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Validation of a cuff-based device for measuring carotid-femoral pulse wave velocity in children and adolescents.

Tommy Y CaiAlice MeroniHasthi DissanayakeMelinda PhangAlberto AvolioDavid S CelermajerMark ButlinMichael R SkiltonAhmad Qasem
Published in: Journal of human hypertension (2019)
Carotid-femoral pulse wave velocity is associated with arterial stiffness in major elastic arteries, and predicts future cardiovascular events. However, little is known about carotid-femoral pulse wave velocity as a marker of vascular health in children. Semi-automated cuff-based devices for assessing pulse wave velocity are increasingly popular, although these utilize an algorithm developed and validated in adults. Physiological differences between adults and children may, however, reduce the accuracy of cuff-based methods. We sought to determine the accuracy of a cuff-based pulse wave velocity device in healthy children, and determine whether a novel age-appropriate algorithm increases accuracy. We recruited 29 healthy children between the ages of 2 and 20 years. Pulse wave velocity was measured both by using a tonometer on the carotid artery and an inflated cuff on the thigh, and using a tonometer on both the carotid artery and femoral artery as a reference standard. Accuracy of the cuff-based device with its standard algorithm developed in adults, and a novel age-appropriate algorithm corrected for physiological differences in leg pulse wave velocity was assessed with Regression analysis and Bland-Altman plots. Cuff-based device estimates of pulse wave velocity had excellent agreement to the reference standard (Δ = -0.26 ms-1 [SD 0.44]). The novel age-appropriate algorithm improved the accuracy of the cuff-based method (Δ = 0.02 ms-1 [SD 0.44]). The cuff-based semi-automatic approach estimates carotid-femoral pulse wave velocity with excellent agreement to the reference standard. However, adjusting the algorithm for known differences in leg pulse wave velocity further improves the accuracy of cuff-based measurement in children and adolescents.
Keyphrases
  • blood pressure
  • blood flow
  • machine learning
  • deep learning
  • cardiovascular events
  • young adults
  • healthcare
  • public health
  • ms ms
  • neural network
  • type diabetes
  • current status
  • data analysis