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无袖带血压监测:前景与挑战。

Cuffless Blood Pressure Monitoring: Promises and Challenges.

机构信息

Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

出版信息

Clin J Am Soc Nephrol. 2020 Oct 7;15(10):1531-1538. doi: 10.2215/CJN.03680320. Epub 2020 Jul 17.

DOI:10.2215/CJN.03680320
PMID:32680913
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7536750/
Abstract

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.

摘要

目前的血压测量是基于传统的血压袖带方法。动态血压监测通常在 24 小时内以 15 至 30 分钟的间隔进行,提供了足够连续的读数,优于基于办公室的快照,但该系统不适合频繁重复使用。一种真正的连续血压测量方法,可以被动且频繁地收集血压,需要一种无袖带方法,可以由患者佩戴,数据以与心率和心律相同的方式以电子方式存储。理想情况下,通过新型设备,在同一受试者的白天和夜间的各种活动中连续和频繁地测量血压。基于传感器和算法开发的测量血压的新方法引起了越来越多的关注。随着新设备的改进和准确性的提高,将有可能更好地评估隐匿性高血压、夜间高血压以及血压的严重程度和变异性。在这篇综述中,我们讨论了该领域的进展,特别是在过去 5 年中的进展,最后介绍了基于传感器的方法,这些方法结合了机器学习算法,以实现个性化医疗。