Development of a multimodal kidney age prediction based on automatic segmentation CT image in patients with normal renal function.

Background: For decades, description of renal function has been of interest to clinicians and researchers. Serum creatinine (Scr) and estimated glomerular filtration rate (eGFR) are familiar but also limited in many circumstances. Meanwhile, the physiological volumes of the kidney cortex and medulla are presumed to change with age and have been proven to change with decreasing kidney function. Methods: We recruited 182 patients with normal Scr levels between October 2021 and February 2022 in Peking Union Medical College Hospital (PUMCH) with demographic and clinical data. A 3D U-Net architecture is used for both cortex and medullary separation, and volume calculation. In addition, we included patients with the same inclusion criteria but with diabetes (PUMCH-DM test set) and diabetic nephropathy (PUMCH-DN test set) for internal comparison to verify the possible clinical value of "kidney age" (K-AGE). Results: The PUMCH training set included 146 participants with a mean age of 47.5 ± 7.4 years and mean Scr 63.5 ± 12.3 µmol/L. The PUMCH test set included 36 participants with a mean age of 47.1 ± 7.9 years and mean Scr 66.9 ± 13.0 µmol/L. The multimodal method predicted K-AGE approximately close to the patient's actual physiological age, with 92% prediction within the 95% confidential interval. The mean absolute error increases with disease progression (PUMCH 5.00, PUMCH-DM 6.99, PUMCH-DN 9.32). Conclusion: We established a machine learning model for predicting the K-AGE, which offered the possibility of evaluating the whole kidney health in normal kidney aging and in disease conditions.

Renal Outcomes in Older Adults with Antineutrophil Cytoplasmic Autoantibody-Associated Vasculitis: A New Prediction Model.

INTRODUCTION: Older patients with antineutrophil cytoplasmic autoantibody-associated vasculitis (AAV) commonly experience renal impairment and poor prognoses. This study aimed to establish a risk-scoring system for predicting composite renal outcomes in older patients with AAV. METHODS: This retrospective observational study included all patients with AAV hospitalized in a single-center tertiary hospital in China between January 2013 and April 2022. Patients aged ≥65 years were defined as older adults and short-term composite renal outcomes included a ≥25% reduction in estimated glomerular filtration rate (eGFR) (for AKI), renal replacement therapy, provision of renal replacement therapy (long-term dialysis, kidney transplant, or sustained eGFR <15 mL/min/1.73 m), or all-cause mortality. Patients were randomly divided into development and validation cohorts (2:1). Logistic regression analysis was performed in the development cohort to analyze risk factors. The scoring system was established accordingly and further validated in the validation cohort. RESULTS: 1,203 patients were enrolled in the study, among whom the older adult group accounted for 36% with a mean age of 71. The older adult group had a worse prognosis, a higher mortality rate, a higher rate of end-stage renal disease, and worsening renal function. Logistic regression showed that age >75 years, chronic heart disease, and elevated serum creatinine and D-dimer values were risk factors for poor prognosis in patients with AAV. The development and validation cohorts in patients with AAV produced area under the curve values of 0.82 (0.78-0.86) and 0.83 (0.77-0.89), respectively. CONCLUSION: We established a risk-scoring system based on baseline clinical characteristics to predict composite renal outcomes in patients with AAV. Our results suggest that more attention should be paid to older patients with severe renal impairment and active inflammation.

Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks.

Artificial neural networks (ANN), established tools in machine learning, are applied to the problem of estimating parameters of a transversely isotropic (TI) material model using data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). We use neural networks to estimate parameters from experimental measurements of ultrasound-induced shear waves after training on analogous data from simulations of a computer model with similar loading, geometry, and boundary conditions. Strain ratios and shear-wave speeds (from MRE) and fiber direction (the direction of maximum diffusivity from diffusion tensor imaging (DTI)) are used as inputs to neural networks trained to estimate the parameters of a TI material (baseline shear modulus µ, shear anisotropy φ, and tensile anisotropy ζ). Ensembles of neural networks are applied to obtain distributions of parameter estimates. The robustness of this approach is assessed by quantifying the sensitivity of property estimates to assumptions in modeling (such as assumed loss factor) and choices in fitting (such as the size of the neural network). This study demonstrates the successful application of simulation-trained neural networks to estimate anisotropic material parameters from complementary MRE and DTI imaging data.

Shear wave speeds in nearly-incompressible fibrous materials with two fiber families.

An analytical and numerical investigation of shear wave behavior in nearly-incompressible soft materials with two fiber families was performed, focusing on the effects of material parameters and imposed pre-deformations on wave speed. This theoretical study is motivated by the emerging ability to image shear waves in soft biological tissues by magnetic resonance elastography (MRE). In MRE, the relationships between wave behavior and mechanical properties can be used to characterize tissue properties non-invasively. We demonstrate these principles in two material models, each with two fiber families. One model is a nearly-incompressible linear elastic model that exhibits both shear and tensile anisotropy; the other is a two-fiber-family version of the widely-used Holzapfel-Gasser-Ogden (HGO) model, which is nonlinear. Shear waves can be used to probe nonlinear material behavior using infinitesimal dynamic deformations superimposed on larger, quasi-static "pre-deformations." In this study, closed-form expressions for shear wave speeds in the HGO model are obtained in terms of the model parameters and imposed pre-deformations. Analytical expressions for wave speeds are confirmed by finite element simulations of shear waves with various polarizations and propagation directions. These studies support the feasibility of estimating the parameters of an HGO material model noninvasively from measured shear wave speeds.

Shear Wave Propagation and Estimation of Material Parameters in a Nonlinear, Fibrous Material.

This paper describes the propagation of shear waves in a Holzapfel-Gasser-Ogden (HGO) material and investigates the potential of magnetic resonance elastography (MRE) for estimating parameters of the HGO material model from experimental data. In most MRE studies the behavior of the material is assumed to be governed by linear, isotropic elasticity or viscoelasticity. In contrast, biological tissue is often nonlinear and anisotropic with a fibrous structure. In such materials, application of a quasi-static deformation (predeformation) plays an important role in shear wave propagation. Closed form expressions for shear wave speeds in an HGO material with a single family of fibers were found in a reference (undeformed) configuration and after imposed predeformations. These analytical expressions show that shear wave speeds are affected by the parameters (µ0, k1, k2, κ) of the HGO model and by the direction and amplitude of the predeformations. Simulations of corresponding finite element (FE) models confirm the predicted influence of HGO model parameters on speeds of shear waves with specific polarization and propagation directions. Importantly, the dependence of wave speeds on the parameters of the HGO model and imposed deformations could ultimately allow the noninvasive estimation of material parameters in vivo from experimental shear wave image data.