RESUMO
Background: The use of rapid-deployment valves (RDVs) has been shown to reduce the operative time for surgical aortic valve replacement (AVR). Long-term core laboratory-adjudicated data are scarce, however. Here we report final 7-year data on RDV use. Methods: TRANSFORM was a prospective, nonrandomized, multicenter, single-arm trial implanting a stented bovine pericardial valve with an incorporated balloon-expandable sealing frame. A prior published 1-year analysis included 839 patients from 29 centers. An additional 46 patients were enrolled and implanted, for a total of 885 patients. Annual clinical and core laboratory-adjudicated echocardiographic outcomes were collected through 8 years. Primary endpoints were structural valve deterioration (SVD), all-cause reintervention, all-cause valve explantation, and all-cause mortality. Secondary endpoints included hemodynamic performance assessed by echocardiography. The mean duration of follow-up was 5.0 ± 2.0 years. Results: The mean patient age was 73.3 ± 8.2 years. Isolated AVR was performed in 62.1% of the patients, and AVR with concomitant procedures was performed in 37.9%. Freedom from all-cause mortality at 7 years was 76.0% for isolated AVR and 68.2% for concomitant AVR. Freedom from SVD, all-cause reintervention, and valve explantation at 7 years was 97.5%, 95.7%, and 97.8%, respectively. The mean gradient and effective orifice area at 7 years were 11.1 ± 5.3 mm Hg and 1.6 ± 0.3 cm2, respectively. Paravalvular leak at 7 years was none/trace in 88.6% and mild in 11.4%. In patients undergoing isolated AVR, the cumulative probability of pacemaker implantation was 13.9% at 30 days, 15.5% at 1 year, and 21.8% at 7 years. Conclusions: AVR for aortic stenosis using an RDV is associated with low rates of late adverse events. This surgical pericardial tissue platform provides excellent and stable hemodynamic performance through 7 years.
RESUMO
OBJECTIVE: To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. METHODS: A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. RESULTS: We found a statistically significant learning curve (P < .01). The institutional learning percentage∗ derived from total robot times for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2) = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). CONCLUSIONS: Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis.