BACKGROUND AND AIM OF THE STUDY: As with all mechanical prostheses, bileaflet heart valves are prone to thrombus formation, reduced hemodynamic performance, and the occurrence of embolic events. The early detection of thrombotic formations is crucial for correct diagnosis and adequate therapy. The study aim was to analyze the power spectra of the phonocardiographic signals acquired in vitro for various thrombotic deposits reproduced on a bileaflet mechanical valve, in order to monitor and classify their presence. METHODS: Data were acquired for the St. Jude Medical Regent valve mounted in the aortic position of a Sheffield Pulse Duplicator. Different pulsatile flow conditions were reproduced, changing the heart rate and stroke volume. Thrombotic deposits of various weights and shapes were placed on the valve leaflet, or on the annular housing. The case of a thrombus completely blocking one leaflet was also investigated. Power spectra were calculated from the phono-cardiographic signals and classified by an artificial neural network. RESULTS: The proposed approach resulted in a 95% correct identification of all simulated thrombotic deposits. Interestingly, phonocardiographic analysis is capable of detecting the presence of different types of artificial thrombi and also to classify them, whereas transvalvular pressure values cannot provide such detection. CONCLUSION: An effective diagnostic tool capable of detecting valvular thrombosis at the early stages of formation may help clinicians to formulate valvular dysfunction diagnoses before the appearance of critical symptoms. The ability to transfer results obtained in vitro to actual clinical situations might represent a significant advance in the follow up of patients.
BACKGROUND AND AIM OF THE STUDY: As with all mechanical prostheses, bileaflet heart valves are prone to thrombus formation, reduced hemodynamic performance, and the occurrence of embolic events. The early detection of thrombotic formations is crucial for correct diagnosis and adequate therapy. The study aim was to analyze the power spectra of the phonocardiographic signals acquired in vitro for various thrombotic deposits reproduced on a bileaflet mechanical valve, in order to monitor and classify their presence. METHODS: Data were acquired for the St. Jude Medical Regent valve mounted in the aortic position of a Sheffield Pulse Duplicator. Different pulsatile flow conditions were reproduced, changing the heart rate and stroke volume. Thrombotic deposits of various weights and shapes were placed on the valve leaflet, or on the annular housing. The case of a thrombus completely blocking one leaflet was also investigated. Power spectra were calculated from the phono-cardiographic signals and classified by an artificial neural network. RESULTS: The proposed approach resulted in a 95% correct identification of all simulated thrombotic deposits. Interestingly, phonocardiographic analysis is capable of detecting the presence of different types of artificial thrombi and also to classify them, whereas transvalvular pressure values cannot provide such detection. CONCLUSION: An effective diagnostic tool capable of detecting valvular thrombosis at the early stages of formation may help clinicians to formulate valvular dysfunction diagnoses before the appearance of critical symptoms. The ability to transfer results obtained in vitro to actual clinical situations might represent a significant advance in the follow up of patients.