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

在过去的30年里，静脉血栓栓塞（VTE）的发生率没有显著改善。 这些血块在四肢发展，可以移动到肺部并形成肺栓塞，这是肺栓塞的主要原因。 这是医院里最常见的可预防的死亡原因。目前可用于预测和预防的工具 医院获得性VTE（HA-VTE）是在没有来自一线临床医生足够输入的情况下开发的， 工作量大，在日常临床实践中实施起来太麻烦，表现出较差的预测准确性， 不考虑出血并发症的风险。重要的是，这些工具的使用尚未显示出 改善患者预后。因此，在现行的可变做法制度与 在VTE风险评估模式和降低HA-VTE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HA-VTE的实时预后模型， 以便于忙碌的临床医生适当和及时地开始血栓预防。我们的多学科 研究小组开发了一种模型，根据临床表现预测所有成人住院患者中HA-VTE的概率。 因素和病史。模型随着临床情况的发展而更新， 高风险和低风险患者，与现有风险分层相比，显示出上级预测性能 工具.尚不清楚在临床实践中使用HA-VTE预后模型是否能改善患者结局。 为了实现这一重要目标，我们将： 他们在当前风险评估工作流程以及对时间、内容和可视化的偏好方面面临的挑战 预后模型（目标1）;创建用户友好的临床决策支持（CDS）工具-基于准确的 和经验证的HA-VTE预后模型-可无缝集成到现有临床工作流程中， 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， 真实的时间（目标2）;并进行一项实用的随机试验和实施评估的预后 模型加CDS预防与常规护理预防HA-VTE的比较。在一个自适应的平台上 在一项前瞻性试验中，我们将评估模型引导CDS减少HA-VTE的有效性， 总体和关键患者亚组，并通过随机化研究实施策略， 为临床医生提供最佳服务，并改善患者的治疗效果（目标3）。我们将广泛传播可推广的 预防HA-VTE和避免死亡所急需的知识和实施工具， 医院，包括实施手册、CDS知识工件和开源统计软件。 相关性：我们的提案与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;创建可持续的适应性实施策略 降低HA-VTE的发生率;并通过整合多个 数据类型、创新统计方法和信息学方法，以促进广泛传播。

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.