Dynamic Prediction Modeling to Improve Clinical Predictions

项目摘要 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被用来提高临床医生和研究人员的量化能力， 并传达风险。然而，预测模型仅在其在外部应用时是准确的情况下才有用。 人口是在其中发展起来的。不幸的是，今天使用的许多预测模型被证明是不准确的 当随着时间的推移和新的人口，不仅产生不准确的预测，而且还错误的水平， 对风险评估质量的信心。这通常是因为模型被应用于 具有不同临床特征和疾病风险的患者，与使用的医疗实践不同 发展模型，以及随着时间不断变化的护理方法。当前的科学范式 并不容易让模型适应这些差异。因此，模型的准确性往往 由于多年的临床使用受到损害，新模型开发缓慢（如果有的话），并且这些新模型 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”。“与其使用现有的模型， 实践，而不适应其不可避免的性能退化，充其量，很少发展 具有相同限制的新模型，动态预测建模更新现有预测模型 随着新数据的不断积累。在这种方法中，更新的模型联合收割机组合了 在原始模型中捕获来自新患者的数据，以产生用于未来预测的更新模型。作为 由于这一持续的模型改进过程，动态预测模型有可能增强和 随着时间的推移，在患者人群和医疗实践不断变化的情况下，保持模型的准确性。 我们在这个提案中的目标是开发和测试这种新的模式，通过严格的风险预测， 统计和应用研究，为实际使用动态预测提供全面指导 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 和实践具体而言，本项目将：（1）使用正式和全面的模拟来制定指导方针 实现动态模型的重新校准、修正和扩展;（2）测试和比较这些动态模型， 预测建模方法与传统的预测建模方法在两个真实的世界和不同的 临床设置，然后完善的方法，以提高准确性和普遍性;（3）正式和 前瞻性地测试动态预测建模在大型多中心人群中的实施， 重症监护病房的患者，以证明动态预测建模的实用性，可行性和准确性 方法在现实世界中的设置。最终目的是增强预测的普适性和实用性 模型，并提高我们提供精准护理的能力。

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.