Mukkamala Ramakrishna, Shroff Sanjeev G, Kyriakoulis Konstantinos G, Avolio Alberto P, Stergiou George S
Department of Bioengineering (R.M., S.G.S.), University of Pittsburgh, PA.
Department of Anesthesiology and Perioperative Medicine (R.M.), University of Pittsburgh, PA.
Hypertension. 2025 Jun;82(6):957-970. doi: 10.1161/HYPERTENSIONAHA.125.24822. Epub 2025 Apr 15.
Cuffless blood pressure (BP) measurement offers considerable potential for clinical practice but is a challenging technological field. Many are investigating pulse wave analysis with or without pulse arrival time in which machine learning is applied to pulsatile waveforms obtained with mobile devices (eg, wristbands, smartphones) to estimate BP. These methods generally require individual user calibration with cuff BP measurements or demographics (eg, age, sex). This calibration makes it difficult to evaluate the method's accuracy, and many studies claiming accuracy used inadequate testing procedures. Yet, publications and regulatory-cleared devices continue to rise, seemingly implying technological advancements. An update is provided on the flurry of activity in cuffless BP technologies over the last 2 to 3 years, covering the clinical need, the latest devices, recent publications based on pulse wave analysis and pulse arrival time, progress in developing validation standards for cuffless BP devices, and recent publications on other cuffless BP measurement principles. Despite the high volume of research and development, to date, there is no compelling evidence that pulse wave analysis and pulse arrival time can provide significant added value in BP measurement accuracy beyond the cuff BP or demographic data for calibration. Thus, it is reasonable to at least be skeptical of published and future studies on pulse wave analysis and pulse arrival time for cuffless BP measurement with uncertain testing procedures. It is important to focus on establishing robust validation standards for cuffless BP devices requiring individual user calibration and also pursuing cuffless and calibration-free BP measurement methodologies going forward.
无袖带血压测量在临床实践中具有巨大潜力,但却是一个具有挑战性的技术领域。许多人正在研究有或没有脉搏到达时间的脉搏波分析,其中机器学习被应用于通过移动设备(如腕带、智能手机)获得的脉动波形来估计血压。这些方法通常需要使用袖带血压测量或人口统计学数据(如年龄、性别)对个体用户进行校准。这种校准使得评估该方法的准确性变得困难,而且许多声称具有准确性的研究使用的测试程序并不充分。然而,相关出版物和获得监管批准的设备仍在不断增加,这似乎意味着技术在进步。本文提供了过去两到三年无袖带血压技术一系列活动的最新情况,涵盖临床需求、最新设备、基于脉搏波分析和脉搏到达时间的近期出版物、无袖带血压设备验证标准制定的进展,以及关于其他无袖带血压测量原理的近期出版物。尽管进行了大量的研发工作,但迄今为止,没有令人信服的证据表明脉搏波分析和脉搏到达时间在血压测量准确性方面能够提供超出袖带血压或用于校准的人口统计学数据的显著附加值。因此,对于那些测试程序不确定的关于无袖带血压测量的脉搏波分析和脉搏到达时间的已发表及未来研究,至少持怀疑态度是合理的。重要的是要专注于为需要个体用户校准的无袖带血压设备建立稳健的验证标准,并且还要继续探索无袖带且无需校准的血压测量方法。
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