Predictors of Nonseroconversion to SARS-CoV-2 Vaccination in Kidney Transplant Recipients

Kidney transplant recipients (KTRs) are still at risk of severe COVID-19 disease after SARS‑CoV‑2 vaccination, especially when they have limited antibody formation. Our aim was to understand the factors that may limit their humoral response. METHODS. Our data are derived from KTRs who were enrolled in the Dutch Renal Patients COVID-19 Vaccination consortium, using a discovery cohort and 2 external validation cohorts. Included in the discovery (N = 1804) and first validation (N = 288) cohorts were participants who received 2 doses of the mRNA-1273 vaccine. The second validation cohort consisted of KTRs who subsequently received a third dose of any SARS-CoV-2 vaccine (N = 1401). All participants had no history of SARS-CoV-2 infection. A multivariable logistic prediction model was built using stepwise backward regression analysis with nonseroconversion as the outcome. RESULTS. The discovery cohort comprised 836 (46.3%) KTRs, the first validation cohort 124 (43.1%) KTRs, and the second validation cohort 358 (25.6%) KTRs who did not seroconvert. In the final multivariable model‚ 12 factors remained predictive for nonseroconversion: use of mycophenolate mofetil/mycophenolic acid (MMF/MPA); chronic lung disease, heart failure, and diabetes; increased age; shorter time after transplantation; lower body mass index; lower kidney function; no alcohol consumption; ≥2 transplantations; and no use of mammalian target of rapamycin inhibitors or calcineurin inhibitors. The area under the curve was 0.77 (95% confidence interval [CI], 0.74-0.79) in the discovery cohort after adjustment for optimism, 0.81 (95% CI, 0.76-0.86) in the first validation cohort, and 0.67 (95% CI, 0.64-0.71) in the second validation cohort. The strongest predictor was the use of MMF/MPA, with a dose-dependent unfavorable effect, which remained after 3 vaccinations. CONCLUSIONS. In a large sample of KTRs, we identify a selection of KTRs at high risk of nonseroconversion after SARS-CoV-2 vaccination. Modulation of MMF/MPA treatment before vaccination may help to optimize vaccine response in these KTRs. This model contributes to future considerations on alternative vaccination strategies

Tumor Ulceration Does Not Fully Explain Sex Disparities in Melanoma Survival among Adolescents and Young Adults

Hypertension in kidney transplant recipients (KTRs) is a risk factor for cardiovascular mortality and graft loss. Data on the prevalence of hypertension and uncontrolled hypertension (uHT) in paediatric and young adult KTRs are scarce. Also, it is unknown whether 'transition' (the transfer from paediatric to adult care) influences control of hypertension. We assessed the prevalence of hypertension and uHT among Dutch paediatric and young adult KTRs and analysed the effects of transition. Additionally, we made an inventory of variations in treatment policies in Dutch transplant centres. Cross-sectional and longitudinal national data from living KTRs a parts per thousand currency sign30 years of age (a parts per thousand yen1-year post-transplant, eGFR > 20 mL/min) were extracted from the 'RICH Q' database, which comprises information about all Dutch KTRs <19 years of age, and the Netherlands Organ Transplant Registry database for adult KTRs (a parts per thousand yen18-30 years of age). We used both upper-limit blood pressure (BP) thresholds for treatment according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. uHT was defined as a BP above the threshold. A questionnaire on treatment policies was sent to paediatric and adult nephrologists at eight Dutch transplant centres. Hypertension and uHT were more prevalent in young adult KTRs (86.4 and 75.8%) than in paediatric KTRs (62.7 and 38.3%) according to the KDIGO definition. Time after transplantation was comparable between these groups. Longitudinal analysis showed no evidence of effect of transition on systolic BP or prevalence of uHT. Policies vary considerably between and within centres on the definition of hypertension, BP measurement and antihypertensive treatment. Average BP in KTRs increases continuously with age between 6 and 30 years. Young adult KTRs have significantly more uHT than paediatric KTRs according to KDIGO guidelines. Transition does not influence the prevalence of uHT

Long-Term Transplantation Outcomes in Patients With Primary Hyperoxaluria Type 1 Included in the European Hyperoxaluria Consortium (OxalEurope) Registry

INTRODUCTION: In primary hyperoxaluria type 1 (PH1), oxalate overproduction frequently causes kidney stones, nephrocalcinosis, and kidney failure. As PH1 is caused by a congenital liver enzyme defect, combined liver–kidney transplantation (CLKT) has been recommended in patients with kidney failure. Nevertheless, systematic analyses on long-term transplantation outcomes are scarce. The merits of a sequential over combined procedure regarding kidney graft survival remain unclear as is the place of isolated kidney transplantation (KT) for patients with vitamin B6-responsive genotypes. METHODS: We used the OxalEurope registry for retrospective analyses of patients with PH1 who underwent transplantation. Analyses of crude Kaplan–Meier survival curves and adjusted relative hazards from the Cox proportional hazards model were performed. RESULTS: A total of 267 patients with PH1 underwent transplantation between 1978 and 2019. Data of 244 patients (159 CLKTs, 48 isolated KTs, 37 sequential liver–KTs [SLKTs]) were eligible for comparative analyses. Comparing CLKTs with isolated KTs, adjusted mortality was similar in patients with B6-unresponsive genotypes but lower after isolated KT in patients with B6-responsive genotypes (adjusted hazard ratio 0.07, 95% CI: 0.01–0.75, P = 0.028). CLKT yielded higher adjusted event-free survival and death-censored kidney graft survival in patients with B6-unresponsive genotypes (P = 0.025, P < 0.001) but not in patients with B6-responsive genotypes (P = 0.145, P = 0.421). Outcomes for 159 combined procedures versus 37 sequential procedures were comparable. There were 12 patients who underwent pre-emptive liver transplantation (PLT) with poor outcomes. CONCLUSION: The CLKT or SLKT remains the preferred transplantation modality in patients with PH1 with B6-unresponsive genotypes, but isolated KT could be an alternative approach in patients with B6-responsive genotypes

Determinants of Kidney Failure in Primary Hyperoxaluria Type 1:Findings of the European Hyperoxaluria Consortium

INTRODUCTION: Primary hyperoxaluria type 1 (PH1) has a highly heterogeneous disease course. Apart from the c.508G&gt;A (p.Gly170Arg) AGXT variant, which imparts a relatively favorable outcome, little is known about determinants of kidney failure. Identifying these is crucial for disease management, especially in this era of new therapies. METHODS: In this retrospective study of 932 patients with PH1 included in the OxalEurope registry, we analyzed genotype-phenotype correlations as well as the impact of nephrocalcinosis, urolithiasis, and urinary oxalate and glycolate excretion on the development of kidney failure, using survival and mixed model analyses.RESULTS: The risk of developing kidney failure was the highest for 175 vitamin-B6 unresponsive ("null") homozygotes and lowest for 155 patients with c.508G&gt;A and c.454T&gt;A (p.Phe152Ile) variants, with a median age of onset of kidney failure of 7.8 and 31.8 years, respectively. Fifty patients with c.731T&gt;C (p.Ile244Thr) homozygote variants had better kidney survival than null homozygotes ( P = 0.003). Poor outcomes were found in patients with other potentially vitamin B6-responsive variants. Nephrocalcinosis increased the risk of kidney failure significantly (hazard ratio [HR] 3.17 [2.03-4.94], P &lt; 0.001). Urinary oxalate and glycolate measurements were available in 620 and 579 twenty-four-hour urine collections from 117 and 87 patients, respectively. Urinary oxalate excretion, unlike glycolate, was higher in patients who subsequently developed kidney failure ( P = 0.034). However, the 41% intraindividual variation of urinary oxalate resulted in wide confidence intervals. CONCLUSION: In conclusion, homozygosity for AGXT null variants and nephrocalcinosis were the strongest determinants for kidney failure in PH1. </p

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.peer-reviewe