Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools

IF 8.8 1区医学 Q1 CRITICAL CARE MEDICINE

Critical Care Pub Date : 2024-12-05 DOI:10.1186/s13054-024-05140-6

Andrew J. E. Seely, Kimberley Newman, Rashi Ramchandani, Christophe Herry, Nathan Scales, Natasha Hudek, Jamie Brehaut, Daniel Jones, Tim Ramsay, Doug Barnaby, Shannon Fernando, Jeffrey Perry, Sonny Dhanani, Karen E. A. Burns

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引用次数: 0

Abstract

Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring. We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring. Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial). The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

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监测发展路线图：开发和评估基于波形变异性的人工智能驱动的预测临床决策支持软件工具

连续波形监测是危重患者的标准护理措施。心率、呼吸频率和血压变异性由波形得出，包含有用的诊断和预后信息；当与机器学习相结合时，可以提供与疾病严重程度和/或生理储备减少有关的预测指标。将预测模型集成到临床决策支持软件（CDSS）工具中代表了监测的潜在演变。我们对开发和严格评估基于监测的预测性临床决策支持工具所需的多学科步骤进行了回顾和分析。基于波形的变异性预测模型的开发和评估涉及多步骤、多学科的方法。分步过程涉及数据科学（数据收集，波形处理，变异性分析，统计分析，机器学习，预测建模），CDSS开发（迭代研究原型演变为商业工具）和临床研究（观察和干预性实施研究，随后是可行性，然后是明确的随机对照试验），并提出了独特的挑战(包括技术，分析，心理，监管和商业)。拟议的路线图为开发和评估新的预测性CDSS工具提供了指导，这些工具有可能帮助改变监测和改善护理。

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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来源期刊

Critical Care 医学-危重病医学

CiteScore

20.60

自引率

3.30%

发文量

348

审稿时长

1.5 months

期刊介绍： Critical Care is an esteemed international medical journal that undergoes a rigorous peer-review process to maintain its high quality standards. Its primary objective is to enhance the healthcare services offered to critically ill patients. To achieve this, the journal focuses on gathering, exchanging, disseminating, and endorsing evidence-based information that is highly relevant to intensivists. By doing so, Critical Care seeks to provide a thorough and inclusive examination of the intensive care field.