Background: The implementation of nonphysician-led exercise stress testing (EST) has increased over the last 30 years, with endorsement by many cardiovascular societies around the world. The comparable safety of nonphysician-led EST to physician-led studies has been demonstrated, with some studies also showing agreement in diagnostic preliminary interpretations.

Objective: The study aim was to firstly confirm the safety of nonphysician-led EST in a large cohort and secondly compare the interobserver agreement and diagnostic accuracy of cardiac scientist and junior medical officer (JMO)-led EST reports to cardiology consultant overreads.

Methods: All ESTs performed between 1/7/2010 and 30/6/2013 were included in the study for JMO led tests (n = 1332). ESTs performed for the investigation of coronary artery disease between 1/7/2013 and 30/6/2016 were included for scientist-led testing (n = 1904).

Results: There was one adverse event, an ST segment myocardial infarction during the recovery phase of a JMO-led EST. Interobserver agreement was superior between the cardiologist and the scientist compared with the cardiologist and the JMO (P < 0.0001). Sensitivity for JMO-led tests differed from the cardiologist overread (86.96% vs. 96.77%, P = 0.03). There were no other significant differences between the cardiologist overread and the JMO- or scientist-led interpretation.

Conclusions: Scientist-led EST is safe in intermediate risk patients and their preliminary reports are equally diagnostic as cardiologist overreads. While JMO-led ESTs are just as safe, the preliminary reports differ significantly from cardiologist overread particularly with respect to sensitivity.

Download full-text PDF

Source
http://dx.doi.org/10.1097/HPC.0000000000000193DOI Listing

Publication Analysis

Top Keywords

cardiologist overread
12
safety nonphysician-led
8
nonphysician-led est
8
agreement diagnostic
8
interobserver agreement
8
jmo-led est
8
ests performed
8
preliminary reports
8
cardiologist
7
est
6

Similar Publications

Objectives: Out of Hospital Cardiac Arrest (OHCA) is a frequently encountered pathology with resultant poor outcomes in the majority of patients. Echocardiography has been utilized to help guide clinical decision making and monitor effectiveness of resuscitative efforts. Transthoracic echocardiography (TTE) the mainstay of point-of-care ultrasound (POCUS) real time resuscitative imaging has limitations, most notably is the disruption of closed chest compressions.

View Article and Find Full Text PDF

Background: Handheld single-lead electrocardiographic (1L ECG) devices are increasingly used for atrial fibrillation (AF) screening, but their real-world performance is not well understood.

Objectives: The purpose of this study was to quantify the diagnostic test characteristics of 1L ECG automated interpretations for prospective AF screening.

Methods: We calculated the diagnostic test characteristics of the AliveCor KardiaMobile 1L ECG (AliveCor, US) algorithm using unblinded cardiologist overread as the gold standard using single 30s tracings administered by medical assistants among individuals aged ≥65 years participating in the VITAL-AF trial (NCT03515057) of population-based AF screening embedded within routine primary care.

View Article and Find Full Text PDF

Aims: Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.

View Article and Find Full Text PDF

Introduction: Non-gated, non-contrast computed tomography (CT) scans are commonly ordered for a variety of non-cardiac indications, but do not routinely comment on the presence of coronary artery calcium (CAC)/atherosclerotic cardiovascular disease (ASCVD) which is known to correlate with increased cardiovascular risk. Artificial intelligence (AI) algorithms can help detect and quantify CAC/ASCVD which can lead to early treatment and improved outcomes.

Methods: Using an FDA-approved algorithm (NANOX AI) to measure coronary artery calcium (CAC) on non-gated, non-contrast CT chest, 536 serial scans were evaluated in this single-center retrospective study.

View Article and Find Full Text PDF

Background: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!