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Evaluation of the Accuracy of Cuffless Blood Pressure Measurement Devices: Challenges and Proposals.

Ramakrishna MukkamalaMohammad YavarimaneshKeerthana NatarajanJin-Oh HahnKοnstantinos G KyriakoulisAlberto P AvolioGeorge S Stergiou
Published in: Hypertension (Dallas, Tex. : 1979) (2021)
Several novel cuffless wearable devices and smartphone applications claiming that they can measure blood pressure (BP) are appearing on the market. These technologies are very attractive and promising, with increasing interest among health care professionals for their potential use. Moreover, they are becoming popular among patients with hypertension and healthy people. However, at the present time, there are serious issues about BP measurement accuracy of cuffless devices and the 2021 European Society of Hypertension Guidelines on BP measurement do not recommend them for clinical use. Cuffless devices have special validation issues, which have been recently recognized. It is important to note that the 2018 Universal Standard for the validation of automated BP measurement devices developed by the American Association for the Advancement of Medical Instrumentation, the European Society of Hypertension, and the International Organization for Standardization is inappropriate for the validation of cuffless devices. Unfortunately, there is an increasing number of publications presenting data on the accuracy of novel cuffless BP measurement devices, with inadequate methodology and potentially misleading conclusions. The objective of this review is to facilitate understanding of the capabilities and limitations of emerging cuffless BP measurement devices. First, the potential and the types of these devices are described. Then, the unique challenges in evaluating the BP measurement accuracy of cuffless devices are explained. Studies from the literature and computer simulations are employed to illustrate these challenges. Finally, proposals are given on how to evaluate cuffless devices including presenting and interpreting relevant study results.
Keyphrases
  • blood pressure
  • healthcare
  • systematic review
  • type diabetes
  • metabolic syndrome
  • deep learning
  • heart rate
  • skeletal muscle
  • climate change
  • weight loss
  • artificial intelligence
  • hypertensive patients
  • big data