Muscle-related parameters-based machine learning model for predicting postinduction hypotension in patients undergoing colorectal tumor resection surgery

Weixuan Sheng; Danyang Gao; Pengfei Liu; Mingxue Song; Lei Liu; Huihui Miao; Tianzuo Li

doi:10.3389/fmed.2023.1283503

Muscle-related parameters-based machine learning model for predicting postinduction hypotension in patients undergoing colorectal tumor resection surgery

Front Med (Lausanne). 2023 Dec 27:10:1283503. doi: 10.3389/fmed.2023.1283503. eCollection 2023.

Authors

Weixuan Sheng^#¹, Danyang Gao^#¹, Pengfei Liu¹, Mingxue Song¹, Lei Liu², Huihui Miao¹, Tianzuo Li¹

Affiliations

¹ Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
² Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

^# Contributed equally.

Abstract

Objectives: This study used machine learning algorithms to identify important variables and predict postinduction hypotension (PIH) in patients undergoing colorectal tumor resection surgery.

Methods: Data from 318 patients who underwent colorectal tumor resection under general anesthesia were analyzed. The training and test sets are divided based on the timeline. The Boruta algorithm was used to screen relevant basic characteristic variables and establish a model for the training set. Four models, regression tree, K-nearest neighbor, neural network, and random forest (RF), were built using repeated cross-validation and hyperparameter optimization. The best model was selected, and a sorting chart of the feature variables, a univariate partial dependency profile, and a breakdown profile were drawn. R², mean absolute error (MAE), mean squared error (MSE), and root MSE (RMSE) were used to plot regression fitting curves for the training and test sets.

Results: The basic feature variables associated with the Boruta screening were age, sex, body mass index, L3 skeletal muscle index, and HUAC. In the optimal RF model, R² was 0.7708 and 0.7591, MAE was 0.0483 and 0.0408, MSE was 0.0038 and 0.0028, and RMSE was 0.0623 and 0.0534 for the training and test sets, respectively.

Conclusion: A high-performance algorithm was established and validated to demonstrate the degree of change in blood pressure after induction to control important characteristic variables and reduce PIH occurrence.

Keywords: Boruta; machine learning; partial dependence profile; postinduction hypotension; regression fitting curve.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (82071180, 82271206), Natural Science Foundation of Beijing (7212023).