Predictive model of general anesthesia post-induction hypotension based on machine learning
Objective To construct a clinical prediction model of general anesthesia post-induction hypotension(PIH)in patients undergoing laparoscopic cholecystectomy(LC)based on machine learning.Methods The data of patients who underwent LC surgery from May 2019 to September 2023 were retro-spectively selected.The training set and the validation set were allocated at a ratio of 7:3.Logistic regres-sion,random forest(RF),and support vector machine(SVM)were used to construct the prediction model of PIH.The discrimination accuracy of the model was evaluated by the area under the receiver operating characteristic(ROC)curve,and the calibration of the model was evaluated by calibration curve and Hos-mer-Lemeshow(H-L)test.The nomogram was used for visual interpretation of the model.Results A total of 260 patients were included,and 82 patients(31.5％)developed PIH.There were 182 patients in the training set and 78 patients in the validation set.Fifty-eight patients(31.9％)in the training set developed PIH,and 24 patients(30.8％)in the validation set developed PIH.Age,BMI,use of angiotensin conver-ting enzyme inhibitors(ACEIs)/angiotensin receptor blockers(ARBs),basal HR and MAP,HR and MAP on entering the operating room,HR change,and MAP change were important factors for the model to predict PIH.The logistic regression model had the best prediction performance,with AUC,accuracy and re-call rate of 0.93(95％CI 0.90-0.99),0.81,and 0.81 in validation set,respectively.Conclusion The logistic regression model based on machine learnin has good performance in predicting PIH.The selected predictive variables are age,BMI,use of ACEIs/ARBs,basal HR and MAP,HR and MAP on entering the operating room,HR and MAP change,which can quickly and accurately assess the risk of PIH.
Laparoscopic cholecystectomyGeneral anesthesiapost-induction hypotensionMa-chine learningPrediction model
万方数据
维普
