Combining computational fluid dynamics and magnetic resonance imaging data using lattice Boltzmann based topology optimisation

This thesis presents the combination of magnetic resonance imaging (MRI) measurements and computational fluid dynamics (CFD) to reduce statistical measurement noise and identify objects and finer structures in the MRI data. Using a lattice Boltzmann based topology optimisation approach, the method allows those solutions that best match the measured flow field but satisfy the macroscopic conservation laws of fluid flow, here mass and momentum conservation. This combination is formulated as a distributed control problem that minimises the distance between measured and simulated flow field, the latter being the solution of a parametrised Boltzmann equation with Bhatnagar-Gross-Krook collision operator, where the controls represent the porosity distributed in the domain. The problem is solved with an adjoint lattice Boltzmann method using the open source software OpenLB

Immunosuppressant drugs and quality-of-life outcomes in kidney transplant recipients: An international cohort study (EU-TRAIN)

Immunosuppressant; Quality of life; TransplantationImmunosupressor; Qualitat de vida; TrasplantamentInmunosupresor; Calidad de vida; TrasplanteIntroduction: Patient-Reported Outcomes (PRO) integrate a wide range of holistic dimensions that arenot captured within clinical outcomes. Particularly, from induction treatment to maintenance therapy, patient quality-of-life (QoL) of kidney transplant recipients have been sparsely investigated in international settings. Methods: In a prospective, multi-centric cohort study, including nine transplant centers in four countries, we explored the QoL during the year following transplantation using validated elicitation instruments (EQ-5D-3L index with VAS) in a population of kidney transplant patients receiving immunosuppressive therapies. Calcineurin inhibitors (tacrolimus and ciclosporin), IMPD inhibitor (mycophenolate mofetil), and mTOR inhibitors (everolimus and sirolimus) were the standard-of-care (SOC) medications, together with tapering glucocorticoid therapy. We used EQ-5D and VAS data as QoL measures alongside descriptive statistics at inclusion, per country and hospital center. We computed the proportions of patients with different immunosuppressive therapy patterns, and using bivariate and multivariate analyses, assessed the variations of EQ-5D and VAS between baseline (i.e., inclusion Month 0) and follow up visits (Month 12). Results: Among 542 kidney transplant patients included and followed from November 2018 to June 2021, 491 filled at least one QoL questionnaire at least at baseline (Month 0). The majority of patients in all countries received tacrolimus and mycophenolate mofetil, ranging from 90.0% in Switzerland and Spain to 95.8% in Germany. At M12, a significant proportion of patients switched immunosuppressive drugs, with proportion varying from 20% in Germany to 40% in Spain and Switzerland. At visit M12, patients who kept SOC therapy had higher EQ-5D (by 8 percentage points, p < 0.05) and VAS (by 4 percentage points, p < 0.1) scores than switchers. VAS scores were generally lower than EQ-5D (mean 0.68 [0.5–0.8] vs. 0.85 [0.8–1]). Discussion: Although overall a positive trend in QoL was observed, the formal analyses did not show any significant improvements in EQ-5D scores or VAS. Only when the effect of a therapy use was separated from the effect of switching, the VAS score was significantly worse for switchers during the follow up period, irrespective of the therapy type. If adjusted for patient characteristics and medical history (e.g., gender, BMI, eGRF, history of diabetes), VAS and EQ-5D delivered sound PRO measures for QoL assessments during the year following renal transplantation.The project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 754995. Open access funding by University of Lausanne

Influence of lipid profile and statin administration on arterial stiffness in renal transplant recipients

Background: Hyperlipidemia is one of the major risk factors for developing a cardiovascular disease (CVD) and it is a frequent post-transplant complication, occurring in up to 60% of the renal transplant recipients (RTRs). Lipid lowering therapy with HMG-CoA reductase inhibitors (statins) is generally recommended and may reduce the overall cardiovascular risk. The aim of this study was to evaluate the lipid profile, statin administration and their relationship with arterial stiffness parameters in renal transplant recipients. Methods: Three hundred and forty-four stable RTRs (62.5% male) transplanted between 1994 and 2018 were randomly enrolled to the study. The following parameters of arterial stiffness was measured in each patient: carotid femoral pulse wave velocity (baPWV left and right, cfPWV) and pulse pressure (PP right and left). The study group was divided based on the use statins: 143 (41.6%) and 201 (58.4%). RTRs were qualified to the statin (+) and the statin (–) group, respectively. Results: In the statin (+) as compared to statin (–) group there were more patients with a CVD (32.9% vs. 14.9%) and diabetes (25.2% vs. 14.4%). In the whole study group, CVD was associated with a significant increase of both baPWV and cfPWV as well as PP (8.5 mmHg). There were significant differences in arterial stiffness parameters (baPWV, cfPWV, PP) between the statin (+) and the statin (–) group. Conclusions: Arterial stiffness was increased in RTRs with CVD and hyperlipidemia. The control of hyperlipidemia was poor in RTRs

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Predictors of graft survival at diagnosis of antibody‐mediated renal allograft rejection: a retrospective single‐center cohort study

Antibody-mediated rejection (ABMR) is a major cause of graft loss in renal transplantation. We assessed the predictive value of clinical, pathological, and immunological parameters at diagnosis for graft survival. We investigated 54 consecutive patients with biopsy-proven ABMR. Patients were treated according to our current standard regimen followed by triple maintenance immunosuppression. Patient characteristics, renal function, and HLA antibody status at diagnosis, baseline biopsy results, and immunosuppressive treatment were recorded. The risk of graft loss at 24 months after diagnosis and the eGFR slope were assessed. Multivariate analysis showed that eGFR at diagnosis and chronic glomerulopathy independently predict graft loss (HR 0.94; P = 0.018 and HR 1.57; P = 0.045) and eGFR slope (beta 0.46; P < 0.001). Cyclophosphamide treatment (6x 15 mg/m²) plus high-dose intravenous immunoglobulins (IVIG) (1.5 g/kg) was superior compared with single-dose rituximab (1x 500 mg) plus low-dose IVIG (30 g) (HR 0.10; P = 0.008 and beta 10.70; P = 0.017) and one cycle of bortezomib (4x 1.3 mg/m(2)) plus low-dose IVIG (HR 0.16; P = 0.049 and beta 11.21; P = 0.010) regarding the risk of graft loss and the eGFR slope. In conclusion, renal function at diagnosis and histopathological signs of chronic ABMR seem to predict graft survival independent of the applied treatment regimen. Stepwise modifications of the treatment regimen may help to improve outcome