New machine learning scoring system for predicting postoperative mortality in gastroduodenal ulcer perforation: A study using a Japanese nationwide inpatient database.

Abstract

Conventional prediction models for estimating risk of postoperative mortality in gastroduodenal ulcer perforation have suboptimal prediction ability. We aimed to develop and validate new machine learning models and an integer-based score for predicting the postoperative mortality. We retrospectively identified patients with gastroduodenal ulcer perforation who underwent surgical repair, using a nationwide Japanese inpatient database. In a derivation cohort from July 2010 to March 2016, we developed 2 machine learning-based models, Lasso and XGBoost, using 45 candidate predictors, and also developed an integer-based score for clinical use by including important variables in Lasso. In a validation cohort from April 2016 to March 2017, we measured the prediction performances of the models by computing area under the curve and comparing it to the conventional American Society of Anesthesiology risk score. Of 25,886 patients, 1,176 (4.5%) died after surgical repair. For the validation cohort, Lasso and XGBoost had significantly higher prediction abilities than the American Society of Anesthesiology score (Lasso area under the curve = 0.84; 95% confidence interval 0.81-0.86; American Society of Anesthesiology score area under the curve = 0.70; 95% confidence interval 0.65-0.74, P < .001). The integer-based risk score, which had 13 factors, had a prediction ability similar to those of Lasso and XGBoost (area under the curve = 0.83; 95% confidence interval 0.81-0.86). According to the integer-based score, the mortalities were 0.1%, 2.3%, 9.3%, and 29.0% for the low (score, 0), moderate (1-2), high (3-4), and very high (≥5) score groups, respectively. Machine learning models and the integer-based risk score performed well in predicting risk of postoperative mortality in gastroduodenal ulcer perforation. These models will help in decision making.