The Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington, Seattle WA, USA.
Department of Biostatistics, University of Washington, Seattle WA, USA.
Med Decis Making. 2022 May;42(4):474-486. doi: 10.1177/0272989X211049213. Epub 2021 Nov 7.
Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty.
We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes.
When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point.
A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care.
In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.
利用重复的生物标志物测量进行患者监测为早期发现和治疗疾病进展提供了机会。在包括癌症和糖尿病在内的几种疾病中，建议并常规进行频繁的生物标志物监测测试。然而，频繁的测试存在权衡。尽管监测测试提供了关于当前疾病状况的信息，但对于进展风险较低的患者，频繁测试的并发症和成本可能不合理。基于患者早期生物标志物值的预测可用于辅助决策；然而，预测具有不确定性，导致决策不确定性。
我们提出了个性化风险自适应监测（PRAISE）框架，这是一种将预测纳入信息价值（VOI）框架的新方法，以随时间考虑不确定性的成本，并确定收集生物标志物数据最有价值的时间点。所提出的序贯决策框架具有创新性，它利用了患者的纵向病史，考虑了个体的收益和危害，并允许通过考虑当前信息的不确定性和估计新信息可能改变治疗决策的概率，以及这种变化对患者结果的影响，来动态调整监测间隔。
当应用于囊性纤维化患者的数据时，与“始终测试”策略相比，PRAISE 通过允许一些患者跳过就诊，可以降低成本。它通过在不太可能在跳过时间点接受治疗的患者中推荐更不频繁的测试，在不影响预期生存的情况下做到了这一点。
基于 VOI 的患者监测方法是可行的，可以应用于多种疾病，以制定更具成本效益和个性化的持续患者护理策略。
在许多患者监测环境中，对于进展风险较低的患者，频繁测试的并发症和成本可能不合理。基于患者病史的预测可以用于根据不断变化的风险来个性化患者就诊的时间。我们提出了个性化风险自适应监测（PRAISE），这是一种个性化监测测试时间的新方法，其中预测模型预测疾病轨迹，基于信息价值（VOI）的实用决策理论框架量化了患者和时间特定的收益-危害权衡。基于 VOI 的患者监测方法可以应用于多种疾病，以制定更具个性化和成本效益的持续患者护理策略。
