Petersen Teun B, Boersma Eric, Kardys Isabella, Rizopoulos Dimitris
Department of Biostatistics, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Stat Med. 2025 May;44(10-12):e70088. doi: 10.1002/sim.70088.
Patient monitoring is routinely used to detect disease aggravation in many chronic conditions. We propose an adaptive scheduling strategy based on dynamic individual risk predictions that can improve the efficiency of monitoring programs that incorporate multiple longitudinal measurements and competing events. It is motivated by stable chronic heart failure (CHF) patients who are periodically seen to assess the risk of disease aggravation based on multiple patient characteristics and circulating marker protein levels such as NT-proBNP and troponin. We assess the performance of the adaptive strategy versus fixed schedule alternatives using a simulation study based on the Bio-SHiFT study, a cohort of stable CHF patients.
在许多慢性病中，患者监测通常用于检测疾病恶化情况。我们提出了一种基于动态个体风险预测的自适应调度策略，该策略可以提高包含多个纵向测量和竞争事件的监测计划的效率。其灵感来自于稳定的慢性心力衰竭（CHF）患者，他们会定期接受检查，以便根据多种患者特征以及循环标记蛋白水平（如N末端脑钠肽原和肌钙蛋白）来评估疾病恶化风险。我们使用基于Bio-SHiFT研究（一组稳定的CHF患者队列）的模拟研究，评估了自适应策略与固定调度方案相比的性能。
