Providing a model for predicting blood pressure fluctuations after induction of general anesthesia with data mining: a brief report

Background: Fluctuations in blood pressure after induction of general anesthesia have played a significant role in complications of surgery. Therefore, the present study was performed by identifying the causes of blood pressure fluctuations after induction of anesthesia, predicting and preventing them. Methods: For this study which is a retrospective cohort, data mining methods in the data set including the information related to 3150 patients who underwent anesthesia and surgery from April 2018 to September 2019 in Imam Khomeini Hospital in Kermanshah were used. The data set included patients aged 18 years and older (age range of 18 to 96) who underwent a general anesthesia induction test using Propofol and subsequently endotracheal intubation for non-cardiac surgery. If patients did not have intubation, data were missing, or patients underwent intubation after repeated trials, they got excluded. In total, 2640 patients were included in this analysis. Preoperative patient clinical information was collected from pre-anesthesia evaluation records. Intraoperative data were obtained from computer anesthesia records. This data from the patient monitoring system and the anesthesia machine was automatically stored in the anesthesia files, while drug doses and anesthesia techniques were recorded manually. The data were then pre-processed using SPSS software, version 26 (IBM SPSS, Armonk, NY, USA). Results: In this study, 53 features of patients' records were used (The maximum number of features used in previous studies were 48 features, which compared to them, 5 new features were included in the study) for which a P-value was calculated. Finally, features with a P<0.05 (Indicates the level of significance of the variable) were selected. Then, three data mining algorithms, logistic regression, neural networks and decision tree (the most repetitive data mining algorithms based on previous studies) were used to predict blood pressure. Also, using the criteria of accuracy, precision, sensitivity and F function, the performance of three prediction algorithms in data mining was evaluated. Conclusion: Six features with P<0.05 were selected that the logistic regression model was more accurate, which was presented as the final model for predicting increased blood pressure fluctuations with path coefficients.