Quantitative assessment of paravalvular leakage after transcatheter aortic valve replacement using a patient-specific pulsatile flow model

BACKGROUND

Quantitative assessment of post-transcatheter aortic valve replacement (TAVR) aortic regurgitation (AR) remains challenging. We developed patient-specific anatomical models with pulsatile flow circuit and investigated factors associated with AR after TAVR.

METHODS

Based on pre-procedural computed tomography (CT) data of the six patients who underwent transfemoral TAVR using a 23-mm SAPIEN XT, anatomically and mechanically equivalent aortic valve models were developed. Forward flow and heart rate of each patient in two days after TAVR were duplicated under mean aortic pressure of 80mmHg. Paravalvular leakage (PVL) volume in basal and additional conditions was measured for each model using an electromagnetic flow sensor. Incompletely apposed tract between the transcatheter and aortic valves was examined using a micro-CT.

RESULTS

PVL volume in each patient-specific model was consistent with each patient's PVL grade, and was affected by hemodynamic conditions. PVL and total regurgitation volume increased with the mean aortic pressure, whereas closing volume did not change. In contrast, closing volume increased proportionately with heart rate, but PVL did not change. The minimal cross-sectional gap had a positive correlation with the PVL volumes (r=0.89, P=0.02). The gap areas typically occurred in the vicinity of the bulky calcified nodules under the native commissure.

CONCLUSIONS

PVL volume, which could be affected by hemodynamic conditions, was significantly associated with the minimal cross-sectional gap area between the aortic annulus and the stent frame. These data may improve our understanding of the mechanism of the occurrence of post-TAVR PVL.

Cox regression with dependent error in covariates

Many survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association 95, 1209-1219; 2006, Statistica Sinica 16, 861-881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided.

Trend-constrained corrected score for proportional hazards model with covariate measurement error

In many medical research studies, survival time is typically the primary outcome of interest. The Cox proportional hazards model is the most popular method to investigate the relationship between covariates and possibly right-censored survival time. However, in many clinical trials, the true covariates may not always be accurately measured due to natural biological fluctuation or instrument error. It is well know that for regression analysis in general, naively using mismeasured covariates in conventional inference procedures may incur substantial estimation bias. In the presence of covariate measurement error, several functional modeling methods have been proposed under the situation where the distribution of the measurement error is known. Among them are parametric corrected score and conditional score. Although both methods are consistent, each suffers from severe problem of multiple roots or absence of appropriate root when the measurement error is substantial. The problem persists even when the sample size is practically large. We conduct a detailed investigation on the pathological behaviors of parametric corrected score and propose an approach of incorporating additional estimating functions to remedy these pathological behaviors. The estimation and inference are then accomplished by means of quadratic inference function. Extensive simulation studies are conducted to evaluate the performance of proposed method.