Dynamic Prediction Modeling to Improve Clinical Predictions

动态预测建模改善临床预测

• 批准号: 9904186
• 负责人: Stephen E. Kimmel
• 金额: $ 60.94万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2020-12-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904186
• 关键词: Applied Research; Cardiac Surgery procedures; Caring; Characteristics; Clinical; Clinical Research; Clinical Trials; Complex; Critical Illness; Data; Disease; Ensure; Failure; Future; Goals; Guidelines; Heterogeneity; Hospitals; Individual; Intensive Care Units; Lung; Lung Transplantation; Medical; Methodology; Methods; Modeling; Operative Surgical Procedures; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Population; Population Heterogeneity; Preventive Intervention; Probability; Process; Provider; Public Health; Public Health Practice; Quality of Care; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Signs and Symptoms; Specific qualifier value; Technology; Testing; Time; Transplantation; United Network for Organ Sharing; Update; Validation; Work; aortic valve replacement; base; clinical practice; cohort; disorder risk; diverse data; improved; mortality; patient population; patient stratification; personalized care; post-transplant; predictive modeling; prospective test; response; risk prediction model; side effect; simulation

Project Summary Risk prediction is inherent to all clinical practice and public health and has been a topic of scientific research for decades. Formal prediction models are frequently used to enhance clinicians' and researchers' ability to quantify and communicate risk. However, a prediction model is only useful if it is accurate when applied outside of the population within which it was developed. Unfortunately, many prediction models in use today prove inaccurate when applied over time and to new populations, yielding not only inaccurate predictions but also a false level of confidence about the quality of their risk assessments. This commonly occurs because models are applied to patients with different clinical characteristics and risk of disease, to medical practices that differ from those used to develop the model, and to methods of care that constantly change over time. The current scientific paradigm does not readily allow models to accommodate these differences. As a result, model accuracy is often compromised for years of clinical use, new models are slow to be developed (if at all), and these new models are no better able to account for changing patient populations or medical practice than the original models. A potential solution to these problems is `Dynamic Prediction Modeling.' Rather than using existing models in practice without accommodating their inevitable degradation in performance and, at best, infrequently developing new models with the same limitations, dynamic prediction modeling updates an existing prediction model continually as new data are accrued. In this approach, the updated models combine the information that is captured in the original model with data from new patients to produce an updated model for future predictions. As a result of this ongoing model-refinement process, dynamic prediction models have the potential to enhance and maintain model accuracy in the presence of changing patient populations and medical practices over time. Our objective in this proposal is to develop and test this new paradigm for risk prediction through rigorous statistical and applied research, to provide comprehensive guidance for the real-world use of dynamic prediction modeling, and thus to remove critical barriers to the wider dissemination of these methods in clinical research and practice. Specifically, this project will: (1) use formal and comprehensive simulations to develop guidelines for implementing dynamic model recalibration, revision, and extension; (2) test and compare these dynamic prediction modeling approaches with the traditional approach to prediction modeling in two real world and diverse clinical settings, and then refine the methods to enhance accuracy and generalizability; and (3) formally and prospectively test the implementation of dynamic prediction modeling in a large, multicenter population of intensive care unit patients to demonstrate the utility, feasibility, and accuracy of dynamic prediction modeling methods in a real-world setting. The ultimate goal is to enhance the generalizability and usefulness of prediction models and improve our ability to deliver precision care.

项目概要 风险预测是​​所有临床实践和公共卫生所固有的，并且一直是科学研究的主题 几十年。正式的预测模型经常用于增强临床医生和研究人员的量化能力 并沟通风险。然而，预测模型只有在应用于外部时准确时才有用。 其开发的人口。不幸的是，当今使用的许多预测模型被证明是不准确的 当随着时间的推移应用于新的人群时，不仅会产生不准确的预测，而且会产生错误的水平 对风险评估质量的信心。这种情况通常会发生，因为模型应用于 具有不同临床特征和疾病风险的患者，接受与所使用的医疗实践不同的医疗实践 开发模型以及随时间不断变化的护理方法。当前的科学范式 不容易让模型适应这些差异。因此，模型的准确率通常是 经过多年的临床使用，新模型的开发速度很慢（如果有的话），并且这些新模型是 没有比原始模型更好地解释不断变化的患者群体或医疗实践。 这些问题的一个潜在解决方案是“动态预测建模”。而不是使用现有模型 练习时不去适应他们不可避免的性能下降，并且充其量也不会经常发展 新模型具有相同的局限性，动态预测建模更新了现有的预测模型 随着新数据的积累而不断变化。在这种方法中，更新的模型结合了以下信息： 使用来自新患者的数据在原始模型中捕获，以生成用于未来预测的更新模型。作为 由于这种持续的模型细化过程，动态预测模型有潜力增强和 在患者群体和医疗实践随时间变化的情况下保持模型的准确性。 我们在本提案中的目标是通过严格的方法开发和测试这种新的风险预测范式 统计和应用研究，为动态预测的实际使用提供全面指导 建模，从而消除这些方法在临床研究中更广泛传播的关键障碍 并练习。具体来说，该项目将：（1）使用正式且全面的模拟来制定指南 用于实施动态模型重新校准、修订和扩展； (2) 测试并比较这些动态 预测建模方法与传统的预测建模方法在两个现实世界中存在差异 临床环境，然后完善方法以提高准确性和普遍性； (3) 正式地和 前瞻性地测试动态预测模型在大型、多中心人群中的实施情况 向重症监护病房患者展示动态预测模型的实用性、可行性和准确性 现实环境中的方法。最终目标是增强预测的普遍性和有用性 模型并提高我们提供精准护理的能力。