使用连续单导联心电图分析仪预测需要启动快速反应小组的病人病情恶化情况。

PLOS digital health Pub Date : 2024-10-24 eCollection Date: 2024-10-01 DOI:10.1371/journal.pdig.0000465
Sooin Lee, Bryce Benson, Ashwin Belle, Richard P Medlin, David Jerkins, Foster Goss, Ashish K Khanna, Michael A DeVita, Kevin R Ward
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引用次数: 0

摘要

尽管通过更好的生命体征监测和快速反应小组(RRT)的启动可以更早地对患者做出反应,但识别患者病情恶化的起始时间仍具有挑战性。在这项研究中,将基于心电图软件的医疗设备--血流动力学不稳定性预测指数分析仪(AHI-PI)与心率、血压和呼吸频率等生命体征进行了比较,以评估在启动 RRT 之前,AHI-PI 能多早显示风险。与生命体征(41.67%)相比,AHI-PI(92.71%)能更早地提示风险。AHI-PI 提示风险的时间较早,平均比 RRT 事件早一天以上。在 AHI-PI 和生命体征均可提示风险的事件中,AHI-PI 比生命体征更早地识别出病情恶化。一项病例对照研究显示,需要 RRT 的情况比不需要 RRT 的情况更有可能出现 AHI-PI 风险提示。该研究得出了一些见解,支持 AHI-PI 作为临床决策支持系统的功效。研究结果表明,AHI-PI 有可能成为未来 RRT 事件的可靠预测指标。它有可能帮助临床医生识别早期临床恶化,并对生命体征未注意到的情况做出反应,从而帮助临床医生改善临床预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI's efficacy as a clinical decision support system. The findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.

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