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.

项目摘要 风险预测是​​所有临床实践和公共卫生固有的，它一直是科学研究的话题 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 并传达风险。但是，仅当预测模型在外部应用时正确时才有用 开发它的人口。不幸的是，今天使用的许多预测模型证明不准确 随着时间的推移和新人群的应用，不仅会产生不准确的预测，还会产生一个错误的水平 对其风险评估质量的信心。这种通常是因为将模型应用于 具有不同临床特征和疾病风险的患者，与所使用的患者不同 开发模型，以及随着时间的流逝不断变化的护理方法。当前的科学范式 不容易允许模型适应这些差异。结果，模型的准确性通常是 在多年的临床用途中受到损害，开发新模型（如果有的话），这些新模型是 没有比原始模型更好地解释患者人群或医疗实践的能力。 解决这些问题的潜在解决方案是“动态预测建模”。而不是使用现有模型 练习而不适应其不可避免的性能下降，充其量是不经常发展的 具有相同限制的新模型，动态预测建模更新现有的预测模型 随着新数据的计算，不断。在这种方法中，更新的模型结合了信息 在原始模型中捕获了来自新患者的数据，以生成更新的模型以进行未来的预测。作为 这一正在进行的模型进行过程的结果，动态预测模型具有增强和 随着时间的流逝，在不断变化的患者人群和医疗实践的情况下，保持模型准确性。 我们在此提案中的目标是通过严格的风险预测开发和测试这种新的范式 统计和应用研究，以为现实世界使用动态预测提供全面的指导 建模，从而消除临床研究中这些方法更广泛传播这些方法的关键障碍 和练习。具体而言，该项目将：（1）使用正式和全面的模拟制定准则 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 在两个现实世界中使用传统的预测建模方法进行预测建模方法 临床环境，然后完善提高准确性和普遍性的方法； （3）正式 前瞻性测试在大型多中心人口中的动态预测建模的实施 重症监护病房的患者可以证明动态预测建模的实用性，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实用性 建模并提高我们提供精确护理的能力。