Development of a Novel Venous Thromboembolism Risk Assessment Model for Perioperative Clinical Decision Support

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7. Project Summary / Abstract Venous thromboembolic events (VTE) remain a leading preventable cause of morbidity and mortality in postoperative patients. National guidelines recommend initiation of prophylaxis based on individualized risk stratification. Despite this standard, multiple studies, including an Emory University Hospital study conducted by our group, have shown that most high-risk patients do not receive recommended prophylaxis. Standardized use of VTE risk assessment models (RAMs) in surgical patients has been limited to date due to a myriad of factors including confusion around the risk assessment process, a perceived increased risk of bleeding from administration of prophylactic anticoagulant medications, and the cumbersome additional workflow for RAM calculation. For example, one study found that the detailed history and the capture of non-routine lab tests as required by the Caprini score adds 6 minutes per patient to a physician’s clinic workflow. The potential for automation of this process is an exciting step toward patient care that is both more standardized and more patient-centered, but it requires development of models built on data that is readily available in the pre- operative phase of care such that a care plan can be enacted prospectively. Our group’s previous research demonstrated that readily available data such as a patient’s age, BMI, race, and comorbidity burden – in lieu of a more complex and time-consuming RAM – could be utilized to effectively risk-stratify patients. We hypothesize that implementation of a standardized, brief, and automated VTE risk assessment model in the pre-operative workflow will improve the quality of surgical care by increasing delivery of indicated prophylaxis, thereby decreasing VTE rates and improving perioperative efficiency. With that long-term goal, our proposed specific aims include (1) the application of multivariable logistic regression and decision tree machine learning to the National Surgical Quality Improvement Project database to develop a novel pre-operative VTE RAM and (2) a scoping review of VTE prophylaxis guidelines with the subsequent proposal of a standardized protocol that would facilitate actionable risk stratification. Future research will aim to build the RAM into the electronic health record and subsequent implementation of the proposed protocol, including automation of the developed model and tracking of impact on pertinent process measures and VTE outcomes. Prospective patient-level risk assessment driving pre-operative workflows has the potential to make surgical care more standardized, more patient-centered, and more equitable. The F32 will facilitate further training in analytic methods and translational research that will allow me to pursue these aims, an experience that will be invaluable on my path toward a career as a surgeon scientist and leader in quality improvement.