Development and Internal Validation of the Cleveland Clinic Bleeding Model to Predict Major Bleeding Risk at Admission in Medical Inpatients

Abstract

BackgroundGuidelines recommend pharmacological VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment. ObjectiveWe developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model to IMPROVE. MethodsWe selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Hemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a LASSO logistic regression model in the training set and compared the discrimination and calibration of our model and IMPROVE in the validation set. ResultsAmong 46,314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (OR = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (AUC = 0.85 vs. 0.70, p < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high-risk (7.2% vs. 11.8%, p < .001). Calibration was adequate (Brier score = 0.0057). ConclusionUsing a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model.