Hypotension prediction index: From reactive to predictive hemodynamic management, the key to maintaining hemodynamic stability

J. Ripollés-Melchor, A. Ruiz-Escobar, Paula Fernández-Valdes-Bango, J. V. Lorente, I. Jiménez-López, A. Abad-Gurumeta, Laura Carrasco-Sánchez, M. Monge-García
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Abstract

Intraoperative hypotension is common and has been associated with adverse events, including acute kidney failure, myocardial infarction, and stroke. Since blood pressure is a multidimensional and measurable variable, artificial intelligence and machine learning have been used to predict it. To date, studies have shown that the prediction and prevention of hypotension can reduce the incidence of hypotension. This review describes the development and evaluation of an artificial intelligence predictive algorithm called Hypotension Prediction (HPI), which can predict hypotension up to 15 min before it occurs.
低血压预测指标:从反应性血流动力学管理到预测性血流动力学管理,是维持血流动力学稳定的关键
术中低血压是常见的,并与不良事件相关,包括急性肾衰竭、心肌梗死和中风。由于血压是一个多维的、可测量的变量,人工智能和机器学习已经被用来预测它。迄今为止,已有研究表明,对低血压的预测和预防可以降低低血压的发生率。本文介绍了一种名为低血压预测(HPI)的人工智能预测算法的开发和评估,该算法可以在低血压发生前15分钟预测到低血压。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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