-threatening arrhythmias. However, frequent false alarms or false-positive arrhythmia results from wearable devices are unwanted. Therefore, for clinical evaluation, it should be possible to measure and evaluate the biosignals of the wearables independent of the manufacturer.

Unlike radiological devices that do support the universal digital imaging and communications in medicine standard, these medical-grade devices do not yet support a secure standardized exchange pathway between sensors, smartphones/smartwatches, and end services such as cloud storage or universal Web-based application programming interface (API) access. selleck chemicals llc Consequently, postprocessing of recorded ECGs or heart rate interval data requires a whole toolbox of customized software technologies.

Various methods for measuring and analyzing nonstandardized ECG and heart rate data are proposed, including online measurement of ECG waveforms within a PDF, access to data using manufacturer-specific software development kits, and access to biosignals using modern Web APIs.

With the appropriate workaround, modern software technologies such as JavaScript and PHP allow health care providers and researchers to easily and instantly access necessary and important signal measurements on demand.
With the appropriate workaround, modern software technologies such as JavaScript and PHP allow health care providers and researchers to easily and instantly access necessary and important signal measurements on demand.
A decade after the Health Information Technology for Economic and Clinical Health (HITECH) Act, electronic health records (EHRs) largely remain poorly designed and contribute to clinician burnout.

The purpose of this study was to understand clinicians' wants, needs, and perceived barriers imposed by the EHR; implement best practices in user-centered design; and create a clinician-centered EHR framework validated via a functional EHR prototype.

Usability evaluations were performed using a simulated patient with a complex clinical scenario. Convergent parallel mixed methods linked to action research and agile development were used to create an EHR prototype based on clinician-centered design. Prototype functionality was validated via a final usability evaluation.

Between 2015 and 2017, 53 clinicians from 8 cardiology practices (4 academic and 4 private) participated in initial evaluations of their installed EHR. In 2019, 25 clinicians participated in final evaluations of their EHR vs our EHR prototype. linician testing of the EHR prototype demonstrated it was significantly more useful and usable to clinicians, thus identifying a framework and pathway for substantive improvement of EHR systems.
Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success.

We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation.

Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods.

Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633,
= .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38,
= .09, I
= 98% favoring learning methods.

Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.
Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.
Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence-based methods can be used to develop personalized prediction models and improve early-warning systems.

The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy.

This international, multicenter, prospective, observational study consists of 2 phases (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing).
Website: https://www.selleckchem.com/products/marimastat.html