Tzavaras A, Weller P R, Prinianakis G, Lahana A, Afentoulidis P, Spyropoulos B
Medical Instrumentation Technology Department, Technological Educational Institute of Athens, Greece.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:112-5. doi: 10.1109/IEMBS.2011.6089909.
The aim of the paper is to identify the key physiological variables and ventilator settings involved in ventilation management, and required for an appropriate Clinical Decision Support System (CDSS). Based on the results of a questionnaire designed for the purpose of the research, 70 hours of physiological and ventilation data were recorded. Recorded data were classified by clinicians into three major lung pathologies and were further statistically analyzed for identifying strong relationships between monitored and controlled ventilator parameters. Correlation analysis was evaluated by Intensive Care Unit (ICU) clinicians. Based on the evaluators' majority voting the number and type of participating variables in a CDSS was drastically decreased. The number and type of monitored variables ranged from a single one to six, depending on the patient's lung pathology, and the controlled ventilator setting. Evaluation results were successfully applied to Neural Network models for providing suggestions on Tidal Volume and the Fraction of inspired Oxygen.
本文的目的是确定通气管理中涉及的关键生理变量和呼吸机设置，以及合适的临床决策支持系统（CDSS）所需的这些变量和设置。基于为该研究目的设计的问卷调查结果，记录了70小时的生理和通气数据。临床医生将记录的数据分为三种主要的肺部疾病，并进一步进行统计分析，以确定监测和控制的呼吸机参数之间的强关系。重症监护病房（ICU）的临床医生对相关性分析进行了评估。基于评估人员的多数投票，CDSS中参与变量的数量和类型大幅减少。根据患者的肺部疾病和控制的呼吸机设置，监测变量的数量和类型从单一变量到六个不等。评估结果成功应用于神经网络模型，以提供关于潮气量和吸入氧分数的建议。
