Time-sequential prediction of postoperative complications after gastric cancer surgery using machine learning: a multicenter cohort study.

[BACKGROUND] Although many studies have developed logistic regression models for predicting complications using preoperative and intraoperative data, none have applied comprehensive perioperative information with machine learning (ML) to enable time-sequential predictions.

[METHODS] This study included patients undergoing gastric cancer surgery between 2013 and 2019 at two hospitals. Comprehensive perioperative data were collected. Four ML models were developed: the postoperative day (POD) 1 and POD 3 models predicted complications occurring from POD 2 and POD 4, while the 24-h and 8-h models predicted complications within the 24 and 8 h, respectively, after collection of the most recent biochemical data and vital signs. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with repeated validation for generalizability.

[RESULTS] Among 4139 patients, 782 (18.9%) experienced complications (Clavien-Dindo grade ≥ II). The 8-h model achieved the highest AUC (0.737) for overall complications. The POD 3 model outperformed the POD 1 model, with AUCs exceeding 0.8 for pancreatic fistula (0.869) and intra-abdominal abscess (0.821). The 8-h and the 24-h model both achieved AUCs above 0.8 for specific infectious complications. The 8-h model demonstrated the following AUCs: 0.889 for pancreatic fistula, 0.842 for intra-abdominal abscess, 0.826 for pneumonia, and 0.824 for anastomotic leakage, surpassing all POD-based models. In each 8-h model, C-reactive protein, pulse rate, and intraoperative blood loss consistently emerged as significant variables.

[CONCLUSION] Hour-based ML models incorporating comprehensive perioperative data predict post-gastric cancer surgery complications with high accuracy and time-sequential capability, potentially aiding clinical decision-making and improving outcomes.