Standardized, risk-adapted induction therapy in kidney transplantation.

BACKGROUND: The choice of induction therapy in kidney transplantation is often non-standardized and centre-specific. Clinicians can choose between depleting and non-depleting antibodies, which differ in their immunosuppressive capacity and the concomitant risk of infection. We herein present a standardized risk-stratified algorithm for induction therapy that might help to balance the risk of rejection and/or serious infection. METHODS: Prior to kidney transplantation, patients were stratified into low-risk, intermediate-risk or high-risk according to our protocol based on immunologic risk factors. Depending on their individual immunologic risk, patients received basiliximab (low risk), antithymocyte globulin (intermediate risk) or low-dose alemtuzumab (high risk) for induction therapy. We analysed the results after 3 years of implementation of our risk-stratified induction therapy protocol at our kidney transplant centre. RESULTS: Between 01/2017 and 05/2020, 126 patients were stratified in accordance with our protocol (low risk/intermediate risk/high risk: 69 vs. 42 vs. 15 patients). The median follow-up time was 1.9 [1.0-2.5] years. No significant difference was observed in rejection rate and allograft survival (low risk/intermediate risk/high risk: 90.07% vs. 80.81% vs. 100% after 3 years (p > 0.05)) among the groups. The median eGFR at follow-up was (low risk/intermediate risk/high risk) 47 [33-58] vs 58 [46-76] vs 44 [22-55] ml/min/1.73 m2. Although the rate of viral and bacterial infections did not differ significantly, we observed a higher rate of opportunistic fungal infections with alemtuzumab induction. CONCLUSIONS: Our strategy offers facilitated and individualized choice of induction therapy in kidney transplantation. We propose further evaluation of our algorithm in prospective trials.

Living kidney donor evaluation is associated with early identification of life-changing diagnoses in potentially healthy donor candidates.

BACKGROUND: In order to ensure eligibility for living kidney donation, donor candidates undergo a thorough medical evaluation. This process might reveal hitherto undetected medical conditions, leading to refusal of the kidney donor candidate. Detection of such conditions may, however, also have a lifesaving effect. We report on 13 years of data from our living donor transplantation program on kidney donor candidates who were diagnosed with major medical conditions during evaluation. MATERIALS AND METHODS: We performed a retrospective analysis of living kidney donor candidates who attended our transplant center between January, 2007 and December, 2019. The main focus was on newly diagnosed medical conditions that required immediate medical attention and their prognostic significance. RESULTS: Of the 436 donor candidates who were evaluated for living kidney donation at our transplant center, 192 (44%) were accepted, while 244 (56%) were excluded from donation. Interestingly, 81 (33.1%) of the ineligible donor candidates were newly diagnosed as having a medical condition that required immediate attention. While 45 (18.5%) candidates were newly diagnosed with diabetes or prediabetes, 12 (4.9%) candidates had hitherto undetected malignancies, 10 candidates (4.1%) cardiac disease, five (2.0%) hypertension with end-organ damage, and four (1.6%) suffered from kidney disease. The remaining four candidates (1.6%) were diagnosed with gastrointestinal diseases, and one candidate (.4%) had an endocrine disorder. CONCLUSION: A comprehensive evaluation process for living kidney donation facilitates the identification of life-changing diagnoses in a significant proportion of candidates and secures immediate medical attention.

Late Onset Cerebral Nocardiosis in a Sensitized Renal Transplant Recipient Following Alemtuzumab Induction: A Case Report.

BACKGROUND: Balancing immunosuppressive regimen to prevent rejection yet avoiding severe infectious complications remains a key challenge following renal transplantation, especially in patients sensitized after exposure to human leukocyte antigens. We herein report a late onset infection with nocardia in a sensitized renal transplant recipient. CASE PRESENTATION: A 65-year-old male patient, who had received kidney transplantation with alemtuzumab induction due to human leukocyte antigen-sensitization 3 years ago, was admitted with headache and dizziness. A cerebral magnetic resonance imaging scan showed a right parieto-occipital brain abscess. Surgical abscess drainage was performed and microbiology analysis detected Nocardia paucivorans in the abscess fluid. Laboratory results showed persistently reduced lymphocyte and T-cell counts 3 years after transplantation. We started intravenous antibiotic therapy with high dose trimethoprim/sulfamethoxazole and imipenem/cilastatin. Furthermore, immunosuppression was adapted with discontinuation of mycophenolate. After 7 weeks of intravenous antibiotic therapy, the patient was switched to an oral antibiotic regimen with amoxicillin/clavulanic acid and minocycline. In the follow-up magnetic resonance imaging scan, cerebral lesions were substantially reduced, initial symptoms completely disappeared, and allograft function remained stable. CONCLUSIONS: Induction therapy with the CD52-antibody alemtuzumab enables transplantation in highly sensitized patients but leads to lymphocyte depletion for several weeks. Our patient presented with prolonged lymphopenia and a significantly reduced T-cell count 3 years after transplantation. To our knowledge, our case is the first to describe a late-onset nocardia infection 3 years after alemtuzumab induction in a renal transplant recipient. It underlines the importance of considering this rare disease in transplant patients, especially after induction therapy with depleting antibodies.

Contrast-enhanced MRI for simultaneous evaluation of renal morphology and split renal function in living kidney donor candidates.

PURPOSE: The evaluation process of potential living kidney donors focusses on renal anatomy and split renal function. This study aimed to evaluate a magnetic resonance imaging (MRI)-based approach for simultaneous evaluation of both and its impact on clinical decision making. METHOD: Over a 3-year period, 65 potential living kidney donors were consecutively enrolled. The MRI protocol was extended by MR-nephrography to measure split renal function. Standard DTPA-scintigraphy was used for functional comparison. RESULTS: Split renal function showed no systematic bias between the two methods (mean difference 0.3%, p = 0.08). Both methods would have yielded the same clinical decision for donor nephrectomy in 75% of the patients. In 25 % of the patients, one method indicated a relevant side difference while the other did not, and a different clinical decision could have been made based on split renal function alone. CONCLUSIONS: MRI proved eligible for comprehensive living kidney donor evaluation and non-inferior to scintigraphy for determining split renal function. In clinical decision making, these two methods would have resulted in the same side for donor nephrectomy in a large proportion of potential donors. Whether MRN will be implemented in clinical practice depends on transplant centre infrastructure and policy.

Successful long-term management of recurrent focal segmental glomerulosclerosis after kidney transplantation with costimulation blockade.

Recurrence of primary focal segmental glomerulosclerosis (FSGS) occurs in up to 50% of patients after kidney transplantation and is associated with poor allograft outcome. Novel therapeutic concepts directly target podocyte function via B7-1 with inconsistent response. We present the case of a 19-year-old patient with recurrent primary FSGS early after living donor kidney transplantation. Plasmapheresis and rituximab did not induce remission. Repetitive abatacept administration was able to achieve partial remission. Maintenance immunosuppression was subsequently switched to a belatacept-based calcineurin inhibitor-free immunosuppression, resulting in sustained complete remission with excellent allograft function throughout a follow-up of >56 months.

Low-dose alemtuzumab induction in a tailored immunosuppression protocol for sensitized kidney transplant recipients.

BACKGROUND: Induction therapy is crucial in kidney transplantation and constitutes an important cornerstone for long-term allograft survival. Alemtuzumab is a depleting CD52-specific antibody with T- and B-cell activity, leading to prolonged lymphocyte depletion for up to 12 months, with profound immunosuppression and an associated risk of serious infections. Current concepts aim to optimize dosing strategies to reduce side effects. Here we present data from an ongoing centre protocol consisting of low-dose alemtuzumab induction and tailored immunosuppression in sensitized patients undergoing kidney transplantation. METHODS: 10-year results of the protocol were analysed. Low-dose alemtuzumab induction consisted of a single dose of 20 mg intraoperatively, followed by tacrolimus and corticosteroids for initial immunosuppression, with mycophenolate mofetil suspended until a total lymphocyte count (TLC) >5% or 200/µl was reached. RESULTS: Between 01/2007 and 04/2017, 46 patients were treated in accordance with the protocol in 48 kidney transplantations. Median PRAmax was 43 [22-76; IQR] %; all patients had negative CDC-crossmatch prior to transplantation. Low-dose alemtuzumab was well tolerated. Median time to TLC recovery was 77 [62-127; IQR] d. Within a median follow-up of 3.3 [1.5-5.6; IQR] years, 12 (25%) patients developed BPAR, 10 of which were antibody-mediated (3 acute, 7 chronic ABMR). Death-censored 5-year allograft survival was 79.2%, with an excellent allograft function at the end of follow-up. There was no increased rate of infections, in particular viral infections. CONCLUSIONS: Our protocol, comprising low-dose alemtuzumab induction, initial suspension of mycophenolate mofetil and triple maintenance immunosuppression, provides excellent patient and allograft outcome in sensitized renal allograft recipients.

Low-dose cidofovir and conversion to mTOR-based immunosuppression in polyomavirus-associated nephropathy.

BACKGROUND: Polyomavirus-associated nephropathy (PVAN) remains a relevant complication following kidney transplantation with allograft loss rates of up to 50%. Reduction in overall immunosuppression is a cornerstone of therapy, whereas no specific antiviral regimen has shown conclusive benefit to date. The present case series demonstrates the efficacy of a dual therapeutic approach with low-dose cidofovir and conversion to mTOR-based immunosuppression in PVAN. METHODS: Patients with biopsy-proven PVAN having received low-dose cidofovir (0.25 mg/kg) according to the Tübingen Cidofovir Protocol and been converted to mTOR-based immunosuppression were analyzed retrospectively. RESULTS: Twenty-three patients with a median follow-up of 2.24 [IQR 1.55-5.01] years were included in the analysis. Median time to PVAN diagnosis was 268 [IQR 153-869] days after transplantation. Polyomavirus clearance from plasma was achieved in 78% of patients after a median of 118 [IQR 76-293] days. Of the 23 patients, nine patients (39%) lost their allograft function during follow-up, but only three of these (13%) due to PVAN. Fourteen patients (61%) stabilized or improved allograft function. The cidofovir protocol allowed for specific antiviral therapy without adverse nephrotoxicity, even in patients with low allograft function. CONCLUSIONS: Low-dose cidofovir and conversion to mTOR-based immunosuppression allow for effective virus clearance and preservation of allograft function in a high proportion of patients with PVAN and progressive allograft dysfunction and may prolong allograft survival in these patients.

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

Visplause: Visual Data Quality Assessment of Many Time Series Using Plausibility Checks.

Trends like decentralized energy production lead to an exploding number of time series from sensors and other sources that need to be assessed regarding their data quality (DQ). While the identification of DQ problems for such routinely collected data is typically based on existing automated plausibility checks, an efficient inspection and validation of check results for hundreds or thousands of time series is challenging. The main contribution of this paper is the validated design of Visplause, a system to support an efficient inspection of DQ problems for many time series. The key idea of Visplause is to utilize meta-information concerning the semantics of both the time series and the plausibility checks for structuring and summarizing results of DQ checks in a flexible way. Linked views enable users to inspect anomalies in detail and to generate hypotheses about possible causes. The design of Visplause was guided by goals derived from a comprehensive task analysis with domain experts in the energy sector. We reflect on the design process by discussing design decisions at four stages and we identify lessons learned. We also report feedback from domain experts after using Visplause for a period of one month. This feedback suggests significant efficiency gains for DQ assessment, increased confidence in the DQ, and the applicability of Visplause to summarize indicators also outside the context of DQ.

[Patient from Gambia with sonographic white kidneys]. / Patient aus Gambia mit sonografisch weißen Nieren - Fall 7 / 2016.

HISTORY AND ADMISSION FINDINGS: A 39-year-old male refugee from Gambia is admitted to the emergency room with fever, body aches and productive cough. INVESTIGATIONS: Laboratory, ultrasound and CT investigations show a sepsis due to Staphylcoccus aureus, a renal failure with the appearance of "snow-white" kidneys on ultrasound and a previously unknown acute HIV-infection. DIAGNOSIS, TREATMENT AND COURSE: Broad antibiotic treatment and an antiretroviral therapy (adapted to the impaired renal function) as well as hemodialysis are commenced. Despite of successful sepsis treatment and viral load reduction, the kidney function does not recover. Histologically, a HIV-nephropathy is confirmed. CONCLUSIONS: The appearance of "snow-white" kidneys on ultrasound can be a characteristic sign of a HIV-associated nephropathy.

Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations.

An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used.

A partition-based framework for building and validating regression models.

Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models.