Random forest algorithm for predicting postoperative hypotension in oral cancer resection and free flap reconstruction surgery.

This study aimed to investigate the risk factors for low postoperative blood pressure and construct a machine learning (ML) model based on these features for real-time prediction in patients with oral cancer following reconstruction surgery. The retrospective cohort analysis included adults who had undergone oral cancer resection and free flap reconstruction surgery between December 2022 and December 2023. Patient clinical characteristics were obtained from the electronic medical records. Seven ML techniques were attempted with postoperative hypotension (POH) (mean arterial pressure ˂ 55 mmHg) as the primary outcome. The best-performing ML model was tuned, and the final performance was evaluated using split-set validation, followed by risk factor identification and model interpretability. Of the 727 patients, 412 were finally included, with 66 (16.2%) experiencing POH, resulting in higher inpatient costs and prolonged hospitalization. With an area under the receiver operating characteristic curve of 0.805 (95% confidence interval [CI]: 0.674-0.935), the random forest model demonstrated excellent performance. Shapley additive explanation and feature importance analysis revealed that systolic pressure, heart rate, tumor size, lactic acid level, diastolic pressure, surgical time, total liquid infusion volume, and body mass index were significant risk factors for POH, indicating the robustness of the random forest model.