Development and Validation of a Risk Prediction Model for In-Hospital Mortality in Patients With Acute Upper Gastrointestinal Bleeding.

Abstract

Acute upper gastrointestinal bleeding (UGIB) is a common life-threatening clinical emergency with a poor prognosis. The aim of this study was to develop a risk prediction model for in-hospital mortality in patients with UGIB. We performed a post hoc analysis of a publicly available retrospective clinical data. A total of 360 patients with UGIB were included in this study. The least absolute shrinkage and selection operator regression was used to screen predictors and a restricted cubic spline function was used to investigate the assumption of linear relationships between continuous predictors and the risk of in-hospital mortality. Backward stepwise selection with the Akaike information criterion was used to identify variables for the best prediction model. A nomogram was developed based on the results of the best prediction model. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis were used to evaluate the performance of the nomogram. The optimal prediction model consisting of 4 predictors: red cell distribution width (odds ratio [OR] = 8.44; 95% confidence interval [CI]: 1.77-89.10), platelet count (OR = 0.99; 95% CI: 0.99-1.00), pulse rate (OR = 1.03; 95% CI: 1.01-1.05), and SpO2 (OR = 0.92; 95% CI: 0.86-0.96). The nomogram model had good discrimination (area under the curve = 0.86, 95% CI: 0.78-0.95), calibration, and clinical usefulness. In this study, we developed a nomogram model for predicting death during hospitalization in patients with UGIB based on blood biomarkers and baseline vital signs at the time of admission. The model has good performance, allowing rapid risk stratification of patients with UGIB.