Leveraging Data Science for a Personalized Haemodialysis

Abstract

Background: The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed.

Summary: Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis.

Key messages: Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

Keywords: Artificial intelligence; Data science; Haemodialysis; Machine learning; Personalized medicine; Pragmatic clinical trials.

Fig. 1

Trajectories map of social inclusion of spinal cord injury patients. This is an automatic software-generated visual representation. Each column of nodes represents a data wave. Each node represents one of the clusters found in that wave, which is labelled according to the patient's profile represented by the cluster, after a conceptualization process made by experts on the basis of cluster information automatically provided by the software. In this case, labelling regards the functionality, wellness, and sometimes evolution of the lesion. Colours can be associated with a latent target concept, like, for example, more (in red) or less (in green) global impairment of the patient. This is represented with the vertical position of the nodes in the graph. Edges represent the paths followed by patients along time, and thickness of the edges is associated with observed prevalence of each path. IndepPos, functionally independents, with assistive technologies required, and feel wellness; IndepModAnt, functionally independent with moderate distress and old lesions; SemiDepNeg, very distressed and require help for some specific daily life activities such as moving from bed to wheelchair or going to bathroom; dependents, dependent and psychologically heterogeneous; IndepPositius, independent and feel wellness; IndepModerat, independent and moderate wellness; DepEstoics, dependents, but feel wellness; IndepMod, independents and moderate wellness; and SemidepHetero, with dependence for some specific daily life activities such as moving from bed to wheelchair or going to bathroom, psychologically heterogeneous.