Predicting blood pressure change during induction of anesthesia with propofol: using logistic regression and artificial neural network models

• Main Authors: Ruey-Horng Rau, 饒瑞泓
• Other Authors: Yu-Chuan LI
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/25049171440634966644

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 91 === Propofol is a popular hypnotic agent used in induction or maintain of anesthesia. Other intensive care units in the hospital also use it as a sedative drug. The most attractive feature of propofol is rapid recovery of patient’s conscious level while terminating the infusion of propofol. Moreover, its antiemetic effect seems attractive to many anesthesia staffs to avoid post operative nausea and vomiting. Unfortunately, propofol can produce hypotension more often than other anesthesia induction agent. The hypotensive effect of propofol comes from direct depress of cardiac muscle and vasodilatation of peripheral vessels. If not treated promptly and properly, hypotension may induce sever damage to vital organs such as kidney, heart and brain. We want to setup reliable predicting models to forecast the blood pressure change caused by injection of propofol during induction of anesthesia. Seventeen values (including demographic data such as age and gender, patient past histories such as diabetes mellitus and asthma, and laboratory data such as hemoglobin level and blood pressure before induction) from 200 patients who received propofol as their induction agent in a routine operation were collected in about one year. Another data set from 100 patients, for evaluating the performance of the predicting models was collected in the same period by the same induction procedure. Area under ROC (Receiver Operating Characteristic) curve was used as an index tools to evaluate the performance of our predicting models. Two types of prediction models were built up in our study. The first type is binary logistic regression model. We have make up two logistic regression model using different input variables. The first logistic model contained all the seventeen input variables, and the second logistic regression model had only two input variables. Another type of predicting model used artificial neural network to predict the blood pressure change. We have finally constructed five different neural network models with dissimilar training protocol and network topography. To compare the predicting models with human beings, three anesthesia attending doctors (experts of anesthesia), four anesthesia resident doctors and fifteen anesthesia nurse with different clinical experiences were also enrolled in this study. Their discrimination abilities of blood pressure change caused by injection of propofol in the evaluation group were compared with our models. Finally we have found that the two logistic regression models and five artificial neural network models had the same predicting abilities and were all superior to the three anesthesia experts, although statistical significance did exist between them. But the abilities of our predicting models surpassed the anesthesia resident doctors and the fifteen anesthesia nurses and the statistic significance was also found. The predicting modes can be easily integrated in the hospital information system and can act as a reliable decision supporting system