Development of a Novel Venous Thromboembolism Risk Assessment Model for Perioperative Clinical Decision Support
开发用于围手术期临床决策支持的新型静脉血栓栓塞风险评估模型
基本信息
- 批准号:10748559
- 负责人:
- 金额:$ 7.18万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
7. 项目摘要/摘要
项目成果
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