A Novel Multiparametric Score for the Detection and Grading of Prosthetic Mitral Valve Obstruction in Cases With...

Prosthetic mechanical valves are the elective choice in mitral valve (MV) replacement, because of their reliability and easiness of implantation. However, these prostheses can suffer from complications, the major one being prosthetic mitral valve thrombosis (PMVT). In these cases, transthoracic doppler echocardiogram (TDE) is the standard diagnostic workup for diagnosis of valve malfunction. The American Society of Echocardiography (ASE) indicates the possible TDE-derived indexes, which can help in identifying insurgence of MV replacement complications. Unfortunately, in some cases, it is not possible to detect PMVT based on these criteria. In these cases, we speak of Doppler silent thrombosis and only more accurate and invasive analyses, such as fluoroscopy, allow for a correct diagnosis. In this work, computational fluid dynamic models were implemented to simulate valve fluid dynamics in different clinical scenarios in order to improve the reliability of PMVT diagnosis based on TDE. In detail, seven mechanical valve configurations, associated to different potential thrombotic conditions (symmetric and asymmetric stenosis), were designed and tested using five pathologic transmitral velocity profile, extracted from real TDE images; to obtain the flow rate profiles, each TDE velocity profile was scaled to yield a mean flow rate (MFR) of 4, 5 and 6 L/min, respectively. As a result, 105 (7 × 5 × 3) synthetic cases, accounting for different velocity profiles, MFRs and valve configurations, were simulated. TDE-derived indexes were calculated according to the ASE guidelines that were extracted. Advanced statistical methods were applied to propose a new diagnostic algorithm for detecting PMVT. Our results showed that there isn't any significant difference between symmetric and asymmetric stenosis, probe location and flow rate waveform and confirmed that the single modality diagnostic is not able to predict thrombosis in a relevant number of cases, referable to mild and mild-severe stenosis cases. To overcome the problem, a novel multi-parametric discrete score based on the designed diagnostic algorithm was attained and tested; the percentage of stenosis (POS) was predicted with an accuracy rate of 90.5%. Even more interestingly, the error rate of 9.5% is related to four false positive cases corresponding to mild stenosis (POS = 15%) which were erroneously classified as mild-severe stenosis. No false negatives were obtained. Our results suggest that a reliable estimation must take into account the mean flow rate as well as the transmitral velocity profile in order to provide a correct diagnosis.