In:
Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 146, No. Suppl_1 ( 2022-11-08)
Kurzfassung:
Introduction: Despite obvious diagnostic usefulness, left ventricular (LV) apical sparing has not been widely accepted to diagnose transthyretin amyloid cardiomyopathy, because it is time consuming and requires a level of expertise. Automatic assessment may be the solution for these problems. Methods and Results: We enrolled 69 patients aged ≥ 70 years who underwent 99m TC-PYP scintigraphy and transthoracic echocardiography (TTE) by EPIQ7G, and had enough information for two-dimensional speckle tracking echocardiography at Kumamoto University Hospital from January 2016 to December 2019. LV apical sparing was diagnosed as relative apical longitudinal strain (LS) index (RapLSI) 〉 1.0. Measurement of LS was repeated using the same apical images with three different measurement packages as follows: (1) full-automatic assessment, (2) semi-automatic assessment, and (3) manual assessment. The calculation time for full-automatically assessment (14.8±1.5 sec/patient) and semi-automatically assessment (67.3±14.1 sec/patient) were significantly shorter than for manual assessment (211.1±60.5 sec/patient) (p 〈 0.01 for both). Receiver operating characteristic curve analysis showed that the area under curve (AUC) of the RapLSI evaluated by full-automatically assessment for 99m Tc-PYP scintigraphy positivity was 0.656 (best cut-off point; 1.12 [sensitivity 63%, specificity 68%]), by semi-automatic assessment was 0.806 (best cut-off point; 1.00 [sensitivity, 61%; specificity, 100%] ) and by manual assessment was 0.806 (best cut-off point; 1.00 [sensitivity, 71%; specificity, 96%]). Conclusion: The diagnostic accuracy of semi-automatically assessed RapLSI to predict 99m Tc-PYP scintigraphy positivity was as same as that of manual assessed RapLSI. Semi-automatically approach using novel automated software was useful to diagnose ATTR-CM in the terms of rapidity, reproducibility and diagnostic accuracy.
Materialart:
Online-Ressource
ISSN:
0009-7322
,
1524-4539
DOI:
10.1161/circ.146.suppl_1.12457
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2022
ZDB Id:
1466401-X
