Publications by authors named "D A Playford"
Background: Although the prognostic implications of severe mitral regurgitation (MR) are well recognised, they are less clear in moderate MR. We therefore explored the prognostic impact of both moderate and severe MR within the large National Echocardiography Database Australia cohort.
Methods: Echocardiography reports from 608 570 individuals were examined using natural language processing to identify MR severity and leaflet pathology.
Article Synopsis
- * In a cohort of over 21,000 adults, the AI-AAS identified significantly more cases of severe AS than initial clinical evaluations, highlighting a gender bias in diagnosis and treatment referrals.
- * Results show that while 54.4% of undiagnosed severe AS cases were women, the AI-AAS could help ensure that more patients, particularly women, receive necessary interventions for this potentially life-threatening condition.
Article Synopsis
- The study investigates the prevalence and impact of pulmonary hypertension (PHT) in adults with left ventricular diastolic dysfunction (LVDD) and preserved ejection fraction, using a large database of 16,058 patients.
- Findings indicate that nearly 84% of subjects had some form of PHT, correlating higher tricuspid regurgitation velocity (TRV) levels with increased mortality rates over 1 and 5 years.
- The research highlights that the risk of death increases significantly with higher TRV levels, establishing a clear prognostic relationship between PHT severity and mortality, particularly noted at TRV levels above 2.9 m/s.
Article Synopsis
- - The study aimed to examine how a 'mitral-specific' cardiac damage score (m-CDS) relates to survival outcomes in patients with mitral regurgitation (MR) and compared it with an 'aortic-specific' cardiac damage score (a-CDS) using data from a large Australian database.
- - Out of over 620,000 adults analyzed through echocardiography, about 17,658 (3.1%) had moderate or severe MR, and a subset of 5,000 patients was tested to evaluate the effectiveness of various CDS models in predicting all-cause mortality over an average follow-up of 3.8 years.
- - The research revealed that higher m-CDS stages were linked to
Background: Identifying individuals with severe aortic stenosis (AS) at high risk of mortality remains challenging using current clinical imaging methods.
Objectives: The purpose of this study was to evaluate an artificial intelligence decision support algorithm (AI-DSA) to augment the detection of severe AS within a well-resourced health care setting.
Methods: Agnostic to clinical information, an AI-DSA trained to identify echocardiographic phenotype associated with an aortic valve area (AVA)<1 cm using minimal input data (excluding left ventricular outflow tract measures) was applied to routine transthoracic echocardiograms (TTE) reports from 31,141 U.