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Background: The response to severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) vaccination is severely impaired in patients on maintenance immunosuppression after kidney transplantation., Methods: We conducted a prospective cohort study of 194 kidney transplant recipients (KTR) who exhibited no response to SARS-CoV-2 vaccinations (i.e., SARS-CoV-2 spike protein antibodies ≤264 U/mL) and had no prior documented infection. Patients received 300 mg of cilgavimab/tixagevimab as SARS-CoV-2 pre-exposure prophylaxis (PrEP) between March 4, 2022, and May 3, 2022 and were contrasted to a matched cohort of 186 KTRs also without immunization again defined as SARS-CoV-2 spike protein antibodies ≤264 U/mL and no documented prior infection. The primary outcome was the serum kinetics of cilgavimab/tixagevimab, the secondary endpoints were time to SARS-CoV-2 breakthrough infection, severity of disease and variant specific live viral in vitro neutralization tests of patient sera., Findings: Longitudinal serum level monitoring showed a half-life of 91 days for both antibodies (95% CI 86-95 days for cilgavimab and 85-96 days for tixagevimab) in KTRs. In vitro neutralization tests showed effectiveness against the BA.2 omicron subvariant but not BA.5. The cumulative incidence of SARS-CoV-2 infections until May 15, 2022, (BA.2 dominance) was 15/194 vs 36/186 in the PrEP and control group respectively (OR = 0.35, 95% CI 0.18-0.66) but was not different thereafter (BA.4/5 dominance). The number of severe infections during the BA.2 period was lower in the prophylaxis than in the control group (OR = 0.37, 95% CI 0.17-0.79)., Interpretation: This study showed that SARS-CoV-2 PrEP with cilgavimab/tixagevimab demonstrated clinical effectiveness against variants that are neutralised (BA.2) but not against BA.4/5., Funding: This study was funded by the Medical University of Vienna and an unrestricted grant from AstraZeneca (ESR-21-21585)., Competing Interests: Declaration of interests “The authors declare no competing interests for this manuscript”, (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome., Methods: We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets., Results: Through our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity., Conclusion: The dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Hurtado, Khajavi, Essabbar, Kammer, Xie, Coullomb, Pradines, Casanova, Kruczynski, Gouin, Clermont, Boutillet, Senosain, Zou, Zhao, Burq, Mahfoudi, Besse, Launay, Passioukov, Chetaille, Favre, Maldonado, Cruzalegui, Delfour, Mazières and Pancaldi.)

Background: Measurement of tumor markers from peripheral venous blood is an emerging tool to assist in the early diagnosis of lung cancer. Samples from the pulmonary artery and pulmonary artery wedge position (trans-pulmonary samples) are accessible via right-heart catheterization and, by virtue of their proximity to lung tumors, may increase diagnostic yield., Case Presentation: We report a case of a 64 year-old woman from whom trans-pulmonary samples were obtained and who was diagnosed 16 months later with recurrent metastatic small cell lung cancer. Carcinoembryonic antigen, cytokeratin fragment 21 - 1 (CYFRA), and human epididymis protein 4 (HE4) levels demonstrated increasing concentrations across the pulmonary circulation. These gradients exceeded the assays' coefficient of variation by several-fold. For CYFRA and HE4, pulmonary artery wedge concentrations exceeded peripheral venous levels by more than 10% and peripheral arterial levels were up to 8% higher than peripheral venous levels., Conclusions: Evaluating the feasibility and utility of trans-pulmonary tumor markers for lung cancer diagnosis in a larger cohort should be considered. The addition of a peripheral arterial sample to standard peripheral venous samples may be a more practical alternative., (© 2024. The Author(s).)

Background: Induction of donor-specific tolerance is a promising approach to achieve long-term graft patency in transplantation with little to no maintenance immunosuppression. Changes to the recipient's T cell receptor (TCR) repertoire are understood to play a pivotal role in the establishment of a robust state of tolerance in chimerism-based transplantation protocols., Methods: We investigated changes to the TCR repertoires of patients participating in an ongoing prospective, controlled, phase I/IIa trial designed to evaluate the safety and efficacy of combination cell therapy in living donor kidney transplantation. Using high-throughput sequencing, we characterized the repertoires of six kidney recipients who also received bone marrow from the same donor (CKBMT), together with an infusion of polyclonal autologous Treg cells instead of myelosuppression., Findings: Patients undergoing combination cell therapy exhibited partial clonal deletion of donor-reactive CD4 + T cells at one, three, and six months post-transplant, compared to control patients receiving the same immunosuppression regimen but no cell therapy (p = 0.024). The clonality, R20 and turnover rates of the CD4 + and CD8 + TCR repertoires were comparable in both groups, showing our protocol caused no excessive repertoire shift or loss of diversity. Treg clonality was lower in the case group than in control (p = 0.033), suggesting combination cell therapy helps to preserve Treg diversity., Interpretation: Overall, our data indicate that combining Treg cell therapy with CKBMT dampens the alloimmune response to transplanted kidneys in humans in the absence of myelosuppression., Funding: This study was funded by the Vienna Science and Technology Fund (WWTF)., Competing Interests: Declaration of interests The authors have declared that no conflict of interest exists., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: The results of this study describe the relationship between the body condition of dairy cows and selected metabolic parameters during the peri- and post-partum period with special consideration of 3 local dairy cow breed in Upper Bavaria and the Allgau., Material and Methods: Three local dairy cattle breeds (Swiss Brown (BV), Simmental (FL), Holstein Friesian (HF)) were examined on 68 farms in southern Germany for 7 consecutive weeks. In dry cows as well as lactating cows (5.-65. day in milk), following blood parameters were investigated: beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), creatinine, aspartate aminotransferase, gamma-glutamyltransferase, glutamate dehydrogenase, total protein, albumin, creatine kinase. In addition, body condition (body condition score [BCS] and back fat thickness [BFT]) were recorded. Exploratory and descriptive statistics were used for data analysis., Results: Concerning the difference in condition before and after calving, the FL showed the smallest difference in RFD. For FL and BV a trend towards higher BFT values could be seen in first lactating cows. For FL and HF, the NEFA values of the later lactating cows were below those of the first lactating cows. The higher lactating cows of BV and FL had higher BHB values. The correlation between BFT and BCS showed the highest R2 (0.53) in the HF cows. BV and FL were below at 0.42 and 0.37. BCS and BFT could not be predicted by the variables NEFA, BHB and liver enzymes. BHB levels of all 3 breeds increased at weeks 2-4 post-partum. The NEFA values for all 3 breeds increased primarily in the 1st-3rd week p.p. in parallel to when the BFT p.p. decreased. NEFA values were highest when body condition declined and therefore when fat mobilization peaked. In BV and HF, there was a constant increase in GLDH when the p.p. BCS difference was there., Conclusion and Clinical Relevance: Body condition assessment (BCS at herd and animals` level, BFT at animal level) is an important tool for animal health monitoring. Due to the recognizable breed specificity, the dairy herds can be dealt with more explicitly. The aim is to optimally influence the energy balance of the cow during early lactation in order maintain the health of the animal and its organ systems., Competing Interests: Die Autor*innen bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Background: We recently found that epiplakin 1 (EPPK1) alterations were present in 12% of lung adenocarcinoma (LUAD) cases and were associated with a poor prognosis in early-stage LUAD when combined with other molecular alterations. This study aimed to identify a probable crucial role for EPPK1 in cancer development., Methods: EPPK1 mRNA and protein expression was analyzed with clinical variables. Normal bronchial epithelial cell lines were exposed to cigarette smoke for 16 weeks to determine whether EPPK1 protein expression was altered after exposure. Further, we used CRISPR-Cas9 to knock out (KO) EPPK1 in LUAD cell lines and observed how the cancer cells were altered functionally and genetically., Results: EPPK1 protein expression was associated with smoking and poor prognosis in early-stage LUAD. Moreover, a consequential mesenchymal-to-epithelial transition was observed, subsequently resulting in diminished cell proliferation and invasion after EPPK1 KO. RNA sequencing revealed that EPPK1 KO induced downregulation of 11 oncogenes, 75 anti-apoptosis, and 22 angiogenesis genes while upregulating 8 tumor suppressors and 12 anti-cell growth genes. We also observed the downregulation of MYC and upregulation of p53 expression at both protein and RNA levels following EPPK1 KO. Gene ontology enrichment analysis of molecular functions highlighted the correlation of EPPK1 with the regulation of mesenchymal cell proliferation, mesenchymal differentiation, angiogenesis, and cell growth after EPPK1 KO., Conclusions: Our data suggest that EPPK1 is linked to smoking, epithelial to mesenchymal transition, and the regulation of cancer progression, indicating its potential as a therapeutic target for LUAD., (© 2024. The Author(s).)

Introduction: Diabetic kidney disease (DKD) is a common cause of chronic kidney disease with around 25-40% of patients with diabetes being affected. The course of DKD is variable, and estimated glomerular filtration rate (eGFR) and albuminuria, the currently used clinical markers, are not able to accurately predict the individual disease trajectory, in particular in early stages of the disease. The aim of this study was to assess the association of urine levels of selected protein biomarkers with the progression of DKD at an early stage of disease., Methods: We measured 22 protein biomarkers using the Mesoscale Discovery platform in 461 urine samples of the PROVALID cohort, an observational study of patients with type 2 diabetes mellitus followed at the primary health care level for a minimum of 4 years. Odds ratios (ORs) were estimated for the effect of marker values above median on fast progression using unadjusted and adjusted logistic regression models. RNA expression at the single-cell level in kidney biopsy samples obtained from a cohort of young persons with type 2 diabetes mellitus was in addition determined for markers showing significant associations with disease progression., Results: Increased urinary levels of epidermal growth factor (EGF) were linked to lower odds of fast progression (defined as annual eGFR decline greater than 2.58 mL/min per 1.73 m2) with an OR of 0.60 (95% CI: 0.46, 0.78). The association with outcome was even stronger when adjusting for a set of 14 baseline clinical parameters including age, biological sex, eGFR, body mass index, albuminuria, and HbA1c. Elevated urinary levels of fatty acid-binding protein 3 (FABP3) and vascular cell adhesion molecule 1 (VCAM1) were each significantly associated with fast progression with an OR of 1.44 (95% CI: 1.11, 1.87) and an OR of 1.41 (95% CI: 1.08, 1.83), respectively. Enriched expression of EGF and FABP3 was observed in distal convoluted tubular cells and VCAM1 in parietal epithelial cells at single-cell level from biopsies of patients with early DKD., Conclusion: In summary, we show that lower urinary levels of EGF and higher urinary levels of FABP3 and VCAM1 are significantly associated with DKD progression in early-stage disease., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Background: Kidney transplantation is the preferred treatment for eligible patients with kidney failure who need renal replacement therapy. However, it remains unclear whether the anticipated survival benefit from kidney transplantation is different for women and men., Methods: We included all dialysis patients recorded in the Austrian Dialysis and Transplant Registry who were waitlisted for their first kidney transplant between 2000 and 2018. In order to estimate the causal effect of kidney transplantation on 10-year restricted mean survival time, we mimicked a series of controlled clinical trials and applied inverse probability of treatment and censoring weighted sequential Cox models., Results: This study included 4408 patients (33% female) with a mean age of 52 years. Glomerulonephritis was the most common primary renal disease both in women (27%) and men (28%). Kidney transplantation led to a gain of 2.22 years (95% CI 1.88 to 2.49) compared with dialysis over a 10-year follow-up. The effect was smaller in women (1.95 years, 95% CI 1.38 to 2.41) than in men (2.35 years, 95% CI 1.92 to 2.70) due to a better survival on dialysis. Across ages the survival benefit of transplantation over a follow-up of 10 years was smaller in younger women and men and increased with age, showing a peak for both women and men aged about 60 years., Conclusions: There were few differences in survival benefit by transplantation between females and males. Females had better survival than males on the waitlist receiving dialysis and similar survival to males after transplantation., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Objective: The aim of the present study was to investigate relationships between elevated haptoglobin concentrations in milk and clinical as well as laboratory parameters in early lactating dairy cows. Furthermore, cut-off values should be identified for the differentiation of healthy and affected animals., Material and Methods: 1462 dairy cows between 5.-65. days in milk were examined on 68 Bavarian farms. Milk and blood samples were taken once a week for a 7-week period per farm and body-condition-scoring, backfat thickness measurement and Metricheck examination, to evaluate uterine health, were performed. Milk samples were analysed for milk fat, milk protein, lactose, urea, ß-hydroxybutyrate and non-esterified fatty acids (indirect measurement, based on IR spectra), cell count, and milk haptoglobin. Blood samples were analysed for creatinine, aspartate aminotransferase, gamma-glutamyl transferase, glutamate dehydrogenase, total protein, albumin, creatine kinase, ß-hydroxybutyrate, non-esterified fatty acids, and blood haptoglobin.Cluster analyses were performed to determine cut-off values for haptoglobin., Results: Besides milk haptoglobin (µg/ml) and blood haptoglobin (µg/ml), cell count (cells/ml milk), milk fat (%), milk protein (%), non-esterified fatty acids in blood (mmol/l), lactation number, days in milk, breed, season, and milk yield (kg) were included as significant input variables (p<0.005) in the cluster analyses. Cluster analysis, using k-means resp. k-prototypes algorithms, resulted in 5 (clusters 1-5 M 1 ) resp. 4 different clusters (clusters 0-3 M 2 and 0-3 B).A cut-off value of 0.5 µg/ml haptoglobin in milk was determined for the differentiation of healthy and affected animals., Conclusion and Clinical Relevance: As milk is an easily available substrate, routine determination of haptoglobin in milk might be a suitable parameter for animal health monitoring. Using the detected cut-off value, apparently healthy animals with subclinical inflammatory diseases can be identified more quickly., Competing Interests: Die Autoren bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015-2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.

Placental membranes have been widely studied and used clinically for wound care applications, but there is limited published information on the benefits of using the chorion membrane. The chorion membrane represents a promising source of placental-derived tissue to support wound healing, with its native composition of extracellular matrix (ECM) proteins and key regulatory proteins. This study examined the impact of hypothermic storage on the structure of chorion membrane, ECM content, and response to degradation in vitro. Hypothermically stored chorion membrane (HSCM) was further characterized for its proteomic content, and for its functionality as a scaffold for cell attachment and proliferation in vitro. HSCM retained the native ECM structure, composition, and integrity of native unprocessed chorion membrane and showed no differences in response to degradation in an in vitro wound model. HSCM retained key regulatory proteins previously shown to be present in placental membranes and promoted the attachment and proliferation of fibroblasts in vitro. These data support the fact that hypothermic storage does not significantly impact the structure and characteristics of the chorion membrane compared to unprocessed tissue or its functionality as a scaffold to support tissue growth.

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures.

Importance: Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking., Objective: To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts., Design, Setting, and Participants: This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023., Main Outcomes and Measures: Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated., Results: Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5., Conclusions and Relevance: In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.

Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory., Methods: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation., Results: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline., Conclusions: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway., (© 2023. The Author(s).)

Background: Photodynamic therapy (PDT) is an effective method for treating actinic keratosis (AK) with pain during illumination representing the major side effect. The efficacy of two different cooling methods for pain relief in PDT of AK in the head region was compared., Methods: Randomized, assessor-blinded, half side comparison study in 20 patients with symmetrically distributed AK on the head. Conventional PDT was performed on both halves of the scalp or face by applying 20% aminolevulinic acid cream (ALA) and subsequent illumination with incoherent red light. During illumination one side was cooled with a cold air blower (CAB) and the other with a standard fan (FAN) in a randomized fashion. Pain and skin temperature were recorded during and after PDT. The phototoxic skin reaction was evaluated up to seven days after PDT. The clearance rate of AK was assessed at 3 and 6 months after PDT., Results: Mean pain (VASmean), maximum pain intensity (VASmax) and the mean skin temperature during PDT were significantly lower with CAB as compared to FAN (VASmean: 2.7 ± 1.4 vs. 3.7 ± 2.1, p = 0.003; VASmax: 3.8 ± 2.0 vs. 4.8 ± 2.5, p = 0.002; 26.8 ± 2.0 °C vs. 32.1 ± 1.7 °C; p=<0.001). The severity of the phototoxic skin reaction and the clearance rate of AK did not differ between the two cooling methods., Conclusion: Cooling with CAB during PDT has a greater analgesic effect than cooling with FAN. Patients with a lower skin temperature during illumination tended to experience less pain, however, this effect did not reach the level of statistical significance., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier B.V.)

Objective: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs., Method: We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model)., Results: Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g., , Auc: 0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model., Conclusions: Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Importance: Kidney transplant is considered beneficial in terms of survival compared with continued dialysis for patients with kidney failure. However, randomized clinical trials are infeasible, and available evidence from cohort studies is at high risk of bias., Objective: To compare restricted mean survival times (RMSTs) between patients who underwent transplant and patients continuing dialysis across transplant candidate ages and depending on waiting time, applying target trial emulation methods., Design, Setting, and Participants: In this retrospective cohort study, patients aged 18 years or older appearing on the wait list for their first single-organ deceased donor kidney transplant between January 1, 2000, and December 31, 2018, in Austria were evaluated. Available data were obtained from the Austrian Dialysis and Transplant Registry and Eurotransplant and included repeated updates on wait-listing status and relevant covariates. Data were analyzed between August 1, 2019, and December 23, 2021., Exposures: A target trial was emulated in which patients were randomized to either receive the transplant immediately (treatment group) or to continue dialysis and never receive a transplant (control group) at each time an organ became available., Main Outcomes and Measures: The primary outcome was time from transplant allocation to death. Effect sizes in terms of RMSTs were obtained using a sequential Cox approach., Results: Among the 4445 included patients (2974 men [66.9%]; mean [SD] age, 52.2 [13.2] years), transplant was associated with increased survival time across all considered ages compared with continuing dialysis and remaining on the wait list within a 10-year follow-up. The estimated RMST differences were 0.57 years (95% CI, -0.14 to 1.84 years) at age 20 years, 3.01 years (95% CI, 2.50 to 3.54 years) at age 60 years, and 2.48 years (95% CI, 1.88 to 3.04 years) at age 70 years. The survival benefit for patients who underwent transplant across ages was independent of waiting time., Conclusions and Relevance: The findings of this study suggest that kidney transplant prolongs the survival time of persons with kidney failure across all candidate ages and waiting times.

Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer., Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index., Results: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy., Conclusions: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management., (Copyright © 2022. Published by Elsevier B.V.)

Background: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues., Methods: We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability., Results: Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results., Conclusions: Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications., (© 2022. The Author(s).)

Secondary hyperparathyroidism in chronic kidney disease poses a major risk factor for vascular calcification and high bone turnover, leading to mineralization defects. The aim was to analyze the effect of active vitamin D and calcimimetic treatment on fibroblast growth factor 23 (FGF23), serum calcification propensity (T50), a surrogate marker of calcification stress and bone specific alkaline phosphatase (BAP) in hemodialysis. This is a subanalysis of a randomized trial comparing etelcalcetide vs. alfacalcidol in 62 hemodialysis patients for 1 year. We compared the change of BAP and serum calcification propensity between the two medications and assessed the influence of FGF23 change over time. We found no significant differences in the change of BAP or serum calcification propensity (T50) levels from baseline to study end between treatment arms (difference in change of marker between treatment with etelcalcetide vs. alfacalcidol: BAP : 2.0 ng/ml [95% CI-1.5,5.4], p = 0.3; T50: -15 min [95% CI -49,19], p = 0.4). Using FGF23 change over time, we could show that BAP levels at study end were associated with FGF23 change (-0.14 [95% CI -0.21, -0.08], p < 0.001). We did not observe the same association between FGF23 change and T50 (effect of FGF23 change on T50: 3.7 [95% CI -5.1, 12], p = 0.4; R 2 = 0.07 vs. R 2 = 0.06). No significant difference was found in serum calcification propensity (T50) values between treatment arms. FGF23 was not associated with serum calcification propensity (T50), but was negatively correlated with BAP underlying its role in the bone metabolism., Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT03182699]., Competing Interests: RO reports grants from Amgen during the conduct of the study. In addition, RO, KD, and RR-S have a patent “Methods of treating left ventricle hypertrophy” pending. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dörr, Hödlmoser, Kammer, Reindl-Schwaighofer, Lorenz, Reiskopf, Jagoditsch, Marculescu and Oberbauer.)

PytuTester is an open-source ventilator tester developed to help bio-engineers in the design and verification of new ventilator prototypes. A ventilator tester allows measuring the flow, pressure, volume, and oxygen concentration provided to the patient. During the global pandemic COVID-19, several open-source ventilators prototypes were developed; however, due to high cost and demand testers, they were not available. In this context, a low-cost tester was developed using a Raspberry Pi and medical-grade sensors for the test ventilators prototypes. This paper presents the design files, software interface, and validations tests. Our results indicate that the tester has good accuracy to evaluate the efficacy and performance of new prototypes. When tested on two ventilator designs developed in Paraguay, PytuTester reported flow profiles that were concordant with the industry-standard VT650 Gas Flow Analyzer. PytuTester was then field deployed to test several DIY ventilator designs in low-resource areas., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

Fibroblast growth factor 23 (FGF23) is elevated in patients with chronic kidney disease and contributes to left ventricular hypertrophy (LVH). The aim of the analysis was to determine whether this effect is mediated by the renin-angiotensin-aldosterone system (RAAS) in hemodialysis. Serum samples from 62 randomized hemodialysis patients with LVH were analyzed for plasma renin activity (PRA-S), angiotensin II (AngII), and metabolites, angiotensin-converting enzyme-2 (ACE2) and aldosterone using a high throughput mass spectrometry assay. Compared to healthy individuals, levels of the RAAS parameters PRA-S, AngII and aldosterone were generally lower [median (IQR) PRA-S 130 (46-269) vs. 196 (98, 238) pmol/L; AngII 70 (28-157) vs. 137 (76, 201) pmol/L; Aldosterone 130 (54, 278) vs. 196 (98, 238) pmol/L]. We did not find an indication that the effect of FGF23 on LVH was mediated by RAAS parameters, with all estimated indirect effects virtually zero. Furthermore, FGF23 was not associated with RAAS parameter levels throughout the study. While there was a clear association between FGF23 levels and left ventricular mass index (LVMI) at the end of the study and in the FGF23 fold change and LVMI change analysis, no association between RAAS and LVMI was observed. Serum concentrations of PRA-S, AngII, and aldosterone were below the ranges measured in healthy controls suggesting that RAAS is not systemically activated in hemodialysis patients. The effect of FGF23 on LVMI was not mediated by systemic RAAS activity. These findings challenge the current paradigm of LVH progression and treatment with RAAS blockers in dialysis., Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT03182699], identifier [NCT03182699]., Competing Interests: RO reported grants from Amgen during the conduct of the study. In addition, RO, KD, and RR-S had a patent “Methods of treating left ventricle hypertrophy” pending. MP was employed by Attoquant Diagnostics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dörr, Kammer, Reindl-Schwaighofer, Lorenz, Marculescu, Poglitsch, Beitzke and Oberbauer.)

Background and Objectives: The median kidney transplant half-life is 10-15 years. Because of the scarcity of donor organs and immunologic sensitization of candidates for retransplantation, there is a need for quantitative information on if and when a second transplantation is no longer associated with a lower risk of mortality compared with waitlisted patients treated by dialysis. Therefore, we investigated the association of time on waiting list with patient survival in patients who received a second transplantation versus remaining on the waiting list., Design, Setting, Participants, & Measurements: In this retrospective study using target trial emulation, we analyzed data of 2346 patients from the Austrian Dialysis and Transplant Registry and Eurotransplant with a failed first graft, aged over 18 years, and waitlisted for a second kidney transplantation in Austria during the years 1980-2019. The differences in restricted mean survival time and hazard ratios for all-cause mortality comparing the treatment strategies "retransplant" versus "remain waitlisted with maintenance dialysis" are reported for different waiting times after first graft loss., Results: Second kidney transplantation showed a longer restricted mean survival time at 10 years of follow-up compared with remaining on the waiting list (5.8 life months gained; 95% confidence interval, 0.9 to 11.1). This survival difference was diminished in patients with longer waiting time after loss of the first allograft; restricted mean survival time differences at 10 years were 8.0 (95% confidence interval, 1.9 to 14.0) and 0.1 life months gained (95% confidence interval, -14.3 to 15.2) for patients with waiting time for retransplantation of <1 and 8 years, respectively., Conclusions: Second kidney transplant is associated with patient survival compared with remaining waitlisted and treatment by dialysis, but the survival difference diminishes with longer waiting time., (Copyright © 2022 by the American Society of Nephrology.)

Background: Chronic kidney disease (CKD) is a well-established complication in people with diabetes mellitus. Roughly one quarter of prevalent patients with diabetes exhibit a CKD stage of 3 or higher and the individual course of progression is highly variable. Therefore, there is a clear need to identify patients at high risk for fast progression and the implementation of preventative strategies. Existing prediction models of renal function decline, however, aim to assess the risk by artificially grouped patients prior to model building into risk strata defined by the categorization of the least-squares slope through the longitudinally fluctuating eGFR values, resulting in a loss of predictive precision and accuracy., Methods: This study protocol describes the development and validation of a prediction model for the longitudinal progression of renal function decline in Caucasian patients with type 2 diabetes mellitus (DM2). For development and internal-external validation, two prospective multicenter observational studies will be used (PROVALID and GCKD). The estimated glomerular filtration rate (eGFR) obtained at baseline and at all planned follow-up visits will be the longitudinal outcome. Demographics, clinical information and laboratory measurements available at a baseline visit will be used as predictors in addition to random country-specific intercepts to account for the clustered data. A multivariable mixed-effects model including the main effects of the clinical variables and their interactions with time will be fitted. In application, this model can be used to obtain personalized predictions of an eGFR trajectory conditional on baseline eGFR values. The final model will then undergo external validation using a third prospective cohort (DIACORE). The final prediction model will be made publicly available through the implementation of an R shiny web application., Discussion: Our proposed state-of-the-art methodology will be developed using multiple multicentre study cohorts of people with DM2 in various CKD stages at baseline, who have received modern therapeutic treatment strategies of diabetic kidney disease in contrast to previous models. Hence, we anticipate that the multivariable prediction model will aid as an additional informative tool to determine the patient-specific progression of renal function and provide a useful guide to early on identify individuals with DM2 at high risk for rapid progression., (© 2021. The Author(s).)

Dairy cows suffer poor metabolic adaptation syndrome (PMAS) 1 during early post-calving periods caused by negative energy balance. Measurement of blood beta-hydroxy butyric acid (BHBA) 2 and blood non-esterified fatty acids (NEFA) 3 allow early and accurate detection of negative energy balance. Machine learning prediction of blood BHBA and blood NEFA using milk testing samples represents an opportunity to identify at-risk animals, using less labor than direct blood testing methods. Routine milk testing on modern dairies and computer record keeping provide an immense amount of data which can then be used in machine learning models. Previous research for predicting blood metabolites using Fourier-transform infrared spectroscopy (FTIR) 4 milk data has focused mainly on individual models rather than a comparison among the models. Full model selection is the process of comparing different combinations of pre-processing methods, variable selection, and statistical learning algorithms to determine which model results in the lowest prediction error for a given dataset. For this project we used a full model selection approach with regression trees (rtFMS) 5 . rtFMS uses the cross-validated performance of different model configurations to feed a regression tree for selecting a final model. A total of 384 possible model configurations (algorithms, predictors and data preprocessing options) for each outcome (blood BHBA and blood NEFA) were considered in the rtFMS technique. rtFMS allows direct comparison of multiple modeling approaches reducing bias due to empirical knowledge, modeling habits, or preferences, identifying the model with minimal root mean squared prediction error (RMSE) 6 . An elastic net regression model was selected as the best performing model for both biomarkers. The input data for blood BHBA predictions were FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers, obtaining RMSE = 0.354 (0.328-0.392). The best performing model for blood NEFA had input data of FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers filter along with the time of milking, obtaining RMSE = 0.601 (0.564-0.654). The comparison of multiple modeling strategies, conducted by rtFMS, present an option for improved FTIR prediction models of blood BHBA and blood NEFA by reducing error due to human bias. The implementation of rtFMS to design future prediction models can guide model inputs and features. Our prediction models have the potential to increase early detection of metabolic disorders in dairy cows during the transition period., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

[Figure: see text].

Background: Granulomas caused by infectious lung diseases can present as indeterminate pulmonary nodules (IPN). This study aims to validate an enzyme immunoassay (EIA) for Histoplasma immunoglobulin G (IgG) and immunoglobulin M (IgM) for diagnosing benign IPN in areas with endemic histoplasmosis., Methods: Prospectively collected serum samples from patients at Vanderbilt University Medical Center (VUMC [n = 204]), University of Pittsburgh Medical Center (n = 71), and University of Cincinnati (n = 51) with IPN measuring 6 to 30 mm were analyzed for Histoplasma IgG and IgM with EIA. Diagnostic test characteristics were compared with results from the VUMC pilot cohort (n = 127). A multivariable logistic regression model was developed to predict granuloma in IPN., Results: Cancer prevalence varied by cohort: VUMC pilot 60%, VUMC validation 65%, University of Pittsburgh Medical Center 35%, and University of Cincinnati 75%. Across all cohorts, 19% of patients had positive IgG titers, 5% had positive IgM, and 3% had positive both IgG and IgM. Of patients with benign disease, 33% were positive for at least one antibody. All patients positive for both IgG and IgM antibodies at acute infection levels had benign disease (n = 13), with a positive predictive value of 100%. The prediction model for granuloma in IPN demonstrated an area under the receiver-operating characteristics curve of 0.84 and Brier score of 0.10., Conclusions: This study confirmed that Histoplasma EIA testing can be useful for diagnosing benign IPN in areas with endemic histoplasmosis in a population at high risk for lung cancer. Integrating Histoplasma EIA testing into the current diagnostic algorithm where histoplasmosis is endemic could improve management of IPN and potentially decrease unnecessary invasive biopsies., (Copyright © 2021 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Objective: To investigate the association between haptoglobin concentration in the blood and the occurrence of ketosis, selected clinical parameters as well as lameness in dairy cows., Material and Methods: The data was collected on 39 dairy farms in Bavaria over a period of 8 months. In 712 Simmental and Brown Swiss cows, clinical examinations as well as milk and blood samplings were performed between 10 and 30 days after calving. In these blood samples, the concentrations of non-esterified fatty acids (NEFA), β-hydroxybutyric acid (BHBA) and haptoglobin (Hp) were determined. Analysis of the milk included milk constituents (fat, protein, urea, lactose and acetone), BHBA, NEFA and the somatic cell count (SCC)., Results: Significant correlations were found between increased Hp-concentration on the one hand and increased NEFA levels in blood and milk (p < 0.001), increased somatic cell count (p < 0.001), lameness (p < 0.001), as well as reduced lactose content (p < 0.001) and protein content in the milk (p = 0.001) on the other hand. Animals sampled during the warmer summer months showed significantly higher serum Hp-concentrations (p < 0,001). Heifers exhibited significantly higher Hp-values than multiparous individuals (p < 0.001). By dividing the examined cows into 4 clusters, a Hp-threshold value could be determined at 0.18 mg/ml. Combined with a SCC threshold of 40 500 cells/ml milk, the majority of animals with subclinical and clinical abnormalities could be identified., Conclusion and Clinical Relevance: Measurement of the Hp-concentration in blood is a pertinent approach in animal health monitoring during the postpartum period. In combination with evaluations of milk amount and contents, deviations from the physiological status may be recognized and affected individuals treated early on. Haptoglobin may be used to assess the health status of the individual animal as well as an indicator of herd health in the context of animal health monitoring., Competing Interests: Die Autoren bestätigen, dass kein Interessenkonflikt besteht., (© Georg Thieme Verlag KG Stuttgart · New York.)

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m 2 , urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor., (Copyright © 2019 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Angiogenesis is essential for the successful repair of tissues; however, in many chronic conditions, angiogenesis is inhibited. Placental tissues have been shown to illicit an angiogenic response both in vitro and in vivo, and the angiogenic properties of these tissues likely contribute to observed clinical outcomes. Although there is some work describing the angiogenic effects of these tissues, comparatively little has been done to determine the possible mechanisms responsible for this effect. The purpose of this study was to conduct a thorough evaluation of a commercially available dehydrated amnion chorion membrane to better understand how these tissues may promote angiogenesis. The proteomic content of this tissue was evaluated using a high throughput proteomic microarray, and then the effects of these grafts were evaluated in vivo using subcutaneous gelfoam sponge implants containing conditioned media (CM) from the graft. Human microvascular endothelial cells were then used to determine how released factors effect migration, proliferation, gene expression, and protein production in vitro. Finally, to elucidate potential signaling-pathways through which tissue-derived factors act to induce pro-angiogenetic phenotypes in endothelial cells in vitro, we performed a global analysis of both serine/threonine and tyrosine kinase activity. Kinomic and proteomic data were then combined to generate protein-protein interaction networks that enabled the identification of multiple growth factors and cytokines with both pro- and anti-angiogenetic properties. In vivo, the addition of CM resulted in increased CD31 and αSMA staining and increases in pro-angiogenic gene expression. In vitro, CM resulted in significant increases in endothelial proliferation, migration, and the expression of granulocyte-macrophage colony-stimulating factor, hepatocyte growth factor, and transforming growth factor beta-3. Integrated kinomic analysis implicated ERK1/2 signaling as the primary pathway activated following culture of endothelial cells with dehydrated amnion/chorion membrane (dACM) CM. In conclusion, dACM grafts triggered pro-angiogenic responses both in vitro and in vivo that are likely at least partially mediated by ERK1/2 signaling., (© 2019 The Authors. Wound Repair and Regeneration published by Wiley Periodicals, Inc. on behalf of by the Wound Healing Society.)

Background: Fibroblast growth factor 23 (FGF23) is associated with left ventricular hypertrophy (LVH) in patients with chronic kidney disease, and calcimimetic therapy reduces plasma concentrations of FGF23. It remains unknown whether treatment with the calcimimetic etelcalcetide (ETL) reduces LVH in patients on hemodialysis., Methods/design: This single-blinded randomized trial of 12 months duration will test the effects of ETL compared with alfacalcidol on LVH and cardiac fibrosis in maintenance hemodialysis patients with secondary hyperparathyroidism. Both treatment regimens will be titrated to equally suppress secondary hyperparathyroidism while alfacalcidol treatment causes an increase and ETL a decrease in FGF23, respectively. Patients treated thrice weekly with hemodialysis for ≥ 3 months and ≤ 3 years with parathyroid hormone levels ≥ 300 pg/ml and LVH will be enrolled in the study. The primary study endpoint is change from baseline to 12 months in left ventricular mass index (LVMI; g/m 2 ) measured by cardiac magnetic resonance imaging. Sample size calculations showed that 62 randomized patients will be necessary to detect a difference in LVMI of at least 20 g/m 2 between the two groups at 12 months. Due to the strong association of volume overload and LVH, randomization will be stratified by residual kidney function, and regular body composition monitoring will be performed to control the volume status of patients. Study medication will be administered intravenously by the dialysis nurses after every hemodialysis session, thus omitting adherence issues. Secondary study endpoints are cardiac parameters measured by echocardiography, biomarker concentrations of bone metabolism (FGF23, vitamin D, parathyroid hormone, calcium, phosphate, s-Klotho), cardiac markers (pro-brain natriuretic peptide, pre- and postdialysis troponin T) and metabolites of the renin-angiotensin-aldosterone cascade (angiotensin I (Ang I), Ang II, Ang-(1-7), Ang-(1-5), Ang-(1-9), and aldosterone)., Discussion: The causal inference and pathophysiology of LVH regression by FGF23 reduction using calcimimetic treatment has not yet been shown. This intervention study has the potential to discover a new strategy for the treatment of cardiac hypertrophy and fibrosis in patients on maintenance hemodialysis. It might be speculated that successful treatment of cardiac morphology will also reduce the risk of cardiac death in this population., Trial Registration: European Clinical Trials Database, EudraCT number 2017-000222-35; ClinicalTrials.gov, NCT03182699 . Registered on.

The purpose of this study is to characterise the composition of a dehydrated amnion and chorion graft and investigate how factors released from this graft interact with cells important to the wound microenvironment using in vitro models. Characterisation was completed by proteomic analysis of growth factors and cytokines, evaluation of matrix components and protease inhibition, immunohistochemistry, and in vitro release of key growth factors and cytokines. To evaluate the effect of released factors on cells found within the microenvironment, in vitro assays including: cell proliferation, migration, gene expression, protein production, and intracellular pathway activation were used; additionally, responses of fibroblasts in the context of inflammation were measured. We found that released factors from dehydrated amnion/chorion membranes (dACM) stimulated cell proliferation, migration, and altered gene and protein expression profiles of cells important for wound repair in vitro. When cells were cultured in the presence of pro-inflammatory cytokines, the addition of releasate from dACM resulted in an altered production of cytokines, including a reduction of pro-inflammatory regulated on activation, normal T cell expressed and secreted (RANTES). In sum, the results presented here characterise the components of dACM, and in vitro studies were used to evaluate interactions of dACM with cell types important in wound healing., (© 2019 The Authors. International Wound Journal published by Medicalhelplines.com Inc and John Wiley & Sons Ltd.)

Organophosphorus compounds (OPs) continue to represent a significant chemical threat to humans due to exposures from their use as weapons, their potential storage hazards, and from their continued use agriculturally. Existing methods for detection include ELISA and mass spectrometry. The new approach presented here provides an innovative first step toward a portable OP quantification method that surmounts conventional limitations involving sensitivity, selectivity, complexity, and portability. DNA affinity probes, or aptamers, represent an emerging technology that, when combined with a mix-and-read, free-solution assay (FSA) and a compensated interferometer (CI) can provide a novel alternative to existing OP nerve agent (OPNA) quantification methods. Here it is shown that FSA can be used to rapidly screen prospective aptamers in the biological matrix of interest, allowing the identification of a 'best-in-class' probe. It is also shown that combining aptamers with FSA-CI enables quantification of the OPNA metabolites, Sarin (NATO designation "G-series, B", or GB) and Venomous Agent X (VX) acids, rapidly with high selectivity at detection limits of sub-10 pg/mL in 25% serum (by volume in PBS). These results suggest there is potential to directly impact diagnostic specificity and sensitivity of emergency response testing methods by both simplifying sample preparation procedures and making a benchtop reader available for OPNA metabolite quantification., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Pre-emptive kidney transplantation is the recommended strategy for patients with end-stage renal failure in all guidelines [Kidney Disease: Improving Global Outcomes (KDIGO), The Australian and New Zealand Dialysis and Transplantation Registry (ANZDATA), European Renal Best Practice Guideline (ERBP), British Transplant Society (BTS)]. This recommendation is intuitive and based on few older studies with considerable limitations. In addition, there is conflicting evidence as to whether the duration of dialysis vintage impacts on graft and patient survival after transplantation. The objective of this structured review was to critically review the published evidence on dialysis vintage and outcomes by including the most recent papers on that topic. We searched Medline using keywords for kidney transplantation, pre-emptive, dialysis vintage and relevant outcomes, and found 14 eligible cohort studies. The best evidence was found for pre-emptive transplantation, which was found to be associated with a lower risk of actual graft loss (including death as event) compared with non-pre-emptive transplantation. When only patients were considered that have been registered pre-emptively but then received or did not receive a pre-emptive transplant, the association with functional graft survival (excluding death as event) was only marginal. Dialysis vintage had a graded association with patient survival in most of the studies, but an unclear estimate with functional graft survival. Older studies also found an association of dialysis vintage with death-censored graft survival, but this association is likely confounded by selection and the competing risk of death and was no longer observed in recent eras, i.e. in transplants performed in the last decade. In summary, the recommendation for pre-emptive kidney transplantation for optimal patient and graft survival remains valid even in recent periods but the association of dialysis vintage after dialysis initiation with death-censored graft survival is less clear. The association of dialysis vintage with mortality after transplantation depends on the median duration of dialysis of the wait-listed population as well as acceptance rates for transplantation, and may thus be country specific. Nevertheless, it is reasonable to advocate pre-emptive kidney transplantation in all age groups. What remains unsolved is the selection issues since the reasons for longer waiting time on dialysis are difficult to capture in retrospective observational studies, and lead time as well as immortal time bias may have confounded the mortality data., (© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The introduction of HLA matching of donors and recipients was a breakthrough in kidney transplantation. However, half of all transplanted kidneys still fail within 15 years after transplantation. Epidemiological data suggest a fundamental role of non-HLA alloimmunity., Methods: We genotyped 477 pairs of deceased donors and first kidney transplant recipients with stable graft function at three months that were transplanted between Dec 1, 2005, and April 30, 2015. Genome-wide genetic mismatches in non-synonymous single nucleotide polymorphisms (nsSNPs) were calculated to identify incompatibilities in transmembrane and secreted proteins. We estimated the association between nsSNP mismatch and graft loss in a Cox proportional hazard model, adjusting for HLA mismatch and clinical covariates. Customised peptide arrays were generated to screen for antibodies against genotype-derived mismatched epitopes in 25 patients with biopsy-confirmed chronic antibody-mediated rejection., Findings: 59 268 nsSNPs affecting a transmembrane or secreted protein were analysed. The median number of nsSNP mismatches in immune-accessible transmembrane and secreted proteins between donors and recipients was 1892 (IQR 1850-1936). The degree of nsSNP mismatch was independently associated with graft loss in a multivariable model adjusted for HLA eplet mismatch (HLA-A, HLA-B, HLA-C, HLA-DP, HLA-DQ, and HLA-DR). Each increase by a unit of one IQR had an HR of 1·68 (95% CI 1·17-2·41, p=0·005). 5-year death censored graft survival was 98% in the quartile with the lowest mismatch, 91% in the second quartile, 89% in the third quartile, and 82% in the highest quartile (p=0·003, log-rank test). Customised peptide arrays verified a donor-specific alloimmune response to genetically predicted mismatched epitopes., Interpretation: Genetic mismatch of non-HLA haplotypes coding for transmembrane or secreted proteins is associated with an increased risk of functional graft loss independently of HLA incompatibility. As in HLA alloimmunity, donor-specific alloantibodies can be identified against genotype derived non-HLA epitopes., Funding: Austrian Science Fund, WWTF (Vienna Science and Technology Fund), and Ministry of Health of the Czech Republic., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), naïve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and β-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Tolerance induction through simultaneous hematopoietic stem cell and renal transplantation has shown promising results, but it is hampered by the toxicity of preconditioning therapies and graft-versus-host disease (GVHD). Moreover, renal function has never been compared to conventionally transplanted patients, thus, whether donor-specific tolerance results in improved outcomes remains unanswered. We collected follow-up data of published cases of renal transplantations after hematopoietic stem cell transplantation from the same donor and compared patient and transplant kidney survival as well as function with caliper-matched living-donor renal transplantations from the Austrian dialysis and transplant registry. Overall, 22 tolerant and 20 control patients were included (median observation period 10 years [range 11 months to 26 years]). In the tolerant group, no renal allograft loss was reported, whereas 3 were lost in the control group. Median creatinine levels were 85 μmol/l (interquartile range [IQR] 72-99) in the tolerant cohort and 118 μmol/l (IQR 99-143) in the control group. Mixed linear-model showed around 29% lower average creatinine levels throughout follow-up in the tolerant group (P < .01). Our data clearly show stable renal graft function without long-term immunosuppression for many years, suggesting permanent donor-specific tolerance. Thus sequential transplantation might be an alternative approach for future studies targeting tolerance induction in renal allograft recipients., (© 2018 The Authors American Journal of Transplantation published by Wiley Periodicals, Inc. on behalf of The American Society of Transplantation and the American Society of Transplant Surgeons.)

Objective: The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable, and early interventions would likely be cost-effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors., Research Design and Methods: We studied participants in PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers), a prospective multinational cohort study of patients with type 2 diabetes and a follow-up of more than 24 months ( n = 2,560; baseline median eGFR, 84 mL/min/1.73 m 2 ; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling., Results: In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R 2 of 62%. A panel of 12 biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% was due to 5 markers. The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline ( R 2 of 79%), and the contribution of each biomarker dropped below 1%., Conclusions: In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low., (© 2018 by the American Diabetes Association.)

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood β-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Background: Patients with spinal cord injury (SCI) are at risk of thrombosis and bleeding. Data on the risks during rehabilitation are inconsistent, and thromboprophylactic strategies are heterogeneous. We aimed to evaluate the thrombotic risk and bleeding events of SCI patients during rehabilitation., Methods: We retrospectively collected hospital record data of 263 consecutive SCI patients admitted at a rehabilitation clinic. 78 patients with acute venous thromboembolism (VTE) at the primary center, without acute trauma or lower extremity paresis, less than one month rehabilitation, or reasons for long-term therapeutic anticoagulation, were excluded. All patients received pharmacologic thromboprophylaxis throughout rehabilitation. Primary endpoint was objectively diagnosed VTE; secondary endpoint was bleeding., Results: Of 185 patients, 162 (88%) were men; mean age was 47.8 years. 94 patients were tetraplegic, 91 paraplegic. During a mean (±SD) time of 5.1±2.1 months, VTE was diagnosed in 8 patients. After excluding five patients with VTE detected within 2 days after admission, the probability of developing VTE after 6 months of rehabilitation was 2% (95% CI 0-4.4%). Only high D-Dimer upon admission was associated with risk of VTE (adjusted HR 2.3, 95% CI 1.4-4.1). Of 24 bleedings, 14 (64%) occurred at the heparin injection site. Two patients had major bleeding and five had clinically relevant non major bleeding., Conclusion: SCI patients are at risk of VTE and bleeding during rehabilitation. Strategies need to be developed to identify these patients in order to initiate adequate anticoagulation. Direct oral anticoagulants, which have a favourable risk-benefit profile and are convenient, should be explored.

Renal transplantation reduces the dramatically elevated risk of cardiovascular death in dialysis patients. We previously showed that left atrial diameter before transplantation predicts cardiovascular and overall mortality. Now, we investigated the association of changes in cardiac morphology after transplantation and mortality. We retrospectively analyzed data from the Austrian transplant repository using multivariable Cox and competing risk models and multivariable logistic regression for the prediction of changes in cardiac morphology. We identified 414 patients with a median follow-up of 8 years and observed a significant progression of mean diameter of left atrium (LA), right atrium and right ventricle and a significant regression of left ventricle. Complete case analysis of 243 patients with a regression of initially enlarged LA diameter had a significantly lower risk of adjusted overall and cardiovascular mortality; hazard ratio (HR 0.45, 95% CI 0.30-0.69, P < 0.001, 124 deaths), and HR of 0.43 [95% CI 0.21-0.92, P = 0.029, 48 cardiovascular (CV) deaths], respectively. Only age at transplantation was significantly associated with regression of LA (OR 0.75, 95% CI 0.60-0.93, P = 0.007). Patients with regression of LA after kidney transplantation exhibited a lower overall and CV mortality risk. Besides age, peritoneal dialysis and antihypertensive therapy were mediators of LA regression., (© 2018 The Authors. Transplant International published by John Wiley & Sons Ltd on behalf of Steunstichting ESOT.)

Essentials Natural antibodies to oxidation-specific epitopes have antithrombotic properties. We evaluated the relation between natural IgM and IgG antibodies and the venous thrombosis risk. Risk of recurrent thrombosis was higher in patients with low natural IgM antibody levels. The protective effect of high IgM levels suggests a role of innate immune response in thrombosis., Summary: Background and objectives Natural antibodies to oxidation-specific epitopes protect from atherothrombotic events. Whether mechanisms of innate immunity are relevant in the pathogenesis of venous thromboembolism (VTE) is unknown. Patients/Methods We measured plasma levels of immunoglobulin M (IgM) antibodies to oxidized low-density lipoproteins (OxLDL) and phosphocholine (PC) by enzyme linked immune assay in 663 patients with unprovoked VTE, who were prospectively followed after discontinuation of anticoagulation for a median of 8.8 years. The study endpoint was recurrent VTE. Results IgM antibody levels to OxLDL and PC were higher in patients without compared to those with recurrent VTE (n = 174, 26.2%). For each doubling of OxLDL-IgM or PC-IgM the hazard ratio (HR) of recurrence was 0.88 (95% confidence interval [CI], 0.77-1.01) and 0.82 (95% CI, 0.71-0.94), respectively. After 5 years the probability of recurrence in patients with PC-IgM levels in the highest tertile (> 19.6 RLU/100 ms) was 13.0% (95% CI, 8.1-17.6%), compared with 21.1% (95% CI, 14.9-26.9%) in the middle tertile and 20.6% (95% CI, 14.7-26.0%) in the lowest tertile. The corresponding HR was 0.56 (0.39-0.82) for PC-IgM levels in the highest compared with the lowest tertile. Neither immunoglobulin G IgG antibody levels to OxLDL nor those to PC were associated with risk of VTE. Conclusion Levels of natural IgM antibodies to oxidation-specific epitopes are inversely related to the risk of VTE., (© 2017 International Society on Thrombosis and Haemostasis.)

Elderly patients represent a growing population among people suffering from ESRD. So far only limited data on actual survival benefits of elderly adults initiating dialysis have been published. Besides the high burden of preexisting comorbidities, dialysis treatment itself may be associated with a further deterioration in functional status in this population. We retrospectively analyzed the Austrian Dialysis and Transplant Registry and identified 8,622 patients who started maintenance hemodialysis after the age of 65 years between 2002 and 2009. We compared this data set to a cohort of 174 patients aged over 65 years with CKD stage 5 who progressed to an eGFR < 10ml/min/ and were managed conservatively in the same era. All patients who died of malignant disease were excluded from this analysis. The risk of mortality was analyzed using multivariable Cox proportional hazards models. Furthermore, a parametric model of time to event analysis was used for visualization of changing risk over time and precise calculation of time to equal risk assuming a Weibull distribution. Hemodialysis treatment was associated with a decreased risk for death with a HR of 0.23 (95% CI 0.18 to 0.29; p<0.001) compared to conservative treatment. The time to event analysis however showed, that although survival was initially superior in the hemodialysis group, hazards crossed thereafter. Time to equal risk was 2.9 months and 1.9 months for female and male patient aged 65, respectively, and decreased to one month in the very elderly aged 95. Elderly patients with ERSD did benefit from initiation of hemodialysis, as the conservative group showed a very high initial mortality rate. This survival benefit of dialysis treatment however did not persist beyond the first two months compared to survivors of the conservative group.

Background: Because chronic fluid volume overload is associated with higher mortality, we tested whether blood-volume monitored regulation of ultrafiltration and dialysate conductivity (UCR) and/or regulation of ultrafiltration and temperature (UTR) would facilitate dry weight reduction, in comparison to conventional dialysis (CONV)., Methods: We carried out a multicenter, 4-week, randomized controlled trial in hemodialysis patients ≥15% above normal extracellular fluid volume (ECV), per bioimpedance spectroscopy, who were randomized 1:1:1. Applying UCR (Nikkiso), UTR (Fresenius) and CONV, initial dry weight was reduced rapidly to target. Dry weight reduction was attenuated and eventually stopped at the occurrence of dialysis complications. The primary outcome was defined as intra- and postdialytic complications. Secondary outcomes were magnitudes of dry weight and blood pressure reduction., Results: Of 244 patients assessed, N = 95 had volume overload ≥15% above normal ECV. Fifty patients received the allocated interventions (N = 16 UCR, N = 18 UTR, N = 16 CONV) and completed the trial. The rate of complications was significantly lower in UTR compared to CONV (21 ± 21% vs 34 ± 20%, p = 0.022), and also compared to UCR (vs 39 ± 27%, p = 0.028), but not statistically different between UCR and CONV (p = 0.93). Dry weight reduction was significantly higher in UTR compared to UCR (5.0 ± 3.4% vs 2.0 ± 2.7% body weight, p = 0.013), but not compared to CONV (vs 3.9 ± 2.1% body weight, p = 0.31). Systolic blood pressure reduction throughout the intervention phase was 17 ± 22 mmHg overall, but not significantly different between the three groups. Average maximum ultrafiltration rates were significantly higher in UTR than in UCR and CONV, at statistically similar dialysis times. Retrospective examination of randomly selected hemodialysis sessions in the UCR group identified technical mistakes in 36% of the dialysis sessions, despite considerable training efforts., Conclusions: Even in patients with volume overload, fluid removal was challenging. Despite the relative advantage of UTR, which must be interpreted with caution in view of the poor technical execution of UCR, this study renders clear that fluid removal must not be reinforced rapidly. Apprehension of this obstacle is imperative for future clinical and academic endeavors aimed at improving dialysis outcomes by correcting volume status., Trial Registration: ClinicalTrials.gov ( NCT01416753 ), trial registration date: August 12, 2011.

Essentials Data on long-term cancer risk are controversial in patients with venous thromboembolism (VTE). We assessed long-term rates and risk factors of cancer in patients with VTE. Cancer risk after anticoagulation is not higher in VTE patients than in the general population. VTE recurrence is not predictive of a future cancer diagnosis., Summary: Background Patients with venous thromboembolism (VTE) are at risk of having a subsequent cancer diagnosis. The risk is highest during the first 6 months. Reports on cancer rates thereafter are controversial. We aimed to assess long-term rates and risk factors of cancer in patients with VTE. Methods and Results We followed patients with a first unprovoked VTE after discontinuation of anticoagulation, and excluded those receiving long-term antithrombotic therapy or with major thrombophilia. The study endpoint was the occurrence of cancer. Sixty-two (5.2%) of 1188 patients developed cancer during a median follow-up of 98 months. The cumulative incidence rates of cancer were 0.7% (95% confidence interval [CI] 0.2-1.2%), 3.1% (95% CI 2.0-4.1%) and 9% (95% CI 6.5-11.5) after 1, 5 and 15 years; these were not significantly different from those in the matched general population (0.6%, 3.4%, and 12.2%, respectively). The corresponding standardized incidence ratios (ratio of the observed cancer cases and the number of cases based on national cancer incidence rates) of 1.1 (95% CI 0.5-2.5), 1.0 (95% CI 0.6-1.4) and 0.9 (95% CI 0.7-1.2) did not indicate a difference in cancer incidence between our cohort and the general population. Advancing age (hazard ratio [HR] per decade 1.5, 95% CI 1.2-2.0) and shorter duration of anticoagulation (HR per 1-month decrease 1.3, 95% CI 1.1-1.6) were associated with an increased cancer risk, whereas VTE recurrence was not (HR 1.17, 95% CI 0.66-2.07). Conclusions Asymptomatic patients with unprovoked VTE who have completed anticoagulation therapy do not have a higher cancer risk. The inverse association between the duration of anticoagulation and the incidence of cancer warrants further investigation., (© 2017 International Society on Thrombosis and Haemostasis.)

Background: Immunosuppressive regimens in renal transplantation frequently contain corticosteroids, but many centers withdraw steroids as a consequence of unwanted side effects of steroids. The optimal timing to withdraw steroids after transplantation, however, remains unclear. The aim of this study was to determine an optimal time point following kidney transplantation that is associated with reduced mortality without jeopardizing the allograft to allow safe discontinuation of steroids., Methods: We conducted a retrospective cohort study and computed a concatenated landmark-stratified Cox supermodel to estimate hazard ratios and 95% confidence intervals for mortality and graft loss using dynamic propensity score matching to adjust for confounding by indication., Results: A total of 6070 first kidney transplant recipients in the Austrian Dialysis and Transplant Registry who were transplanted between 1990 and 2012 were evaluated and classified according to steroid treatment status throughout follow-up after kidney transplantation; 2142 patients were withdrawn from steroids during the study period. Overall, 1131 patients lost their graft and 821 patients in the study cohort died. Steroid withdrawal within 18 months after transplantation was associated with an increased rate of graft loss compared to steroid maintenance during that time (6 months after transplantation: HR = 1.8; 95% CI, 1.3 to 2.6; 18 months after transplantation: HR = 1.3; 95% CI, 1.1 to 1.6; 24 months after transplantation: HR = 1.2; 95% CI, 0.9 to 1.5), while mortality was not different between groups., Conclusions: Our findings suggest that steroid withdrawal after anti-IL-2 induction in the first 18 months after transplantation is associated with an increased risk of allograft loss.