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

通过将高分辨率颈部听诊与先进的数据分析工具相结合，对吞咽困难进行仪器筛查，以识别无声吞咽困难和无声误吸

• 批准号: 9355417
• 负责人: Ervin Sejdic
• 金额: $ 30.24万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9355417
• 关键词: Acute; Address; Admission activity; Adult; Adverse event; Algorithmic Analysis; Algorithms; Aspirate substance; Aspiration Pneumonia; Auscultation; Biomechanics; Breathing; Caring; Cervical; Choking; Clinical; Coughing; Data; Data Analyses; Data Analytics; Data Set; Deglutition; Deglutition Disorders; Dehydration; Dementia; Detection; Device Designs; Devices; Diagnosis; Diagnostic; Disabled Persons; Disease; Equipment; Event; Food; Goals; Gold; Group Homes; Head and Neck Cancer; Health; Hospital Nursing; Hospitalization; Hospitals; Human; Image; Impairment; Incidence; Intervention; Lead; Learning; Length of Stay; Life; Liquid substance; Machine Learning; Malnutrition; Measures; Medical; Methods; Mission; Modeling; Monte Carlo Method; Morbidity - disease rate; Nature; Neurodegenerative Disorders; Nursing Homes; Oral; Outcome; Pathologic; Patient risk; Patients; Pediatric Hospitals; Pharmaceutical Preparations; Physiological; Physiology; Pneumonia; Positioning Attribute; Public Health; Research; Resolution; Risk; Severities; Signal Transduction; Speech; Stroke; Symptoms; Techniques; Testing; Time; Training; Translating; United States National Institutes of Health; Water; Work; airway obstruction; analytical tool; base; cancer therapy; clinical practice; clinically significant; image processing; improved; innovation; instrument; kinematics; mortality; patient safety; predictive signature; predictive tools; screening; tool; translational impact; vibration

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.

摘要 吞咽困难（吞咽障碍）每年导致近15万人住院， 住院天数和住院时间缩短40%。吞咽困难风险通常通过以下方式确定： 主观筛选方法和通过筛选确定的方法进行金标准成像测试 例如视频荧光透视（VF）。然而，筛查方法过度或低估了风险， 识别患有无症状吞咽困难的患者（例如，可能引起肺炎和其他不良反应 事件预先检测无声或接近无声的吸入至关重要。长期目标是发展 基于高分辨率颈部听诊（HRCA）的工具性吞咽困难筛查方法， 早期预测吞咽困难相关的不良事件，并采取干预措施减轻这些不良事件。的 这里的总体目标是开发准确，先进的数据分析方法，将HRCA信号转化为 VF图像中观察到吞咽事件。我们强大的初步数据使我们得出了我们的核心假设： 先进的数据分析工具是分析人力资源能力评估的合适方法， 吞咽困难筛查。其基本原理是，一个可靠的，强大的早期预警工具吞咽困难筛查 该方法将减少无症状性误吸/吞咽困难患者的不良事件，缩短住院时间， 改善整体临床结果。在强有力的初步数据的指导下，我们将追求以下三个具体目标： 目的：（1）开发机器学习算法来区分吞咽产生的HRCA信号 来自吞咽期间产生的类似的非吞咽相关信号的生理事件;（2）翻译HRCA 实际吞咽生理事件的吞咽信号特征，以检测异常吞咽生理; 以及（3）通过机器学习区分正常与异常气道保护和吞咽生理 HRCA信号分析的准确性与VF相似。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，并将其与言语、咳嗽和其他非吞咽事件区分开来。 吞咽事件，与人类专家对VF数据集的解释相比，准确率为90%。 在第二个目标下，VF的客观吞咽生理学观察将与吞咽相匹配。 HRCA观察到的事件，以表明异常吞咽生理学和气道保护将 产生独特的HRCA信号特征，预测与VF相同的事件。根据第三个 目的是，分析算法将用于检测HRCA信号中的气道保护紊乱的迹象 与人类专家的VF图像气道保护评级相比，该签名具有90%的准确性。 这种方法是创新的，因为它将产生分析工具，可以推断吞咽困难和误吸 基于对HRCA的分析，在病人受到伤害之前，以前所未有的准确性。我们 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预警HRCA筛查工具- 无症状患者相关不良事件减少，医疗不良事件减少，住院时间缩短。