OBJECTIVE: Coronary wave intensity analysis (cWIA) has increasingly been applied in the clinical research setting to distinguish between the proximal and distal mechanical influences on coronary blood flow. Recently, a cWIA-derived clinical index demonstrated prognostic value in predicting functional recovery postmyocardial infarction. Nevertheless, the known operator dependence of the cWIA metrics currently hampers its routine application in clinical practice. Specifically, it was recently demonstrated that the cWIA metrics are highly dependent on the chosen Savitzky-Golay filter parameters used to smooth the acquired traces. Therefore, a novel method to make cWIA standardized and automatic was proposed and evaluated in vivo. METHODS: The novel approach combines an adaptive Savitzky-Golay filter with high-order central finite differencing after ensemble-averaging the acquired waveforms. Its accuracy was assessed using in vivo human data. The proposed approach was then modified to automatically perform beat wise cWIA. Finally, the feasibility (accuracy and robustness) of the method was evaluated. RESULTS: The automatic cWIA algorithm provided satisfactory accuracy under a wide range of noise scenarios (≤10% and ≤20% error in the estimation of wave areas and peaks, respectively). These results were confirmed when beat-by-beat cWIA was performed. CONCLUSION: An accurate, standardized, and automated cWIA was developed. Moreover, the feasibility of beat wise cWIA was demonstrated for the first time. SIGNIFICANCE: The proposed algorithm provides practitioners with a standardized technique that could broaden the application of cWIA in the clinical practice as enabling multicenter trials. Furthermore, the demonstrated potential of beatwise cWIA opens the possibility investigating the coronary physiology in real time.
OBJECTIVE: Coronary wave intensity analysis (cWIA) has increasingly been applied in the clinical research setting to distinguish between the proximal and distal mechanical influences on coronary blood flow. Recently, a cWIA-derived clinical index demonstrated prognostic value in predicting functional recovery postmyocardial infarction. Nevertheless, the known operator dependence of the cWIA metrics currently hampers its routine application in clinical practice. Specifically, it was recently demonstrated that the cWIA metrics are highly dependent on the chosen Savitzky-Golay filter parameters used to smooth the acquired traces. Therefore, a novel method to make cWIA standardized and automatic was proposed and evaluated in vivo. METHODS: The novel approach combines an adaptive Savitzky-Golay filter with high-order central finite differencing after ensemble-averaging the acquired waveforms. Its accuracy was assessed using in vivo human data. The proposed approach was then modified to automatically perform beat wise cWIA. Finally, the feasibility (accuracy and robustness) of the method was evaluated. RESULTS: The automatic cWIA algorithm provided satisfactory accuracy under a wide range of noise scenarios (≤10% and ≤20% error in the estimation of wave areas and peaks, respectively). These results were confirmed when beat-by-beat cWIA was performed. CONCLUSION: An accurate, standardized, and automated cWIA was developed. Moreover, the feasibility of beat wise cWIA was demonstrated for the first time. SIGNIFICANCE: The proposed algorithm provides practitioners with a standardized technique that could broaden the application of cWIA in the clinical practice as enabling multicenter trials. Furthermore, the demonstrated potential of beatwise cWIA opens the possibility investigating the coronary physiology in real time.
Authors: Baldeep S Sidhu; Simon Claridge; Haotian Gu; Ye Li; Justin Gould; Bradley Porter; Mark K Elliott; Vishal Mehta; Tom Jackson; Tiffany Patterson; Natalia Briceno; Jack Lee; Simon Redwood; Shaumik Adhya; Steven A Niederer; Phil Chowienczyk; Christopher A Rinaldi Journal: Heart Rhythm O2 Date: 2021-05-28
Authors: Stefano F deMarchi; Christian Gassmann; Tobias Traupe; Steffen Gloekler; Stéphane Cook; Rolf Vogel; Kurt Gysi; Christian Seiler Journal: Open Heart Date: 2019-10-23
Authors: Tiffany Patterson; Simone Rivolo; Daniel Burkhoff; Jan Schreuder; Natalia Briceno; Christopher Allen; Rupert Williams; Satpal Arri; Kaleab N Asrress; Jubin Joseph; Hannah Z R McConkey; Howard Ellis; Antonis Pavlidis; Brian Clapp; Divaka Perera; Jack Lee; Michael S Marber; Simon R Redwood Journal: J Cardiovasc Transl Res Date: 2021-03-15 Impact factor: 4.132