Successful mitral valve repair is dependent upon a full understanding of normal and abnormal mitral valve anatomy and function. Computational analysis is one such method that can be applied to simulate mitral valve function in order to analyse the roles of individual components and evaluate proposed surgical repair. We developed the first three-dimensional finite element computer model of the mitral valve including leaflets and chordae tendineae; however, one critical aspect that has been missing until the last few years was the evaluation of fluid flow, as coupled to the function of the mitral valve structure. We present here our latest results for normal function and specific pathological changes using a fluid-structure interaction model. Normal valve function was first assessed, followed by pathological material changes in collagen fibre volume fraction, fibre stiffness, fibre splay and isotropic stiffness. Leaflet and chordal stress and strain and papillary muscle force were determined. In addition, transmitral flow, time to leaflet closure and heart valve sound were assessed. Model predictions in the normal state agreed well with a wide range of available in vivo and in vitro data. Further, pathological material changes that preserved the anisotropy of the valve leaflets were found to preserve valve function. By contrast, material changes that altered the anisotropy of the valve were found to profoundly alter valve function. The addition of blood flow and an experimentally driven microstructural description of mitral tissue represent significant advances in computational studies of the mitral valve, which allow further insight to be gained. This work is another building block in the foundation of a computational framework to aid in the refinement and development of a truly non-invasive diagnostic evaluation of the mitral valve. Ultimately, it represents the basis for simulation of surgical repair of pathological valves in a clinical and educational setting.

Background-Renal insufficiency after coronary artery bypass graft (CABG) surgery is associated with increased short-term and long-term mortality. We hypothesized that preoperative patient characteristics could be used to predict the patient-specific risk of developing postoperative renal insufficiency. Methods and Results-Data were prospectively collected on 11 301 patients in northern New England who underwent isolated CABG surgery between 2001 and 2005. Based on National Kidney Foundation definitions, moderate renal insufficiency was defined as a GFR Ͻ60 mL/min/1.73m 2 and severe renal insufficiency as a GFR Ͻ30. Patients with at least moderate renal insufficiency at baseline were eliminated from the analysis, leaving 8363 patients who became our study cohort. A prediction model was developed to identify variables that best predicted the risk of developing severe renal insufficiency using multiple logistic regression, and the predictive ability of the model quantified using a bootstrap validated C-Index (Area Under ROC) and Hosmer-Lemeshow statistic. Three percent of the patients with normal renal function before CABG surgery developed severe renal insufficiency (229/8363). In a multivariable model the preoperative patient characteristics most strongly associated with postoperative severe renal insufficiency included: age, gender, white blood cell count Ͼ12 000, prior CABG, congestive heart failure, peripheral vascular disease, diabetes, hypertension, and preoperative intraaortic balloon pump. The predictive model was significant with 2 150.8, probability value Ͻ0.0001. The model discriminated well, ROC 0.72 (95%CI: 0.68 to 0.75). The model was well calibrated according to the Hosmer-Lemeshow test. Conclusions-We developed a robust prediction rule to assist clinicians in identifying patients with normal, or near normal, preoperative renal function who are at high risk of developing severe renal insufficiency. Physicians may be able to take steps to limit this adverse outcome and its associated increase in morbidity and mortality.