Establishment of predictive model for post-induction hypotension in patients undergoing colorectal cancer resection based on muscle CT parameters: machine learning algorithms
10.3760/cma.j.cn131073.20231221.01104
- VernacularTitle:结直肠癌切除术患者基于肌肉CT参数的麻醉诱导后低血压预测模型的建立：机器学习算法
- Author:
Weixuan SHENG
1
;
Danyang GAO
;
Huihui MIAO
;
Tianzuo LI
Author Information
1. 首都医科大学附属北京世纪坛医院麻醉科，北京 100038
- Keywords:
Hypotension;
Anesthesia induction;
Machine learning;
Forecasting model
- From:
Chinese Journal of Anesthesiology
2024;44(11):1293-1299
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a predictive model for post-induction hypotension (PIH) in the patients undergoing colorectal cancer resection using machine learning algorithms based on muscle CT parameters.Methods:This was a single-center, retrospective study. Electronic medical records from 318 patients who underwent colorectal cancer resection from September 1, 2018 to September 30, 2021 at our hospital were collected. Predictive variables included age, gender, body mass index, hemoglobin, American Society of Anesthesiologists Physical Status classification, TNM staging, age-adjusted Charlson comorbidity index, prognostic nutritional index, L 3 level skeletal muscle index, and muscle quality assessed by Hounsfield unit average calculation. The outcome variable was PIH. The training and testing sets were divided based on the timeline (patients before September 1, 2020 were included in the training set, and those after that date were included in the testing set). The filtering method was used to screen the feature variables. Eight models, including logistic regression, Bayesian models, K-nearest neighbors, support vector machines, neural networks, decision trees, extreme gradient boosting trees, and random forests, were established in the training set using over-sampling technique, repeated cross-validation and hyperparameter optimization. After selecting the best model, a sorting chart of the feature variables, a univariate partial dependency profile, and a breakdown profile were drawn. In the testing set, the confusion matrix and parameters were calculated, and the receiver operating characteristic curve, precision recall curve, calibration curve, and decision curve were drawn to evaluate the performance of the predictive model. Results:The screened feature variables were Hounsfield unit average calculation value, age, L 3 level skeletal muscle index, prognostic nutritional index, hemoglobin and body mass index. The random forest was the optimal model, with an accuracy of 0.985 9, a MCC of 0.970 8, the area under the receiver operating characteristic curve was 1.0, and the area under the precision recall curve was 1.0. The Brier score of the calibration curve was 0.076 6; the decision curve showed the highest clinical net benefit of 0.6. Conclusions:In this study, machine learning algorithm is used to identify the important characteristic variables and establish a high-performance PIH prediction model based on muscle CT parameters.
