Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory. We considered different dynamic survival approaches, starting from the simple approach considering the most recent measurement, to the joint model. We then propose a novel method based on wavelet filtering and landmarking to retrieve the prognostic role of past short-term potassium shifts. We argue that while taking into account past information is important, not all past information is equally informative. State-of-the-art dynamic survival models are prone to give more importance to the mean long-term value of potassium. However, our findings suggest that it is essential to take into account also recent potassium instability to capture all the relevant prognostic information. The data used comes from over 2000 subjects, with a total of over 80 000 repeated potassium measurements collected through Administrative Health Records and Outpatient and Inpatient Clinic E-charts. A novel dynamic survival approach is proposed in this work for the monitoring of potassium in heart failure. The proposed wavelet landmark method shows promising results revealing the prognostic role of past short-term changes, according to their different duration, and achieving higher performances in predicting the survival probability of individuals.

翻译：研究纵向生物标志物与死亡风险之间关联的统计方法，对于慢性疾病（如心力衰竭患者的钾水平）患者的长期护理具有重要意义。特别是在存在合并症或药物治疗的情况下，突发危机可能导致钾水平发生急剧但短暂的波动。在钾监测的背景下，需要一种可用于临床实践的动态模型，以评估与观察到的患者钾轨迹相关的死亡风险。我们考虑了不同的动态生存方法，从考虑最近一次测量的简单方法到联合模型。随后，我们提出了一种基于小波滤波和界标法的新方法，以提取过去短期钾波动的预后作用。我们认为，虽然考虑历史信息很重要，但并非所有历史信息都具有同等的信息价值。现有最先进的动态生存模型倾向于更重视钾的长期平均值。然而，我们的研究结果表明，为了捕获所有相关预后信息，必须同时考虑近期的钾不稳定性。所用数据来自超过2000名受试者，通过行政健康记录以及门诊和住院临床电子图表收集了总计超过80,000次重复钾测量值。本研究提出了一种新颖的动态生存方法，用于心力衰竭患者的钾监测。所提出的小波界标法显示出有前景的结果，揭示了不同持续时间的过去短期变化的预后作用，并在预测个体生存概率方面实现了更高性能。