Background: Venous thromboembolism (VTE) is the leading cause of preventable hospital death in the US. Guidelines from the American College of Chest Physicians and American Society for Hematology recommend providing pharmacological VTE prophylaxis to acutely or critically ill medical patients at acceptable bleeding risk, but there is currently only one validated risk assessment model (RAM) for estimating bleeding risk. We developed a RAM using risk factors at admission and compared it with the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model.
Methods: A total of 46,314 medical patients admitted to a Cleveland Clinic Health System hospital from 2017-2020 were included. Data were split into training (70%) and validation (30%) sets with equivalent bleeding event rates in each set. Potential risk factors for major bleeding were identified from the IMPROVE model and literature review. Penalized logistic regression using LASSO was performed on the training set to select and regularize important risk factors for the final model. The validation set was used to assess model calibration and discrimination and compare performance with IMPROVE. Bleeding events and risk factors were confirmed through chart review.
Results: The incidence of major in-hospital bleeding was 0.58%. Active peptic ulcer (OR = 5.90), prior bleeding (OR = 4.24), and history of sepsis (OR = 3.29) were the strongest independent risk factors. Other risk factors included age, male sex, decreased platelet count, increased INR, increased PTT, decreased GFR, ICU admission, CVC or PICC placement, active cancer, coagulopathy, and in-hospital antiplatelet drug, steroid, or SSRI use. In the validation set, the Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (0.86 vs. 0.72, p < .001) and, at equivalent sensitivity (54%), categorized fewer patients as high-risk (6.8% vs. 12.1%, p < .001).
Conclusions: From a large population of medical inpatients, we developed and validated a RAM to accurately predict bleeding risk at admission. The CCBM may be used in conjunction with VTE risk calculators to decide between mechanical and pharmacological prophylaxis for at-risk patients.
Keywords: bleeding; clinical prediction; machine learning; risk assessment model.