Instrumental screening for dysphagia by combining high-resolution cervical auscultation with advanced data analysis tools to identify silent dysphagia and silent aspiration

摘要 吞咽困难(吞咽障碍)每年导致近150,000人住院，超过220,000人额外住院 住院天数减少，住院天数延长40%。吞咽困难的风险通常通过 主观筛查方法和通过筛查确定的方法接受金标准成像测试 例如视频透视(VF)。然而，筛查方法高估或低估了风险，完全无法 确定有可能导致肺炎和其他不良反应的无声吞咽困难(例如，无声吸气)的患者 事件。对无声或近乎无声的吸入进行先发制人的检测是至关重要的。我们的长期目标是发展 一种基于高分辨率颈椎听诊(HRCA)的器质性吞咽困难筛查方法 早期预测与吞咽困难相关的不良事件，并启动干预措施以缓解这些不良事件。这个 这里的总体目标是开发准确、高级的数据分析方法，将HRCA信号转换为 吞咽VF图像中观察到的事件。我们强劲的初步数据让我们得出了我们的核心假设： 先进的数据分析工具适用于HRCA的分析，以便实现自动化 吞咽困难筛查。其基本原理是，一种可靠的、强大的早期预警工具性吞咽困难筛查 该方法将减少静止性吸入/吞咽困难患者的不良事件，缩短住院时间和 改善整体临床结果。在强劲的初步数据指引下，我们将追求以下三个具体 目标：(1)开发机器学习算法来区分吞咽产生的HRCA信号 来自吞咽过程中产生的相似的非吞咽相关信号的生理事件；(2)翻译HRCA 吞咽-实际吞咽生理事件的信号特征，以检测异常吞咽生理； (3)通过机器学习来区分正常和异常的呼吸道保护和吞咽生理 HRCA信号的分析具有与VF相似的精度。在第一个目标下，机器学习方法将是 用于检测咽部吞咽事件，并将其与语音、咳嗽和其他非 与人类专家对我们的VF数据集的解释相比，吞咽事件的准确率达到90%。 在第二个目标下，来自VF的客观吞咽生理观察将与吞咽相匹配 用HRCA观察事件，以表明异常的吞咽生理和呼吸道保护将 产生独特的HRCA信号信号，预测与VF识别的相同事件。在第三层以下 目的，分析算法将被用于检测HRCA信号中的呼吸道保护紊乱的迹象 与人类专家对VF图像的呼吸道保护评级相比，签名的准确率为90%。 这种方法是创新的，因为它将产生能够推断吞咽困难和吸入的分析工具 基于HRCA的分析具有前所未有的准确性，在患者被置于危险境地之前。我们的 这项工作意义重大，因为它将转化为一种预测吞咽困难的早期HRCA筛查工具。 减少无症状患者的相关不良事件，减少医疗不良事件和住院时间。

ABSTRACT Dysphagia (disordered swallowing) causes nearly 150,000 annual hospitalizations and over 220,000 additional hospital days, and prolongs hospital lengths of stay by 40%. Dysphagia risk is typically identified through subjective screening methods and those identified through screening undergo gold standard imaging testing such as videofluoroscopy (VF). However, screening methods over- or underestimate risk, and completely fail to identify patients with silent dysphagia (e.g., silent aspiration) that can cause pneumonia and other adverse events. Pre-emptive detection of silent or near-silent aspiration is essential. The long term goal is to develop an instrumental dysphagia screening approach based on high-resolution cervical auscultation (HRCA) in order to early predict dysphagia-related adverse events, and initiate intervention measures to mitigate them. The overall objective here is to develop accurate, advanced data analysis approaches to translate HRCA signals to swallowing events observed in VF images. Our strong preliminary data has led us to our central hypothesis: advanced data analytics tools are suitable approaches for the analysis of HRCA in order to automate dysphagia screening. The rationale is that a reliable, robust early-warning instrumental dysphagia screening approach will reduce adverse events in patients with silent aspiration/dysphagia, shorten length of stay and improve overall clinical outcomes. Guided by strong preliminary data, we will pursue the following three specific aims: (1) develop machine learning algorithms to differentiate HRCA signals produced by swallowing physiologic events from similar, non-swallow related signals produced during swallowing; (2) translate HRCA swallowing-signal signatures to actual swallow physiologic events to detect abnormal swallowing physiology; and (3) discriminate normal from abnormal airway protection and swallow physiology via machine-learning analysis of HRCA signals with similar accuracy as VF. Under the first aim, a machine learning approach will be used to detect pharyngeal swallowing events and differentiate them from speech, cough and other non- swallow events, with 90% accuracy, when compared to a human expert’s interpretation of our VF data sets. Under the second aim, objective swallowing physiology observations from VF will be matched to swallowing events observed with HRCA in order to show that abnormal swallow physiology and airway protection will produce distinctive HRCA signal signatures that predict the same events identified with VF. Under the third aim, analytical algorithms will be used to detect signs of disordered airway protection in HRCA signal signatures with 90% accuracy when compared to a human expert’s airway protection ratings from VF images. The approach is innovative, as it will produce analysis tools that will infer about dysphagia and aspiration based on the analysis of HRCA with unprecedented accuracy, before patients are placed in harm’s way. Our work is significant, because it will translate to an early-warning HRCA screening tool that predicts dysphagia- related adverse events in asymptomatic patients reducing medical adverse events, and length of stay.