Left ventricular diastolic dysfunction (LVDD) of the heart is a condition where the heart does not relax properly. This condition is important during times of stress, illustrated by the fact that LVDD is associated with significant morbidity of elderly surgical patients. LVDD is often asymptomatic and unrecognized prior to surgery as many of these patients have normal ejection fractions. However, LVDD may lead to heart failure in patients with preserved systolic function, with the incidence being as high as 50% in hospitalized elderly patients. The diagnosis of LVDD is an independent risk factor for postoperative major adverse cardiac events (MACE) and negatively impacts post-surgery readmission rates. With over 14 million noncardiac surgeries currently being performed annually in the elderly, an increased frequency of MACE can be expected unless we find dynamic methods to monitor LVDD and treat it early. Anesthesiologists play a critical role in the care of elderly patients by managing fluid and drug therapies during surgery. Current standard of care is to manage elderly patients with LVDD using only blood pressure monitoring. Unfortunately blood pressure monitoring is unable to detect changes in diastolic function. In contrast, transesophageal echocardiography (TEE) can easily measure diastolic function in real-time in the operating rooms. No current studies, however, have assessed the efficacy of TEE to guide management of patients with LVDD during noncardiac surgery. Therefore, it is important to serially evaluate LVDD intraoperatively with TEE and determine if changes in anesthetic management can reduce the risk of postoperative cardiac events and improve the outcome of elderly patients undergoing surgery.
The specific aims of this study will be to test the hypothesis that the dynamic intraoperative evaluation of LVDD will have a major impact on post-operative morbidity in this patient population.
Specific Aim 1 will demonstrate that underlying LVDD in elderly patients results in dynamic changes in filling pressures during the perioperative period. We predict intraoperative echocardiography indices descriptive of diastolic function are dynamic, real-time markers predictive of clinical outcomes. Furthermore, we predict echocardiographic diastolic indices are influenced by intraoperative management, thus adjusting clinical treatment algorithms based on these indices can affect risk of MACE.
Specific Aim 2 will demonstrate that using real-time echo data, anesthetic management will favorably alter LVDD in these patients and result in improved outcomes. To support our hypothesis, we will conduct a prospective, randomized clinical trial serially examining these echocardiographic diastolic indices during surgery in elderly subjects with known LVDD identified on preoperative screening. The control group will receive standard anesthesia management. The experimental group will receive echocardiography-guided hemodynamic management based on an LVDD-sensitive echo- driven treatment algorithm. We predict experimental subjects will show improved clinical outcomes based on our pilot data including less postoperative adverse cardiac events and reduced length of hospital and ICU stay.

Public Health Relevance

Echocardiography-Guided Hemodynamic Management Strategy to Improve Clinical Outcomes for Elderly Patients with Left Ventricular Diastolic Dysfunction Undergoing Noncardiac Surgery. Elderly patients are the fastest growing surgical population and present with increased risk of postoperative cardiac problems, especially congestive heart failure. Diastolic dysfunction is common in the elderly population and increases the risk of major adverse cardiac events after surgery. This project will use dynamic measurements of diastolic filling pressures by echocardiography for goal-directed fluid and drug management during surgery to determine whether this reduces serious cardiac events related to diastolic dysfunction after surgery in this high-risk population.

National Institute of Health (NIH)
Small Research Grants (R03)
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Special Emphasis Panel (ZAG1)
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Zieman, Susan
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University of Nebraska Medical Center
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