# Flow-Based Coronary Artery Bypass Graft Patency Metrics: Uncertainty Quantification Simulations to Guide Development

## TL;DR

This study uses simulations to understand how uncertain current methods are for evaluating coronary artery bypass grafts, aiming to improve diagnostic accuracy.

## Contribution

The paper introduces a novel diastolic resistance index and identifies key physiological factors affecting graft patency metrics.

## Key findings

- Metrics quantifying diastolic dominance show highest sensitivity to graft stenosis.
- Stenosis severity, cardiac period, and graft diameter are important parameters influencing patency metrics.
- The novel diastolic resistance index is most sensitive to stenosis severity.

## Abstract

Over time, transit time flow measurement (TTFM) has proven itself as a simple and effective tool for intra-operative evaluation of coronary artery bypass grafts (CABGs). However, metrics used to screen for possible technical error show considerable spread, preventing the definition of sharp cut-off values to distinguish between patent, questionable, and failed grafts. The simulation study presented in this paper aims to quantify this uncertainty for commonly used patency metrics, and to identify the most important physiological parameters influencing it.

Uncertainty quantification was performed on a realistic multiscale numerical model of the coronary circulation, guided by Morris screening sensitivity analysis of a simpler, lumped-parameter model. Simulation results were qualitatively verified against results of a recent clinical study.

Correspondence with clinical study data is reasonable, especially considering that the model was not fitted in any way. Stenosis severity was confirmed to be an influential parameter. However, also cardiac period and graft diameter were observed to be important, particularly for mean flow rate and pulsatility index.

Metrics quantifying the flow waveform’s diastolic dominance show the highest sensitivity to graft stenosis, and seem to be least affected by autoregulation. Among these, the novel diastolic resistance index shows the strongest sensitivity to stenosis severity.

The approach used in this study is expected to benefit the development of improved patency metrics, by allowing medical engineers to include sensitivity and uncertainty in assessing, in-silico, the potential of novel metrics, thus enabling them to provide better guidance in the design of clinical studies.