Can data fusion techniques predict adverse physiological events during haemodialysis?

Intra-dialytic haemodynamic instability is a common and disabling problem which may lead to morbidity and mortality though repeated organ ischaemia, but it has proven difficult to link any particular blood pressure threshold with hard patient outcomes. The relationship between blood pressure and downstream organ ischaemia during haemodialysis has not been well characterised. Previous attempts to predict and prevent intra-dialytic hypotension have had mixed results, partly due to patient and event heterogeneity.
Using the brain as the indicator organ, we aimed to model the dynamic relationship between blood pressure, real-time symptoms, downstream organ ischaemia during haemodialysis, in order to identify the most physiologically grounded, prognostic definition of intra-dialytic decompensation. Following on from this, we aimed to predict the onset of intra-dialytic decompensation using personalised, probabilistic models of multivariate, continuous physiological data, ultimately working towards an early warning system for intra-dialytic adverse events.
This was a prospective study of 60 prevalent haemodialysis patients who underwent extensive, continuous physiological monitoring of haemodynamic, cardiorespiratory, tissue oxygenation and dialysis machine parameters for 3-4 weeks. In addition, longitudinal cognitive function testing was performed at baseline and at 12 months.
Despite their use in clinical practice, we found that blood pressure thresholds alone have a poor trade off between sensitivity and specificity for predicting downstream tissue ischaemia during haemodialysis. However, the performance of blood pressure thresholds could be improved by stratification for the presence or absence of cerebral autoregulation, and personalising thresholds according to the individual lower limit of autoregulation. For patients without autoregulation, the optimal blood pressure target was a mean arterial pressure (MAP) of 70mmHg. A key finding was that cumulative intra-dialytic exposure to cerebral ischaemia, but not to hypotension per se, corresponded to change in executive cognitive function over 12 months. Therefore we chose cerebral ischaemia as the definition of intra-dialytic decompensation for predictive modelling.
We were able to demonstrate that the development of cerebral desaturation could be anticipated from earlier deviations of univariate physiological data from the expected trajectory for a given patient, but sensitivity was limited by the heterogeneity of events even within one individual. The most useful phys- iological data streams included peripheral saturation variance, cerebral saturation variance, heart rate and mean arterial pressure. Multivariate data fusion techniques using these variables created promising personalised models capable of giving an early warning of decompensation. Future work will involve the refinement and prospective testing of these models. In addition, we envisage a prospective study assessing the benefit of autoregulation-guided blood pressure targets on short term outcomes such as patient symptoms and wellbeing, as well as longer term outcomes such as cognitive function.

Keywords: Measurement error
Machine learning
Hypotension
Cerebral ischaemia
Tissue perfusion
Haemodialysis
Advisor: Pugh, C
Tarassenko, L