Machine learning prediction and explanation of high intraoperative blood pressure variability for noncardiac surgery using preoperative factors.

BACKGROUND

The objective of this study is to construct an explainable machine learning predictive model for high intraoperative blood pressure variability(IBPV) based on preoperative characteristics, to enhance intraoperative circulatory management and surgical outcomes. This study utilized a retrospective observational design, employing the eXtreme Gradient Boosting (XGBoost) algorithm to create a predictive model for high IBPV.

METHOD

The data for the study were obtained from the central operating room of a major hospital in Beijing, China, covering the period from March 2016 to April 2022. A total of 37,756 noncardiac surgeries were included in the analysis. The dataset comprised demographic, preoperative laboratory, and diagnostic information. Selection criteria included all noncardiac surgeries with complete preoperative data. High IBPV was defined as a coefficient of variation exceeding 20% during the surgical procedure. The main outcome measure was the prediction of high IBPV, assessed using the area under the receiver operating characteristic curve (AUC), accuracy, and specificity.

RESULTS

The XGBoost-based model achieved an accuracy of 0.81 and a specificity of 0.99, with a moderate discriminative ability (AUC = 0.60). SHAP analysis identified age and American Society of Anesthesiologists (ASA) classification as the top positive predictors for high IBPV, with maximum SHAP values of 0.4 and 0.2, respectively. Preoperative plasma albumin level was the key negative predictor, with a maximum SHAP value of -0.6. Interactions between preoperative blood calcium and age, and weight and age, were also influential. The model quantified individual high IBPV risk probabilities and variable contributions.

CONCLUSIONS

The XGBoost model effectively identifies significant predictors of high IBPV, including age, ASA classification, and plasma albumin levels, and is capable of estimating individual risk probabilities. However, external validation of the model in different clinical settings and populations is needed to further confirm its predictive performance and generalizability.

TRIAL REGISTRATION

NCT05698433.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12872-025-05026-7.