Impact of Embedding a Venous Thromboembolism Risk Assessment Model in the Electronic Health Record Versus Usual Care: A Cluster-Randomized Trial.

BACKGROUND: There are multiple risk assessment models (RAMs) for venous thromboembolism prophylaxis, but it is unknown whether they increase appropriate prophylaxis. METHODS: To determine the impact of a RAM embedded in the electronic health record, we conducted a stepped-wedge hospital-level cluster-randomized trial conducted from October 1, 2017 to February 28, 2019 at 10 Cleveland Clinic hospitals. We included consecutive general medical patients aged 18 years or older. Patients were excluded if they had a contraindication to prophylaxis, including anticoagulation for another condition, acute bleeding, or comfort-only care. A RAM was embedded in the general admission order set and physicians were encouraged to use it. The decisions to use the RAM and act on the results were reserved to the treating physician. The primary outcome was the percentage of patients receiving appropriate prophylaxis (high-risk patients with pharmacological thromboprophylaxis plus low-risk patients without prophylaxis) within 48 hours of hospitalization. Secondary outcomes included total patients receiving prophylaxis, venous thromboembolism among high-risk patients at 14 and 45 days, major bleeding, heparin-induced thrombocytopenia, and length of stay. Mixed-effects models were used to analyze the study outcomes. RESULTS: A total of 26 506 patients (mean age, 61; 52% female; 73% White) were analyzed, including 11 134 before and 15 406 after implementation of the RAM. After implementation, the RAM was used for 24% of patients, and the percentage of patients receiving appropriate prophylaxis increased from 43.1% to 48.8% (adjusted odds ratio, 1.11 [1.00-1.23]), while overall prophylaxis use decreased from 73.5% to 65.2% (adjusted odds ratio, 0.87 [0.78-0.97]). Rates of venous thromboembolism among high-risk patients (adjusted odds ratio, 0.72 [0.38-1.36]), rates of bleeding and heparin-induced thrombocytopenia (adjusted odds ratio, 0.19 [0.02-1.47]), and length of stay were unchanged. CONCLUSIONS: Implementation of a RAM for venous thromboembolism increased appropriate prophylaxis use, but the RAM was used for a minority of patients. REGISTRATION: URL: https://www.clinicaltrials.gov/study/NCT03243708?term=nct03243708&rank=1; Unique identifier: NCT03243708.

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

BACKGROUND: Guidelines 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. OBJECTIVE: We 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. METHODS: We 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. RESULTS: Among 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). CONCLUSION: Using 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.

A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients.

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.

Derivation and Validation of a Risk Factor Model to Identify Medical Inpatients at Risk for Venous Thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) prophylaxis is recommended for hospitalized medical patients at high risk for VTE. Multiple risk assessment models exist, but few have been compared in large datasets. METHODS: We constructed a derivation cohort using 6 years of data from 12 hospitals to identify risk factors associated with developing VTE within 14 days of admission. VTE was identified using a complex algorithm combining administrative codes and clinical data. We developed a multivariable prediction model and applied it to three validation cohorts: a temporal cohort, including two additional years, a cross-validation, in which we refit the model excluding one hospital each time, applying the refitted model to the holdout hospital, and an external cohort. Performance was evaluated using the C-statistic. RESULTS: The derivation cohort included 155,026 patients with a 14-day VTE rate of 0.68%. The final multivariable model contained 13 patient risk factors. The model had an optimism corrected C-statistic of 0.79 and good calibration. The temporal validation cohort included 53,210 patients, with a VTE rate of 0.64%; the external cohort had 23,413 patients and a rate of 0.49%. Based on the C-statistic, the Cleveland Clinic Model (CCM) outperformed both the Padua (0.76 vs. 0.72, p = 0.002) and IMPROVE (0.68, p < 0.001) models in the temporal cohort. C-statistics for the CCM at individual hospitals ranged from 0.68 to 0.78. In the external cohort, the CCM C-statistic was similar to Padua (0.70 vs. 0.66, p = 0.17) and outperformed IMPROVE (0.59, p < 0.001). CONCLUSION: A new VTE risk assessment model outperformed recommended models.