背景:慢性阻塞性肺疾病急性加重(AECOPD)与高死亡率相关,发病率,生活质量差,对患者和医疗保健系统构成沉重负担。迫切需要新的方法来预防或降低AECOPD的严重程度。国际上,这促使人们对远程患者监护(RPM)和数字医疗的潜力产生了更大的兴趣.RPM是指患者报告结果的直接传输,生理,和功能数据,包括心率,体重,血压,氧饱和度,身体活动,和肺功能(肺活量测定),通过自动化直接向医疗保健专业人员提供服务,基于Web的数据输入,或基于电话的数据输入。机器学习有可能通过提高AECOPD预测系统的准确性和精度来提高慢性阻塞性肺疾病的RPM。
目的:本研究旨在进行双重系统评价。第一篇综述集中于将RPM用作治疗或改善AECOPD的干预措施的随机对照试验。第二篇综述研究了将机器学习与RPM相结合来预测AECOPD的研究。我们回顾了RPM和机器学习背后的证据和概念,并讨论了它们的优势。局限性,和可用系统的临床使用。我们已经生成了提供患者和医疗保健系统福利所需的建议列表。
方法:全面的搜索策略,包括Scopus和WebofScience数据库,用于确定相关研究。共有2名独立审稿人(HMGG和CM)进行了研究选择,数据提取,和质量评估,通过协商一致解决差异。数据综合涉及使用关键评估技能计划清单和叙述性综合进行证据评估。报告遵循PRISMA(系统审查和荟萃分析的首选报告项目)指南。
结果:这些叙述性综合显示,57%(16/28)RPM干预的随机对照试验未能达到AECOPD患者更好结局所需的证据水平。然而,将机器学习集成到RPM中证明了提高AECOPD预测准确性的前景,因此,早期干预。
结论:这篇综述表明了将机器学习整合到RPM中预测AECOPD的过渡。我们讨论了具有改善AECOPD预测潜力的特定RPM指标,并强调了有关患者因素和RPM持续采用的研究空白。此外,我们强调对与RPM相关的患者和医疗保健负担进行更全面检查的重要性,随着实际解决方案的发展。
BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (
RPM) and digital medicine.
RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance
RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.
OBJECTIVE: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with
RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits.
METHODS: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
RESULTS: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention.
CONCLUSIONS: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of
RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.