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Assessing Automatic Plethysmographic Ankle-Brachial Index Devices in Peripheral Artery Disease Detection: A Comparative Study with Doppler Ankle-Brachial Index Measurements.

Aleksandra DanielukAnna KamieńskaSlawomir Chlabicz
Published in: Medical science monitor : international medical journal of experimental and clinical research (2023)
BACKGROUND The ankle-brachial index (ABI) is a critical diagnostic test for peripheral artery disease (PAD), albeit requiring technical expertise and dedicated resources. The advent of automatic ABI devices proposes a more accessible approach, necessitating fewer resources and less expertise. This study was conducted to gather data on PAD prevalence and to evaluate the correlation and efficacy of automatic ABI measurements vs traditional Doppler ABI measurements to understand their potential role in primary care settings. MATERIAL AND METHODS ABI measurements were obtained using both the Doppler method and an automatic plethysmographic device (Dopplex ABility, Huntleigh Healthcare). RESULTS Of the 290 participants (mean age 67.6±7.4 years), Doppler ABI method identified 16.8% with abnormal results (<0.9), while the automatic method identified only 5.9%. The mean Doppler ABI was 1.05±0.15, and the mean automatic ABI was 1.12±0.13. The sensitivity of the automatic ABI measurements was 22.2%, and the specificity was 96.8%, with a positive predictive value of 57.1%, and a negative predictive value of 86.9%. Adjustments in the automatic assessment and inclusion of pulse wave velocity enhanced the diagnostic capabilities of the automatic ABI device. CONCLUSIONS While the automatic plethysmographic ABI device may lack the necessary diagnostic capabilities to replace the traditional Doppler ABI device as a standalone test in PAD diagnosis, it could still offer significant value in primary care settings if integrated with adjusted cut-off points and pulse wave velocity analysis.
Keyphrases
  • deep learning
  • peripheral artery disease
  • machine learning
  • primary care
  • blood flow
  • healthcare
  • neural network
  • blood pressure
  • risk factors
  • health insurance