Estimated Impact of Model-Guided Venous Thromboembolism Prophylaxis versus Physician Practice
Mittman, B. G.; Rothberg, M. B.
Show abstract
BackgroundThe American Society of Hematology (ASH) recommends assessing venous thromboembolism (VTE) and major bleeding risk to optimize pharmacological VTE prophylaxis for medical inpatients. However, the clinical utility of model-guided approaches remains unknown. MethodsOur objective was to estimate differences in VTE and major bleeding event rates and efficiency with prophylaxis guided by risk models versus prophylaxis based on physician judgment. Patients were adults admitted to one of 10 Cleveland Clinic hospitals between December 2017 and January 2020. We compared physician practice with hypothetical prophylaxis recommended by model- based prophylaxis strategies, including ASH-recommended risk scores (Padua and IMPROVE) and locally derived Cleveland Clinic risk prediction models. For each strategy we quantified the prophylaxis rate, VTE and major bleeding rates, and the incremental number-needed-to-treat (NNT) to prevent one event (VTE or bleeding). ResultsPhysicians prescribed prophylaxis to 62% of patients whereas model-based strategies recommended prophylaxis for 17-87%. Model-guided prophylaxis produced more VTEs and fewer major bleeds than physicians, but total events varied among strategies. Overall, per 1,000 patients, model- based strategies produced 14.0-16.1 events compared with 14.3 for physicians. The Padua/IMPROVE models recommended prophylaxis for the fewest patients but caused the most total events. The most efficient model-based strategy recommended prophylaxis to 28% of patients with an incremental NNT (relative to no prophylaxis) of 80. Compared to physicians, it reduced prophylaxis by 55% and total events by 0.14%. ConclusionsPhysicians often prescribed inappropriate prophylaxis, highlighting the need for decision support. A model-based strategy maximized efficiency, reducing both events and prophylaxis relative to physicians.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients 99%
- Genetic risk and incident venous thromboembolism in middle-aged and older adults following Covid-19 vaccination 90%
- Platelet Activating Immune Complexes Identified in COVID-19 Associated Coagulopathy 90%
Similar papers in this journal
- Incorporating data from multiple endpoints in the analysis of clinical trials: example from RSV vaccines 85%
- Theoretical framework for retrospective studies of the effectiveness of SARS-CoV-2 vaccines 85%
- Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data 85%
Similar papers in this journal
- Incidence and Risk of Post-COVID-19 Thromboembolic Disease and the Impact of Aspirin Prescription; Nationwide Observational Cohort at the US Department of Veteran Affairs 95%
- COVID-19 is associated with higher risk of venous thrombosis, but not arterial thrombosis, compared with influenza: Insights from a large US cohort 95%
- Association of Mortality and Aspirin Prescription for COVID-19 Patients at the Veterans Health Administration 92%
Similar papers in this journal
- Antithrombotic Therapy in COVID-19: Systematic Summary of Ongoing or Completed Randomized Trials 93%
- Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study 88%
- Lipid-Modulating Agents for Prevention or Treatment of COVID-19 in Randomized Trials 87%
Similar papers in this journal
- Hemodilution in High Risk Cardiac Surgery: Laboratory Values, Physiological Parameters and Outcomes 89%
- Predictive accuracy of diagnostic tests for excessive bleeding in cardiac surgery: the COPTIC-C study 88%
- Patient ABO blood type is a major predictor of a positive DAT following a transfusion reaction 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.