MH-STRALP: A scoring system for prognostication in patients with upper gastrointestinal bleeding

BACKGROUND Upper gastrointestinal bleeding (UGIB) is a common medical emergency and early assessment of its outcomes is vital for treatment decisions. AIM To develop a new scoring system to predict its prognosis. METHODS In this retrospective study, 692 patients with UGIB were enrolled from two centers and divided into a training (n = 591) and a validation cohort (n = 101). The clinical data were collected to develop new prognostic prediction models. The endpoint was compound outcome defined as (1) demand for emergency surgery or vascular intervention, (2) being transferred to the intensive care unit, or (3) death during hospitalization. The models' predictive ability was compared with previously established scores by receiver operating characteristic (ROC) curves. RESULTS Totally 22.2% (131/591) patients in the training cohort and 22.8% (23/101) in the validation cohort presented poor outcomes. Based on the stepwise-forward Logistic regression analysis, eight predictors were integrated to determine a new post-endoscopic prognostic scoring system (MH-STRALP); a nomogram was determined to present the model. Compared with the previous scores (GBS, Rockall, ABC, AIMS65, and PNED score), MH-STRALP showed the best prognostic prediction ability with area under the ROC curves (AUROCs) of 0.899 and 0.826 in the training and validation cohorts, respectively. According to the calibration curve, decision curve analysis, and internal cross-validation, the nomogram showed good calibration ability and net clinical benefit in both cohorts. After removing the endoscopic indicators, the pre-endoscopic model (pre-MH-STRALP score) was conducted. Similarly, the pre-MH-STRALP score showed better predictive value (AUROCs of 0.868 and 0.767 in the training and validation cohorts, respectively) than the other pre-endoscopic scores. CONCLUSION The MH-STRALP score and pre-MH-STRALP score are simple, convenient, and accurate tools for prognosis prediction of UGIB, and may be applied for early decision on its management strategies.