Optimizing the implementation of personalized risk-prediction models for venous thromboembolism among hospitalized adults

优化住院成人静脉血栓栓塞个性化风险预测模型的实施

• 批准号: 10658198
• 负责人: BENJAMIN FRENCH
• 金额: $ 77.51万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658198
• 关键词: Academic Medical Centers; Acceleration; Address; Adult; Automobile Driving; Blood Coagulation Disorders; Blood coagulation; Calibration; Cessation of life; Clinical; Clinical Informatics; Clinical Management; Coagulation Process; Collection; Communities; Computer software; Conceptions; Data; Data Science; Development; Discrimination; Disease; Dissemination and Implementation; Effectiveness; Electronic Health Record; Embolism; Ensure; Equity; Evaluation; Exhibits; Failure; Goals; Heart Diseases; Hematological Disease; Hemorrhage; Hospitalization; Hospitals; Informatics; Infrastructure; Inpatients; Institute of Medicine (U.S.); Interdisciplinary Study; Intervention; Interview; Judgment; Knowledge; Leg; Limb structure; Lung; Lung diseases; Manuals; Measures; Medical Errors; Medical History; Methodology; Modeling; Morphologic artifacts; National Heart, Lung, and Blood Institute; Needs Assessment; Outcome; Output; Patient-Focused Outcomes; Patients; Pattern; Performance; Predictive Analytics; Prevention; Probability; Prophylactic treatment; Provider; Qualitative Methods; Randomized; Randomized, Controlled Trials; Recording of previous events; Reproducibility; Research; Resources; Risk; Risk Assessment; Risk Estimate; Risk Factors; Sleep Disorders; Statistical Methods; System; Testing; Time; Travel; Update; Visualization; Work; Workload; arm; clinical decision support; clinical practice; evidence base; implementation evaluation; implementation research; implementation science; implementation strategy; implementation tool; improved; innovation; interest; machine learning method; member; model development; multidisciplinary; multiple data types; open source; patient subsets; personalized risk prediction; pragmatic randomized trial; predictive modeling; predictive tools; preference; prevent; preventable death; prognostic model; prospective; provider communication; response; risk prediction model; risk stratification; support tools; tool; treatment as usual; uptake; user-friendly; venous thromboembolism

In the last 30 years, there has been no significant improvement in rates of venous thromboembolism (VTE). These blood clots develop in the limbs and can travel to the lungs and form pulmonary emboli, which are the most common cause of preventable deaths in the hospital. Currently available tools for predicting and preventing hospital-acquired VTE (HA-VTE) were developed without sufficient input from frontline clinicians, add to clinician workload, are too cumbersome to implement in daily clinical practice, exhibit poor-to-fair prediction accuracy, and do not consider the risk of bleeding complications. Importantly, use of these tools has not been shown to improve patient outcomes. A significant gap therefore exists between the current system of variable practice patterns in VTE risk assessment and the goal of driving down rates of HA-VTE and reducing preventable deaths. Our objective is to refine, implement, and test a real-time prognostic model for HA-VTE among hospitalized adults to facilitate appropriate and timely initiation of thromboprophylaxis by busy clinicians. Our multidisciplinary team has developed a model that predicts the probability of HA-VTE among all adult inpatients based on clinical factors and medical history. The model updates as the clinical scenario evolves, discriminates well between high- and low-risk patients, and exhibits superior prediction performance compared with extant risk-stratification tools. It is unknown whether use of a prognostic model for HA-VTE in clinical practice improves patient outcomes. To achieve this important objective, we will: conduct observations and interviews with clinicians to elucidate their challenges with the current risk-assessment workflow and preferences for timing, content, and visualization of a prognostic model (Aim 1); create user-friendly clinical decision support (CDS) tools—based on an accurate and validated prognostic model for HA-VTE—that can be seamlessly integrated into existing clinical workflows, simultaneously consider the risk of bleeding complications, and maximize use of electronic health record data in real time (Aim 2); and conduct a pragmatic randomized trial and implementation evaluation of the prognostic model plus CDS for prophylaxis compared with usual care for the prevention of HA-VTE. In an adaptive platform trial, we will evaluate on a prospective basis the effectiveness of model-guided CDS to reduce HA-VTE, both overall and among key patient subgroups, and study through randomization the implementation strategies that work best for clinicians and improve patient outcomes (Aim 3). We will broadly disseminate the generalizable knowledge and implementation tools that are urgently needed to prevent HA-VTE and avoid deaths in the hospital, including an implementation manual, CDS knowledge artifacts, and open-source statistical software. Relevance: Our proposal closely aligns with NHLBI objectives, namely: developing and optimizing a real-time prognostic model to prevent HA-VTE, a HLBS disease; creating sustainable, adaptive implementation strategies to reduce rates of HA-VTE; and leveraging emerging opportunities in data science through integration of multiple types of data, innovative statistical methods, and informatics methodology to facilitate broad dissemination.

在过去的 30 年里，静脉血栓栓塞 (VTE) 的发生率没有显着改善。 这些血凝块在四肢形成，可以移动到肺部并形成肺栓塞，这就是 目前可用于预测和预防的医院可预防死亡的最常见原因。 临床医生补充说，医院获得性 VTE (HA-VTE) 的开发没有得到一线忠诚者的充分投入 工作量大，在日常临床实践中实施起来太麻烦，预测准确性较差， 并且不考虑出血并发症的风险。重要的是，这些工具的使用尚未被证明可以改善。 因此，当前的可变实践系统之间存在显着差距。 VTE 风险评估模式以及降低 HA-VTE 发生率和减少可预防死亡的目标。 我们的目标是完善、实施和测试住院患者中 HA-VTE 的实时预后模型 成人，以促进忙碌的人适当和及时地开始血栓预防。 研究小组开发了一个模型，根据临床情况预测所有成人住院患者发生 HA-VTE 的概率 该模型随着临床情况的发展而更新，可以很好地区分。 高风险和低风险患者，与现有的风险分层相比，表现出优越的预测性能 目前尚不清楚在临床实践中使用 HA-VTE 预后模型是否可以改善患者的预后。 为了实现这一重要目标，我们将： 对追随者进行观察和访谈，以阐明 他们对当前风险评估工作流程的挑战以及对时间、内容和可视化的偏好 预后模型（目标 1）；基于准确的预测创建用户友好的临床决策支持 (CDS) 工具 以及经过验证的 HA-VTE 预后模型——可以无缝集成到现有的临床工作流程中， 同时考虑出血并发症的风险，并最大限度地利用电子健康记录数据 实时（目标 2）；并进行实用的随机试验和预后评估 模型加 CDS 预防与在适应性平台中预防 HA-VTE 的常规护理进行比较。 试验中，我们将前瞻性地评估模型引导的 CDS 减少 HA-VTE 的有效性， 整体和关键患者亚组之间，并通过随机化研究实施策略 为忠诚者提供最好的服务并改善患者的治疗效果（目标 3）。 预防 HA-VTE 和避免死亡所迫切需要的知识和实施工具 医院，包括实施手册、CDS 知识工件和开源统计软件。 相关性：我们的建议与 NHLBI 的目标密切相关，即：开发和优化实时 预防 HA-VTE（一种 HLBS 疾病）的预后模型，制定可持续、适应性实施策略； 降低 HA-VTE 的发生率；并通过整合多种方法来利用数据科学中的新兴机会 数据类型、创新统计方法和信息学方法，以促进广泛传播。