方山 真朱

BACKGROUND: Clinically important gastrointestinal bleeding (CIGIB) is a serious complication in critically ill patients, contributing to prolonged ICU stays and increased mortality. Despite efforts to identify high-risk patients, no previous studies have employed machine learning models to predict CIGIB during ICU stay or identify key predictors in this context. METHODS: This single-center retrospective study included ICU patients aged 18 years or older admitted between 2017 and 2024. Patients with ICU stays of less than 24 hours or GIB within 24 hours of admission were excluded. Machine learning models, including XGBoost, Random Forest, and L1-regularized logistic regression, were trained using patient data from the first 24 hours of ICU admission. Model performance was assessed using AUROC, precision, recall, and F1 scores. Shapley Additive Explanations (SHAP) were employed to evaluate key predictors. RESULTS: A total of 7,357 ICU patients were included, of whom 171 (2.3%) experienced CIGIB. The XGBoost model demonstrated the highest predictive performance with an AUROC of 0.84. Key predictors included APACHE III scores, hematocrit levels, APTT, creatinine and respiratory rate, while invasive mechanical ventilation and stress ulcer prophylaxis within the first 24 hours of ICU admission did not rank among the top 20 predictors based on SHAP values. CONCLUSIONS: This study represents the first application of machine learning for predicting CIGIB in ICU patients, providing valuable insights into risk stratification. The model demonstrated high predictive accuracy and interpretability, highlighting its potential to guide early intervention and prophylaxis. Further multi-center studies and interventional trials are needed to validate these findings and refine clinical risk prediction strategies.

Bevacizumab, a humanized monoclonal antibody against vascular endothelial growth factor (VEGF), is used in combination with chemotherapy for various malignancies, including metastatic colorectal cancer. While effective, bevacizumab can inhibit normal blood vessel growth, leading to cardiovascular side effects not typically associated with conventional chemotherapy. We report a rare case, from an international perspective, of a 73-year-old man with a history of gastric cancer and newly diagnosed metastatic colorectal cancer complicated by a pre-existing abdominal aortic aneurysm (AAA) measuring 52 mm. The aneurysm was initially managed conservatively, as the multidisciplinary team (MDT) and the patient agreed to prioritize chemotherapy despite the known rupture risk, given his wish to avoid delaying treatment for his cancer. After the diagnosis of colorectal cancer during chemotherapy, bevacizumab was added to his regimen. He developed a rupture of the AAA two days after the fourth dose. Emergent open surgical repair was successfully performed without wound healing complications. This case highlights the potential risk of large-vessel complications associated with bevacizumab, especially in patients with known vascular anomalies. Careful imaging assessment and monitoring are imperative when considering bevacizumab for patients at risk of aortic rupture. In selected cases, prophylactic measures such as preemptive aneurysm repair should be contemplated to optimize safety.