Mechanisms, Predictors and Clinical Markers of Dysphagia in Cardiac Surgical Patients

心脏外科患者吞咽困难的机制、预测因素和临床标志物

• 批准号: 10442871
• 负责人: Eric Jeng
• 金额: $ 61.94万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-10 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10442871
• 关键词: Acute; Adoption; Adult; American; Aspiration Pneumonia; Assessment tool; Blinded; Cardiac; Cardiac Surgery procedures; Cardiovascular Diseases; Caring; Cause of Death; Cessation of life; Chronic; Clinical; Clinical Markers; Complication; Coughing; Data; Deglutition; Deglutition Disorders; Dehydration; Detection; Development; Early Diagnosis; Eating; Echocardiography; Economic Burden; Enrollment; Evaluation; Foundations; Functional disorder; Future; Generations; Goals; Guidelines; Health; Healthcare; Heart; Hospitalization; Hour; Image; Impairment; Individual; Intake; Intensive Care; Intervention; Knowledge; Larynx; Length; Length of Stay; Logistic Regressions; Malnutrition; Modeling; Monitor; Morbidity - disease rate; Motor; New York; Operative Surgical Procedures; Oral; Outcome; Outpatients; Participant; Pathway interactions; Patient Triage; Patient-Focused Outcomes; Patients; Perioperative; Phase; Physiological; Pneumonia; Population; Postoperative Care; Postoperative Period; Practice Guidelines; Prevention; Prevention strategy; Recovery; Recovery of Function; Reflex action; Resources; Risk; Risk Factors; Safety; Screening procedure; Sensitivity and Specificity; Sensory; Services; Testing; Therapeutic; Time; Triage; Tube; Vestibule; Voice; base; cardiac intensive care unit; care costs; clinical care; clinical decision-making; computer program; design; effective intervention; endotracheal; evidence base; health care model; health care service utilization; high risk; improved; improved outcome; indexing; kinematics; modifiable risk; mortality; novel; patient population; personalized care; personalized health care; prediction algorithm; predictive marker; pressure; response; risk prediction; risk stratification; targeted treatment; tool

Project Summary Dysphagia (swallowing impairment) is a common complication of cardiac surgical procedures, leading to malnutrition, dehydration, aspiration pneumonia, reintubation, increased health care utilization, length of hospitalization, and economic burden. Although preventable, dysphagia-related aspiration pneumonia is a major cause of mortality. Early detection and accurate monitoring of dysphagia are therefore important to facilitate timely interventions to mitigate developing sequelae. Currently, clinical care of dysphagia is hindered by fundamental gaps in knowledge, including 1) contributing risk factors of dysphagia are unknown, prohibiting the use of triaged personalized care pathways; 2) no validated tools to accurately detect and monitor dysphagia in the cardiac intensive care unit exist; and 3) governing mechanisms of swallowing impairment and recovery of function are unknown, impeding the development of mechanistically guided therapeutics and optimization of salient postoperative evaluation time points. Our three specific aims target these knowledge gaps with the broad goal to shift care toward a proactive, multifaceted, and data-driven perioperative Model of Swallowing Integrated Care (MOSAIC). To this end, we will enroll 360 cardiac surgical patients over a four-year period and identify 1) independent risk factors for dysphagia, 2) sensitive clinical markers of swallowing impairment, and 3) governing physiologic mechanisms of unsafe and inefficient swallowing throughout the acute, sub-acute, and long-term postoperative period. Enrolled participants will undergo a preoperative Fiberoptic Endoscopic Evaluation of Swallowing (FEES) to screen out patients with pre-existing dysphagia. Candidate predictor variables will be systematically collected throughout the perioperative time course. Following surgery and within 48 hours of extubation, a simultaneous videofluoroscopy and FEES (VF-FEES) will be performed as well as a battery of simple bedside clinical tests. Detailed blinded analyses will be performed using validated metrics of swallowing safety, efficiency, timing and kinematics to examine acute-phase swallowing function and associated physiologic mechanisms of unsafe or inefficient deglutition. Patients with acute postoperative phase dysphagia will be re-examined at one- and six-months to determine sub-acute and long-term dysphagia trajectories and governing mechanisms of impairment and recovery. Multivariable modeling of dysphagia risk factors will produce a practical dysphagia risk stratification tool to enable accurate forecasting and personalized triaged postoperative care pathways. An accompanying open-access electronic application will be developed to provide seamless clinical adoption and a data-driven clinical decision making tool. The discriminant ability of clinical markers will be determined, and a practical bedside dysphagia screening tool will be validated to enable accurate detection and monitoring of dysphagia in the cardiac intensive care unit. Outcomes will drive future targeted therapeutic and preventative strategies and enhance personalized health care models to ultimately improve patient outcomes.

项目摘要 吞咽困难（吞咽损伤）是心脏外科手术的常见并发症，导致 营养不良，脱水，抽吸肺炎，再插管，增加医疗保健利用率，长度 住院和经济负担。尽管可以预防，但吞咽困难相关的抽吸肺炎是主要的 死亡率的原因。因此，早期检测和准确监测吞咽困难对于促进很重要 及时干预措施以减轻后遗症。目前，吞咽困难的临床护理受到 知识的基本差距，包括1）吞咽困难的危险因素尚不清楚，禁止 使用三雅的个性化护理途径； 2）未经验证的工具可以准确检测和监视吞咽困难 存在心脏重症监护病房； 3）吞咽障碍和恢复的理解机制 功能是未知的，阻碍了机械指导性治疗的发展和优化 显着的术后评估时间点。我们的三个特定目标针对这些知识差距 将注意力转向积极，多方面和数据驱动的围手术期模型的目标 护理（马赛克）。为此，我们将在四年内注册360名心脏外科手术患者，并确定1） 吞咽困难的独立危险因素，2）吞咽障碍的敏感临床标记和3） 整个急性，亚急性和长期吞咽不安全和效率低下的生理机制 术后期。入学的参与者将接受术前的术前内镜评估 吞咽（费用），以筛选出患有吞咽困难的患者。候选预测变量将是 在整个围手术期间课程中有系统地收集。手术后，在48小时内 将进行拔管，同时进行视频荧光镜和费用（VF-FEE）以及一组电池 简单的床边临床测试。将使用经过验证的吞咽指标进行详细的盲目分析 安全，效率，时机和运动学检查急性吞咽功能和相关 不安全或效率低下的生理机制。急性术后吞咽困难的患者 将在一个月和六个月内重新检查，以确定亚急性和长期吞咽困难轨迹以及 管理损害和恢复机制。吞咽困难风险因素的多变量建模将 产生一种实用的吞咽困难风险分层工具，以实现准确的预测和个性化的三叶草 术后护理途径。将开发随附的开放式电子应用程序来提供 无缝的临床采用和数据驱动的临床决策工具。临床的判别能力 将确定标记，并将验证一个实用的床头吞咽困难筛查工具以实现准确 心脏重症监护病房中吞咽困难的检测和监测。成果将推动未来的目标 治疗和预防策略并增强个性化的医疗保健模型，以最终改善 患者的结果。