  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.
