Artificial intelligence driven clustering of blood pressure profiles reveals frailty in orthostatic hypertension.

IF 2.6 4区 医学 Q2 PHYSIOLOGY Experimental Physiology Pub Date : 2024-11-11 DOI:10.1113/EP091876
Claire M Owen, Jaume Bacardit, Maw P Tan, Nor I Saedon, Choon-Hian Goh, Julia L Newton, James Frith
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Abstract

Gravity, an invisible but constant force , challenges the regulation of blood pressure when transitioning between postures. As physiological reserve diminishes with age, individuals grow more susceptible to such stressors over time, risking inadequate haemodynamic control observed in orthostatic hypotension. This prevalent condition is characterized by drops in blood pressure upon standing; however, the contrary phenomenon of blood pressure rises has recently piqued interest. Expanding on the currently undefined orthostatic hypertension, our study uses continuous non-invasive cardiovascular data to explore the full spectrum of blood pressure profiles and their associated frailty outcomes in community-dwelling older adults. Given the richness of non-invasive beat-to-beat data, artificial intelligence (AI) offers a solution to detect the subtle patterns within it. Applying machine learning to an existing dataset of community-based adults undergoing postural assessment, we identified three distinct clusters (iOHYPO, OHYPO and OHYPER) akin to initial and classic orthostatic hypotension and orthostatic hypertension, respectively. Notably, individuals in our OHYPER cluster exhibited indicators of frailty and sarcopenia, including slower gait speed and impaired balance. In contrast, the iOHYPO cluster, despite transient drops in blood pressure, reported fewer fallers and superior cognitive performance. Surprisingly, those with sustained blood pressure deficits outperformed those with sustained rises, showing greater independence and higher Fried frailty scores. Working towards more refined definitions, our research indicates that AI approaches can yield meaningful blood pressure morphologies from beat-to-beat data. Furthermore, our findings support orthostatic hypertension as a distinct clinical entity, with frailty implications suggesting that it is worthy of further investigation.

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人工智能驱动的血压曲线聚类揭示了正性高血压的脆弱性。
重力是一种无形但恒定的力量,在变换姿势时对血压的调节提出了挑战。随着年龄的增长,人的生理储备会逐渐减少,随着时间的推移,人更容易受到这种压力的影响,从而有可能导致血流动力学控制不足,出现正性低血压。这种常见病的特点是站立时血压下降;然而,血压上升的相反现象最近引起了人们的兴趣。我们的研究扩展了目前尚未定义的正位性高血压,使用连续的无创心血管数据来探索社区老年人的血压状况及其相关的虚弱结果。鉴于无创心跳数据的丰富性,人工智能(AI)为检测其中的微妙模式提供了一种解决方案。将机器学习应用于现有的社区成人体位评估数据集,我们发现了三个不同的群组(iOHYPO、OHYPO 和 OHYPER),分别类似于初始和典型的正位性低血压和正位性高血压。值得注意的是,OHYPER 群组中的个体表现出虚弱和肌肉疏松的指标,包括步速减慢和平衡能力受损。相比之下,iOHYPO 组尽管血压短暂下降,但摔倒者较少,认知能力较强。令人惊讶的是,血压持续下降者的表现优于血压持续升高者,他们表现出更强的独立性和更高的弗里德虚弱评分。我们的研究表明,人工智能方法可以从逐次搏动的数据中得出有意义的血压形态,并努力实现更精细的定义。此外,我们的研究结果还支持将正压性高血压作为一个独特的临床实体,其对虚弱的影响表明它值得进一步研究。
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来源期刊
Experimental Physiology
Experimental Physiology 医学-生理学
CiteScore
5.10
自引率
3.70%
发文量
262
审稿时长
1 months
期刊介绍: Experimental Physiology publishes research papers that report novel insights into homeostatic and adaptive responses in health, as well as those that further our understanding of pathophysiological mechanisms in disease. We encourage papers that embrace the journal’s orientation of translation and integration, including studies of the adaptive responses to exercise, acute and chronic environmental stressors, growth and aging, and diseases where integrative homeostatic mechanisms play a key role in the response to and evolution of the disease process. Examples of such diseases include hypertension, heart failure, hypoxic lung disease, endocrine and neurological disorders. We are also keen to publish research that has a translational aspect or clinical application. Comparative physiology work that can be applied to aid the understanding human physiology is also encouraged. Manuscripts that report the use of bioinformatic, genomic, molecular, proteomic and cellular techniques to provide novel insights into integrative physiological and pathophysiological mechanisms are welcomed.
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