Clinical Background

Acute Coronary Syndrome
In contrast to the stable angina cohort, many patients who experience acute syndrome are triaged directly to the cathlab without receiving non-invasive diagnostic imaging. This is due to the time-sensitive nature of the management strategy. Thus it would be diagnostically beneficial to be able to extract as many functional indices as possible from the data that can be collected in the cathlab, which typically are limited to the pressure/velocity wire data and fluoroscopic imaging.

Cathlab Diagnostics
The advancements in the pressure- and Doppler-wire technology has allowed several new developments in coronary diagnostics. Of these, wave intensity analysis (WIA) has shown a lot of untapped potential, in terms of providing not only the haemodynamic assessment, but also of myocardial viability and function thereby capturing a more complete picture of the patient's aetiology to support robust clinical decision making. Our research is addressing cathlab diagnostics on several fronts, from developing improved methodologies for WIA processing to physiological investigations in simultaneous coronary and LV cathether signals, personalised modelling, and large-scale statistical analysis of such models for rapid clinical applications.

 

Scientific Approaches

Wave Intensity Analysis: Practical Aspects
At present, WIA is performed in a limited number of research groups in the world and there is no consensus on protocol or implementation. Achieving standardisation is a crucial step in order to be able to share and compare research data and assessing predictive values of WIA-derived metrics for specific disease conditions. We have previously shown that the method by which pressure & velocity signals are pre-processed impacts significantly on the derived output metrics, so as to make comparisons of published data difficult. This is because WIA depends on the time rate, rather than the magnitude of the signals, such that it exhibits heightened sensitivity to smoothing. The standard Savitzky-Golay filter usually applied for smoothing is susceptible to inter-operator variability and short-comings of the 'eye ball norm'. To this end we have developed an automated WIA pipeline based on an adaptive Savitzky-Golay filter which eliminates user-parameters and provides optimal signal . This approach was validated using noise-added in silico traces as well as on real-world patient data, and allows for the possibility of beat-wise application of WIA. This work is decribed in our upcoming publication, currently in review.

                                           beat-wise coronary wave intensity analysis is enabled by the use of adaptive savitzky-golay filter as a pre-processor

                                           beat-wise coronary wave intensity analysis is enabled by the use of adaptive savitzky-golay filter as a pre-processor


Flow-Contraction Coupling
Investigating the relationship between cardiac events and coronary waves requires a measurement system which can resolve rapid changes that coincide in the signals throughout the cycle. Due to the procedural and technological challenges, simultaneous coronary-LV measurements have not been collected in humans until recently. In collaboration with the interventional cardiologists at King's we have conducted a comprehensive integrated analysis of PV-loop and cWIA data that have been collected in patients. The investigation uncovered previously unreported correlations between specific coronary waves and LV functional indices, identifying new targets for clinical investigation, as described in detail here.

Coronary waves superimposed over LV pressure-volume loop. The data were collected simultaneously using a conduction catheter and a combowire

Coronary waves superimposed over LV pressure-volume loop. The data were collected simultaneously using a conduction catheter and a combowire

 

Patient-Specific Coronary Modelling
A milestone objective for this work is to culminate in a clinical application based on cWIA. To this end, we have built a personalised coronary-ventricular (1D-0D) modelling pipeline and characterised its input-output relationship exhaustively through statistical meta-modelling. Such a model will help to characterise pathphysiology of specific patients as well as discovering sensitive coronary-derived indices of overall cardiac function. A database of 3000 simulations were used to train the model which established statistical relationships between PV-loop derived metrics and the coronary waves observed. Work is currently under way to establish a rapid inverse personalisation using this modelling framework, which will lead to a clinical validation. This work will facilitate the translation of the wider developments described above towards routine cathlab applications.

An example of 1D coronary flow model fitted to patient-specific data. Bottom row shows that coronary wave intensities can be accurately reproduced

An example of 1D coronary flow model fitted to patient-specific data. Bottom row shows that coronary wave intensities can be accurately reproduced