Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid Stoffw Microb

Buschmann, EE; Li, L; Brix, M; Zietzer, A; Hillmeister, P; Busjahn, A; Bramlage, P; Buschmann, I.
A novel computer-aided diagnostic approach for detecting peripheral arterial disease in patients with diabetes.
PLOS ONE. 2018; 13(6): e0199374-e0199374. Doi: 10.1371/journal.pone.0199374 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG


Führende Autor*innen der Med Uni Graz
Buschmann Eva Elina

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Peripheral arterial disease (PAD) is an important manifestation of systemic atherosclerosis, with diabetes being one of its most significant risk factors. Owing to medial arterial calcification (MAC), the ankle-brachial index (ABI) is not always a reliable tool for detecting PAD. Arterial Doppler flow parameters, such as systolic maximal acceleration (ACCmax) and relative pulse slope index (RPSI), may serve as effective surrogates to detect stenosis-induced flow alteration. In the present study, ACCmax and RPSI were prospectively evaluated in 166 patients (304 arteries) with clinical suspicion of PAD, including 76 patients with and 90 patients without diabetes. In the overall sample, the sensitivity of ACCmax (69%) was superior to that of ABI (58%) and RPSI (56%). In patients with diabetes, the sensitivity of ACCmax (57%), ABI (56%) and RPSI (57%) were similar, though a parallel test taking both ACCmax and RPSI into account further increased sensitivity to 68%. The specificity (98%) and accuracy (78%) of ACCmax were superior to those of ABI (83% and 70%, respectively), as were the specificity (95%) and accuracy (77%) of RPSI in patients with diabetes. The diagnostic properties of ACCmax and RPSI were superior to those of ABI for detecting PAD in patients with diabetes. Our acceleration algorithm (Gefäßtachometer®) provides a rapid, safe, noninvasive tool for identifying PAD in patients with diabetes.

© Med Uni Graz Impressum