ABSTRACT
Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.
ABSTRACT
A number of recent community-based epidemiologic studies suggest that 40% to 50% of the cases of heart failure have preserved left ventricular systolic function. Although diastolic heart failure is often not well clinically recognized, it is associated with marked increases in morbidity and all-cause mortality. Doppler echocardiography has emerged as the principal clinical tool for the assessment of left ventricular diastolic function. Doppler mitral inflow velocity-derived variables remain the cornerstone of the evaluation of diastolic function. Pulmonary venous Doppler flow indices and mitral inflow measurements with Valsalva's maneuver are important adjuncts for differentiating normal and pseudonormal mitral inflow patterns. Unfortunately, these Doppler flow variables are significantly influenced by loading conditions and, therefore, the results from these standard techniques can be inconclusive. Recently, color M-mode and Doppler tissue imaging have emerged as new modalities that are less affected by preload and, thus, provide a strong complementary role in the assessment of diastolic function. This review will discuss the diastolic properties of the left ventricle, Doppler echocardiographic evaluation, and grading of diastolic dysfunction.