基于远程电子听诊器平台RESP的慢性咳嗽诊断算法逐步验证程序

M. Glass, J. Kroh, T. Nguyen, D. Piao, S. Cannon-Bailey
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引用次数: 0

摘要

理由:长期关注慢性咳嗽,定义为每天咳嗽8至12周(成人)或4周(儿童)。对咳嗽的兴趣最初是基于病因和治疗反应的区分,以及在排除其他主要病因的情况下提出的难治性慢性咳嗽的诊断。也许并非巧合的是,随着第一代P2X3抑制剂的临床发展,咳嗽计数作为评估治疗反应(必要和充分)的有效方法的融合,导致了许多以患者为导向的设备的出现,这些设备声称可以计数咳嗽。“新冠肺炎咳嗽”和“后新冠肺炎咳嗽”的出现,让人们对咳嗽的兴趣更加浓厚。方法:采用Strados Labs RESPTM进行咳嗽计数验证。根据自动化程度的提高,提出了三种连续记录咳嗽计数的方法。每种经过验证的方法都被证实在金标准记录的真实咳嗽计数的+/- 10%以内。每种方法使用>6例患者(>48小时)的连续记录进行验证。咳嗽次数由训练有素的专业人员注释,然后由临床专业人员审查。这是咳嗽计数的黄金标准。咳嗽计数方法:多名训练有素的贴标员听录音,并就声音是否为咳嗽达成一致。同意的咳嗽次数加在一起,以确定总咳嗽次数。2.一种高度敏感的机器学习算法会突出显示可能是咳嗽的样本。多名训练有素的贴标员听录音,并同意是否咳嗽。3.全自动机器学习算法。结果:与金标准相比,高灵敏度方法灵敏度>98%,全自动机器学习算法特异性>90%,准确率>90%,报告咳嗽次数。结论:本文所示的过程对于验证使用RESP收集的咳嗽数据是必要且充分的。然而,与其他方法不同的是,除了咳嗽计数外,RESP还提供了重要的定量和定性数据。RESP提供频谱图和胸壁运动以及档案记录,以便随着数据集和科学的扩展,将咳嗽解析为单次,多次和痉挛可以重新分类。可以评估咳嗽结构,以区分胸内咳嗽和胸外咳嗽,吸气咳嗽和呼气咳嗽,以及实时对治疗的反应,并增加解剖学上的确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stepwise Validation Program for Developing the Algorithm for Chronic Cough with Remote e-stethoscope Platform RESP
RATIONALE: There has been a sustained interest in chronic cough, defined as 8 to 12 weeks of daily coughing (adults) or 4 weeks (children). The interest was initially driven by discrimination of cough based on etiology and response to therapy, and the proposed diagnosis of refractory chronic cough when other major etiologies have been excluded. Perhaps not coincidentally, the convergence of cough counting as a valid method of assessing response to therapy (necessary and sufficient) with the clinical development of first generation P2X3 inhibitors has led to the emergence of a number of patient-oriented devices that claim to count coughs. The emergence of “COVID cough” and “post COVID cough” have heightened the interest in cough. METHODS: The Strados Labs RESPTM has been validated to count coughs. Presented are three methods to count coughs in a continuous recording, in order of increased automation. Each validated method has been confirmed to be within +/- 10% of the true cough count for the gold standard recordings. Each method is validated using >6 patients (>48 hours) of continuous recordings. Number of coughs are annotated by trained professionals and are then reviewed by clinical professionals. This is the gold standard cough count. Cough Counting Methods:1.Multiple trained labelers listen to the recordings and agree on if a sound is a cough or not. The number of agreed upon coughs are added together to determine a total cough count. 2.A highly sensitive machine learning algorithm highlights samples that have a probable likelihood of a cough. Multiple trained labelers listen to recordings and agree if a cough or not. 3.Fully automated machine learning algorithm. RESULTS: In comparison with the gold standard, the highly sensitive method was (>98% sensitivity, and the fully automated machine learning algorithm is >90% specific and >90% accurate and reports the number of coughs. CONCLUSIONS: The process shown here is both necessary and sufficient to validate cough data collection using the RESP. However, unlike other approaches, the RESP provides significant quantitative and qualitative data beyond counting coughs. The RESP provides spectrograms and chest wall motion as well as archival recordings so that parsing coughs into single, multiple and spasms can be reclassified as the data set and science expand. Cough architecture can be evaluated to differentiate intrathoracic from extra-thoracic cough, cough onset in inspiration versus expiration, and response to therapy in real time and with increased anatomic certainty.
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