Deep Learning Model for Predicting Operative Mortality After Total Gastrectomy: Analysis of the Japanese National Clinical Database (NCD)

Background Radical gastrectomy with lymph node dissection is the primary treatment for gastric cancer. However, the overall complication rate remains approximately 10%–20%, with a postoperative mortality rate of 2.3%. Therefore, preoperative stratification of patients based on their expected surgical risks is important. This study aimed to develop a deep learning prediction model using big data from the National Clinical Database (NCD) to predict operative mortality after total gastrectomy. Methods Patients aged 18 years or older who underwent total gastrectomy for gastric cancer and were registered in the NCD between January 2018 and December 2019 were included. A total of 62 variables, including age, sex, past medical history, preoperative blood test results, and tumor characteristics, were used as covariates, with operative mortality as the outcome variable. Deep learning models were developed using Python, TensorFlow and Keras. Hyperparameters were adjusted using the k‐fold method with the training data. The model was evaluated using validation data. Results Of the 14 980 eligible cases, 11 980 were used for training and 3000 for validation. The event rate was 1.2%. A four‐layer, 5217‐variable model was developed. The final C‐statistic was 0.79 (95% confidence intervals: 0.74–0.83) for the training data and 0.74 (95% confidence intervals: 0.62–0.85) for the validation data. Conclusion We developed a deep learning model to predict operative mortality using big data from the NCD. To improve the accuracy, it is necessary to introduce new variables related to postoperative complications or factors that cannot be analyzed using conventional methods.