A Machine Learning Model to Guide CT Angiography Use in Acute Gastrointestinal Bleeding: A Decision-Support Tool for Gray-Zone Cases

Purpose CT angiography (CTA) is valuable in evaluating acute gastrointestinal (GI) bleeding but lacks guidance for use in patients who are neither hemodynamically unstable nor clearly stable, creating a gray zone of uncertainty in imaging decisions. Our goal was to develop a risk stratifying machine learning (ML) model for hemodynamic borderline patients with GI bleeding to help mitigate testing uncertainty by predicting the probability of a positive CTA. Methods We retrospectively analyzed 11,938 patients with GI bleeding from the MIMIC-IV database. Among 890 CTA scans, 140 were eligible after applying exclusion criteria. A logistic regression model with SMOTE upsampling was trained using seven routine lab values obtained within 24 hours of CTA. Model performance was evaluated using recall, F1 score, and ROC-AUC. Results The model achieved an F1 score of 0.71, recall of 0.83, and ROC-AUC of 0.71. The features - delta hematocrit/hemoglobin and the maximum INR in the last 24 hours were influential predictors, while the feature minimum platelets in the last 24 hours was not. Logistic regression outperformed random forest and XGBoost in identifying true positives. Conclusions A simple, interpretable ML model can assist in identifying patients most likely to benefit from CTA in GI bleeding. Its reliance on structured, readily available labs supports potential real-time integration into electronic health record workflows. With further validation, this approach could improve triage, reduce unnecessary scans, and support real-time decision-making.