- Pretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.
- Condensed abstract Data regarding the comparison of diagnostic accuracy of TEE and CCT for diagnosing IE are limited. The present meta-analysis compares the diagnostic performance of the two imaging modalities for a variety of complications of IE in the same patient populations. Our results show that both TEE and CCT have good diagnostic accuracy, with TEE showing superiority in detecting leaflet defects and CCT performing better in prosthetic valve endocarditis. CCT also showed a trend towards higher sensitivity than TEE for detection of periannular complications. These findings suggest that CCT is a useful adjunct to TEE for IE, whenever appropriate use of complementary imaging modalities is warranted.
- The present study evaluated the impact of deep-learning image reconstruction (DLIR) on noise, image quality, and diagnostic accuracy. In 43 patients who underwent clinically indicated coronary CT angiography and invasive coronary angiography, image quality was improved by up to 62% at similar noise levels. In addition, DLIR-H yielded the highest noise reduction (up to 43%) and best image quality (improvement of up to 138%). More importantly, sensitivity (92% vs. 88%), specificity (73% vs. 73%) and diagnostic accuracy (82% vs. 80%) of DLIR were at least non-inferior to ASiR-V.