Abstract
The Ankle Brachial Index (ABI) is the most commonly used method for the investigation of peripheral artery disease (PAD) in clinical settings, but has a low sensitivity for detecting PAD in asymptomatic patients. It is calculated only based on the systolic blood pressure in the limbs and ignores the shape of the pressure waves. Therefore, we investigated a novel method focusing on waveform changes by analyzing and comparing synchronous pulse wave recordings of all four extremities.
In this study, pulse waves of 44 patients (28M/16F, age 65 (SD 10) years, ABI 0.89 (SD 0.13)) measured at rest on all four limbs simultaneously using the AngE Pro 4 system (Sonotechnik Austria Angio Experience GmbH, Austria) were investigated. The patients Fontaine stage was used as a benchmark. A novel parameter based on pairwise wave shape differences, i.e., the root mean square of the differences (RMSD), was tested for its capability to correctly classify the patients Fontaine stage. Furthermore, the results were compared and combined with ABI measurements.
Of the 44 patients, 7 were in Fontaine stage 0 (no PAD) and 37 in stage 2 (claudication). In univariate analysis, ABI showed significant discrimination power (AUC ROC = 0.86, p<0.01), but was surpassed by the RMSD with a discrimination power beyond ABI (AUC ROC = 0.95, p<0.001, see figure). Furthermore, logistic regression revealed statistical independence of ABI and RMSD, indicating that they reflect different aspects of the measurements. Therefore, combining these two measures, multivariate classification results in a far superior classifier (AUC ROC = 0.98, p<0.001, see figure).
In this study, the novel waveshape-based method RMSD, used to classify stages of PAD, showed better discrimination power than ABI. Furthermore, statistical independence allowed for the combination of ABI and RMSD, resulting in a far superior classification of PAD Fontaine stages. Since, using the appropriate equipment, both parameters can be measured simultaneously and in an automated way, the results are promising for assisting PAD classification in the future.
Acknowledgement/Funding
This work was partly supported by the program “Bridge” by the Austrian Ministry for Transport, Innovation and Technology BMVIT. PWA4PAD FFG No. 858509