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Central mechanisms of respiratory adaptation to mechanical ventilation

呼吸适应机械通气的中心机制

基本信息

批准号：
9130374
负责人：
CHI-SANG POON
金额：
$ 38.36万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-04 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9130374
关键词：
Accident and Emergency department Address Basic Life Support Brain Breathing Cardiac Output Cell Nucleus Characteristics Clinical Clinical Data Clinical Trials Complex Coupled Critical Care Dancing Data Data Analyses Dependence Devices Dysbarism Environmental air flow Feedback Fingerprint Frequencies Future General Hospitals Generations Health Home environment Individual Intensive Care Units Knowledge Label Lead Learning Length of Stay Letters Long-Term Care Lung Manufacturer Name Massachusetts Mechanical Ventilators Mechanical ventilation Mechanics Mediating Medical Medicine Memory Morbidity - disease rate N-Methyl-D-Aspartate Receptors Neural Network Simulation Neurobiology Neuronal Plasticity Neurons Neuropeptides Outcome Patients Phase Physics Physiological Play Pontine structure Population Probability Procedures Pulmonary Gas Exchange Reflex action Rehabilitation therapy Reporting Respiration Risk Role Safety Sedation procedure Series Somatostatin Somatostatin Receptor System Systems Biology Techniques Technology Testing Tracheostomy procedure Translations Ventilator Ventilator Weaning Weaning Work of Breathing base clinical application clinical practice desensitization design fighting human subject improved innovation multi-scale modeling neural circuit neural correlate neuromechanism neuroregulation next generation novel preclinical study pressure respiratory response systematic review theories wasting

项目摘要

DESCRIPTION (provided by applicant): A major challenge during mechanical ventilation of patients in the intensive care unit (ICU) is how to synchronize the ventilator with the patient's breathing effort smoothly and effectively. Dyssynchrony could lead to patient discomfort and increased need for sedation, longer hospital stay and lower probability of survival. Current mechanical ventilator designs for use in the ICU do not take into consideration the patient's profound respiratory adaptation to vagal volume-related feedback, and are prone to patient-ventilator asynchrony. Understanding the bidirectional relationship between control of breathing and mechanical ventilation and bringing such new concepts to the bedside is increasingly recognized as a major unmet priority in critical care medicine. Entrainment-based mechanical ventilation is a novel ventilation approach that may potentially revolutionize the field and shift clinical practice by incorporating respiratory neurobiology concepts into mechanical ventilator designs. This innovative mechanical ventilation technique is motivated by recent evidence indicating that neural circuits in the pontine pneumotaxic center plays an important role in promoting respiratory entrainment to mechanical ventilation through learning and memory of the Hering-Breuer reflex. Patient-ventilator entrainment is a fundamental physiologic phenomenon that is grounded in the classical physics theory of mutual entrainment between coupled oscillators. In entrainment-based ventilation, the patient's spontaneous respiratory rhythm and the ventilator rhythm are phase-locked to one another on the same tempo, just like two individuals dancing together. This new-generation ventilation mode has recently gained FDA approval of investigational device exemption for clinical trial. To facilitate the translation of te base technology from the benchtop to the bedside, this R01 application proposes a series of preclinical studies with an objective to gain better understanding of the neurophysiologic determinants of patient-ventilator interaction. Because patient-ventilator entrainment is a complex phenomenon, a systems biology approach combining experimental testing and multiscale modeling for quantitative data analysis and prediction of novel outcomes is essential. This will be achieved by elucidating the central mechanisms underlying respiratory adaptation to mechanical ventilation at both the systems (Aim 1) and cellular levels (Aim 2), and developing a multiscale neural network model of patient-ventilator interaction in order to simulate the experimental results and predict the open-loop and closed-loop respiratory-ventilator entrainment frequency and phase response relationships in a quantitative manner (Aim 3). A major hypothesis to be tested is that respiratory entrainment to mechanical ventilation is mediated by a distinct neuronal population in the pontine pneumotaxic center which is distinguished by its critical dependence on NMDA receptor activity and sensitivity to inhibition by the neuropeptide somatostatin.