Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study

BackgroundUpper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer the causality.MethodsA total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care-IV and eICU-Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival-associated prognostic factors.ResultsThe optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in-hospital survival, .81 for 30-day survival in internal testing and .87 for in-hospital survival in external testing. Important factors for in-hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30-day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone.ConclusionsOur interpretable machine learning model for predicting in-hospital and 30-day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability. This model can assist clinicians in managing these patients to improve the discrimination of high-risk patients. This study, based on multicenter hospital data, aimed to develop models to predict in-hospital and 30-day survival of critically ill patients with upper gastrointestinal bleeding. The optimal model using the light GBM algorithm achieved high accuracy and generalizability. Significant factors for in-hospital survival and significant factors for 30-day survival were causally inferred, and coefficients of prognostic impact were presented. This model can assist clinicians in managing these patients to improve the discrimination of high-risk patients.image