Optimal Tricuspid Regurgitation Velocity to Screen for Pulmonary Hypertension in Tertiary Referral Centers.

BACKGROUND: A mean pulmonary artery pressure >20 mm Hg now defines pulmonary hypertension. We hypothesize that echocardiographic thresholds must be adjusted. RESEARCH QUESTION: Should tricuspid regurgitation velocity thresholds to screen for pulmonary hypertension be revised, given the new hemodynamic definition? STUDY DESIGN AND METHODS: This multicenter retrospective study included 1,608 patients who underwent both echocardiography and right heart catherization within 4 weeks. The discovery cohort consisted of 1,081 individuals; the validation cohort included 527. Screening criteria for pulmonary hypertension were derived with the use of receiver operating characteristic analysis and the Youden index, assuming equal cost for false-positive and -negative classification. A lower threshold was calculated with the use of a predefined sensitivity: 95%. RESULTS: In the discovery cohort, echocardiographic tricuspid regurgitation velocity had a good discrimination for pulmonary hypertension: area under the curve, 88.4 (95% CI, 85.3-91.5). A 3.4-m/s threshold provided a 78% sensitivity, 87% specificity, and 6.13 positive likelihood ratio to detect pulmonary hypertension; 2.7 m/s had a 95% sensitivity and 0.12 negative likelihood ratio to exclude pulmonary hypertension. In the validation cohort, the discovery threshold of 2.7 m/s provided sensitivity and negative likelihood ratios of 80% and 0.31, respectively. Right cardiac size improved detection of pulmonary hypertension in the lower tricuspid regurgitation velocity groups. INTERPRETATION: Our data support a lower tricuspid regurgitation velocity of approximately 2.7 m/s for screening pulmonary hypertension, with a high sensitivity in tertiary referral centers. Right heart chamber measurements improve the diagnostic yield of echocardiography.

Rapid sampling of stochastic displacements in Brownian dynamics simulations with stresslet constraints.

Brownian Dynamics simulations are an important tool for modeling the dynamics of soft matter. However, accurate and rapid computations of the hydrodynamic interactions between suspended, microscopic components in a soft material are a significant computational challenge. Here, we present a new method for Brownian dynamics simulations of suspended colloidal scale particles such as colloids, polymers, surfactants, and proteins subject to a particular and important class of hydrodynamic constraints. The total computational cost of the algorithm is practically linear with the number of particles modeled and can be further optimized when the characteristic mass fractal dimension of the suspended particles is known. Specifically, we consider the so-called "stresslet" constraint for which suspended particles resist local deformation. This acts to produce a symmetric force dipole in the fluid and imparts rigidity to the particles. The presented method is an extension of the recently reported positively split formulation for Ewald summation of the Rotne-Prager-Yamakawa mobility tensor to higher order terms in the hydrodynamic scattering series accounting for force dipoles [A. M. Fiore et al., J. Chem. Phys. 146(12), 124116 (2017)]. The hydrodynamic mobility tensor, which is proportional to the covariance of particle Brownian displacements, is constructed as an Ewald sum in a novel way which guarantees that the real-space and wave-space contributions to the sum are independently symmetric and positive-definite for all possible particle configurations. This property of the Ewald sum is leveraged to rapidly sample the Brownian displacements from a superposition of statistically independent processes with the wave-space and real-space contributions as respective covariances. The cost of computing the Brownian displacements in this way is comparable to the cost of computing the deterministic displacements. The addition of a stresslet constraint to the over-damped particle equations of motion leads to a stochastic differential algebraic equation (SDAE) of index 1, which is integrated forward in time using a mid-point integration scheme that implicitly produces stochastic displacements consistent with the fluctuation-dissipation theorem for the constrained system. Calculations for hard sphere dispersions are illustrated and used to explore the performance of the algorithm. An open source, high-performance implementation on graphics processing units capable of dynamic simulations of millions of particles and integrated with the software package HOOMD-blue is used for benchmarking and made freely available in the supplementary material.

Rapid sampling of stochastic displacements in Brownian dynamics simulations.

We present a new method for sampling stochastic displacements in Brownian Dynamics (BD) simulations of colloidal scale particles. The method relies on a new formulation for Ewald summation of the Rotne-Prager-Yamakawa (RPY) tensor, which guarantees that the real-space and wave-space contributions to the tensor are independently symmetric and positive-definite for all possible particle configurations. Brownian displacements are drawn from a superposition of two independent samples: a wave-space (far-field or long-ranged) contribution, computed using techniques from fluctuating hydrodynamics and non-uniform fast Fourier transforms; and a real-space (near-field or short-ranged) correction, computed using a Krylov subspace method. The combined computational complexity of drawing these two independent samples scales linearly with the number of particles. The proposed method circumvents the super-linear scaling exhibited by all known iterative sampling methods applied directly to the RPY tensor that results from the power law growth of the condition number of tensor with the number of particles. For geometrically dense microstructures (fractal dimension equal three), the performance is independent of volume fraction, while for tenuous microstructures (fractal dimension less than three), such as gels and polymer solutions, the performance improves with decreasing volume fraction. This is in stark contrast with other related linear-scaling methods such as the force coupling method and the fluctuating immersed boundary method, for which performance degrades with decreasing volume fraction. Calculations for hard sphere dispersions and colloidal gels are illustrated and used to explore the role of microstructure on performance of the algorithm. In practice, the logarithmic part of the predicted scaling is not observed and the algorithm scales linearly for up to 4×106 particles, obtaining speed ups of over an order of magnitude over existing iterative methods, and making the cost of computing Brownian displacements comparable to the cost of computing deterministic displacements in BD simulations. A high-performance implementation employing non-uniform fast Fourier transforms implemented on graphics processing units and integrated with the software package HOOMD-blue is used for benchmarking.