Systemic vasculitis involving the kidney: the nephropathologist's point of view.

Kidneys are often targets of systemic vasculitis (SVs), being affected in many different forms and representing a possible sentinel of an underlying multi-organ condition. Renal biopsy still remains the gold standard for the identification, characterization and classification of these diseases, solving complex differential diagnosis thanks to the combined application of light microscopy (LM), immunofluorescence (IF) and electron microscopy (EM). Due to the progressively increasing complexity of renal vasculitis classification systems (e.g. pauci-immune vs immune complex related forms), a clinico-pathological approach is mandatory and adequate technical and interpretative expertise in nephropathology is required to ensure the best standard of care for our patients. In this complex background, the present review aims at summarising the current knowledge and challenges in the world of renal vasculitis, unveiling the potential role of the introduction of digital pathology in this setting, from the creation of hub-spoke networks to the future application of artificial intelligence (AI) tools to aid in the diagnostic and scoring/classification process.

Bilateral renal lymphangiectasia: Literature review of a rare entity.

Renal lymphangiectasia (RL) is a rare condition in which lymphatic vessels are dilated giving rise to cyst formation in peripelvic, perirenal and intrarenal locations. Knowledge about RL is limited and based upon individual case reports. This can be genetic or acquired. There is no significant association with any gender or age. It can be manifested as focal or diffuse forms and can be unilateral or bilateral. Most of the cases present with abdominal or flank pain. The diagnosis is based on radiological imaging. Due to rarity of diseases, it has potential to be misdiagnosed as other cystic disease of kidneys. The treatment is mainly conservative but prolonged follow up for associated complications like hypertension and renal vein thrombosis is required. We have presented a case of bilateral renal lymphangiectasia with the review of available literature.

Noninvasive and Multiplex Self-Test of Kidney Disease Biomarkers with Graphene-Based Lab-on-a-Chip (G-LOC): Toward Digital Diagnostics in the Hands of Patients.

Chronic kidney disease is one of the major health issues worldwide. However, diagnosis is now highly centralized in large laboratories, resulting in low access to patient monitoring and poor personalized treatments. This work reports the development of a graphene-based lab-on-a-chip (G-LOC) for the digital testing of renal function biomarkers in serum and saliva samples. G-LOC integrates multiple bioelectronic sensors with a microfluidic system that enables multiplex self-testing of urea, potassium, sodium, and chloride. The linearity, limit of detection (LOD), accuracy, and coefficient of variability (CV) were studied. Accuracy values higher than 95.5% and CV lower than 9% were obtained for all of the biomarkers. The analytical performance was compared against three reference lab benchtop analyzers by measuring healthy- and renal-failure-level samples of serum. From receiver operating characteristic (ROC) plots, sensitivities (%) of 99.7, 97.6, 99.1, and 89.0 were obtained for urea, potassium, sodium, and chloride, respectively. Then, the test was evaluated in noninvasive saliva samples and compared against reference methods. Correlation and Bland-Altman plots showed good correlation and agreement of the G-LOC with the reference methods. It is noteworthy that the precision of G-LOC was similar to better than benchtop lab analyzers, with the advantage of being highly portable. Finally, a user testing study was conducted. The analytical performance obtained with untrained volunteers was similar to that obtained with trained chemists. Additionally, based on a user experience survey, G-LOC was found to have very simple usability and would be suitable for at-home diagnostics.

Predictive effect of estimated glomerular filtrate rate by creatinine or cystatin C on mortality in patients with coronary artery disease.

BACKGROUND: Renal dysfunction leads to poor prognosis of patients with coronary artery disease (CAD). Current studies have reported the prognosis or mortality of various diseases using different estimated glomerular filtrate rate (eGFR) formulas, while the performance of these equations is unclear in CAD patients. We aim to evaluate the predict effect of creatinine-based eGFR (eGFRcr), cystatin C-based eGFR (eGFRcys), and both creatinine and cystatin C-based eGFR (eGFRcr-cys) in CAD patients. METHODS: A total of 23,178 patients with CAD were included from CIN-II cohort study. The association of eGFRcr, eGFRcys and eGFRcr-cys with cardiovascular and all-cause mortality was detected by Cox regression analysis. The predictive effect of eGFRcr, eGFRcys and eGFRcr-cys on mortality was assessed. RESULTS: During a median follow up of 4.3 years, totally 2051 patients (8.8%) experience all-cause mortality, of which 1427 patients (6.2%) died of cardiovascular disease. For the detection of cardiovascular mortality among CAD patients, eGFRcr-cys had high discriminatory capacity with area under the curve (AUC) in receiver operator characteristic analysis of 0.730, which was significantly better than eGFRcr (AUC = 0.707, p < 0.001) and eGFRcys (AUC = 0.719, p < 0.001). Similar results were observed in all-cause mortality. Restricted cubic spline showed a U-shaped association between eGFRcr and all outcomes in patients with both reduced and supranormal eGFR levels, while a L-shaped association in eGFRcys and eGFRcr-cys. CONCLUSIONS: Estimated GFR based on both creatinine and cystatin C has highest predictive effect for cardiovascular and all-cause mortality among CAD patients. Meanwhile, supranormal eGFRcr may indicate a higher risk of mortality.

Clinicopathological characteristics and prognosis in patients with monoclonal gammopathy and renal damage in central China: a multicenter retrospective cohort study.

Renal involvement is common in monoclonal gammopathy (MG); however, the same patient may have both MG and non-paraprotein-associated renal damage. Accordingly, distinguishing the cause of renal damage is necessary because of the different clinical characteristics and associated treatments. In this multicenter retrospective cohort study, we described the clinicopathological characteristics and prognosis of 703 patients with MG and renal damage in central China. Patients were classified as having MG of renal significance (MGRS), MG of undetermined significance (MGUS), or hematological malignancy. 260 (36.98%), 259 (36.84%), and 184 (26.17%) had MGRS, MGUS, and hematological malignancies, respectively. Amyloidosis was the leading pattern of MGRS (74.23%), followed by thrombotic microangiopathy (8.85%) and monoclonal immunoglobulin deposition disease (8.46%). Membranous nephropathy was the leading diagnosis of MGUS (39.38%). Renal pathological findings of patients with hematological malignancies included paraprotein-associated lesions (84.78%) and non-paraprotein-associated lesions (15.22%). The presence of nephrotic syndrome and an abnormal free light chain (FLC) ratio were independently associated with MGRS. The overall survival was better in patients with MGUS than in those with MGRS or hematological malignancies.

Organ involvement in newly diagnosed sarcoidosis patients in the Netherlands: The first large European multicentre prospective study.

BACKGROUND: Clinical presentation and prevalence of organ involvement is highly variable in sarcoidosis and depends on ethnic, genetic and geographical factors. These data are not extensively studied in a Dutch population. AIM: To determine the prevalence of organ involvement and the indication for systemic immunosuppressive therapy in newly diagnosed sarcoidosis patients in the Netherlands. METHODS: Two large Dutch teaching hospitals participated in this prospective cohort study. All adult patients with newly diagnosed sarcoidosis were prospectively included and a standardized work-up was performed. Organ involvement was defined using the WASOG instrument. RESULTS: Between 2015 and 2020, a total of 330 patients were included, 55% were male, mean age was 46 (SD 14) years. Most of them were white (76%). Pulmonary involvement including thoracic lymph node enlargement was present in 316 patients (96%). Pulmonary parenchymal disease was present in 156 patients (47%). Ten patients (3%) had radiological signs of pulmonary fibrosis. Cutaneous sarcoidosis was present in 74 patients (23%). Routine ophthalmological screening revealed uveitis in 29 patients (12%, n = 256)). Cardiac and neurosarcoidosis were diagnosed in respectively five (2%) and six patients (2%). Renal involvement was observed in 11 (3%) patients. Hypercalcaemia and hypercalciuria were observed in 29 (10%) and 48 (26%, n = 182) patients, respectively. Hepatic involvement was found in 6 patients (2%). In 30% of the patients, systemic immunosuppressive treatment was started at diagnosis. CONCLUSIONS: High-risk organ involvement in sarcoidosis is uncommon at diagnosis. Indication for systemic immunosuppressive therapy was present in a minority of patients.

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology.

INTRODUCTION: Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification. METHODS: We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains. RESULTS: There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms. CONCLUSION: Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.

Mind the Gap in Kidney Care: Translating What We Know Into What We Do.

Historically, it takes an average of 17 years to move new treatments from clinical evidence to daily practice. Given the highly effective treatments now available to prevent or delay kidney disease onset and progression, this is far too long. The time is now to narrow the gap between what we know and what we do. Clear guidelines exist for the prevention and management of common risk factors for kidney disease, such as hypertension and diabetes, but only a fraction of people with these conditions worldwide are diagnosed, and even fewer are treated to target. Similarly, the vast majority of people living with kidney disease are unaware of their condition, because in the early stages it is often silent. Even among patients who have been diagnosed, many do not receive appropriate treatment for kidney disease. Considering the serious consequences of kidney disease progression, kidney failure, or death, it is imperative that treatments are initiated early and appropriately. Opportunities to diagnose and treat kidney disease early must be maximized beginning at the primary care level. Many systematic barriers exist, ranging from patient to clinician to health systems to societal factors. To preserve and improve kidney health for everyone everywhere, each of these barriers must be acknowledged so that sustainable solutions are developed and implemented without further delay. DOI: 10.52547/ijkd.8216.

Incidence of atrial fibrillation in patients with renal infarction: A retrospective cohort analysis of the Korean national health insurance registry.

BACKGROUND: Regarding the pathophysiology of renal infarction (RI), cardioembolic causes could have large proportion. However, there are notable variations in prevalence of atrial fibrillation (AF) among patients with RI across different studies, ranging from 17 to 65%. The primary objective of this study is to analyze the incidence of AF in patients with RI. METHODS: This nationwide retrospective cohort study enrolled 5200 patients with RI from the Korean National Institute of Health Services database spanning the years 2013 to 2019. The study accessed the AF incidence rate within 12 months in patients without a prior history of AF. Events occurring within 3 months of RI diagnosis were excluded to mitigate cases diagnosed during the initial screening or those with AF diagnoses that were potentially overlooked in the past. RESULTS: AF occurred in 19.1% of patients with RI over the entire period (median: 2.5 years, interquartile range 1.04-4.25 years). The majority of AF cases (16.1%) occured within the first year, resulting in an overall incidence rate of 7.0 per 100 person-years. Patients with newly developed AF were, on average, older than those who did not develop AF (64.1 vs. 57.3 years, P < 0.001). The independent predictors of AF were identified as age, male sex, higher body mass index, current smoking, ischemic heart disease, and heart failure. CONCLUSIONS: Physicians should consider the implementation of active rhythm monitoring for patients with RI to identify potential occurrence of subclinical AF, even if not initially diagnosed during the initial screening after RI diagnosis.

[Consensus on diagnosis and treatment of complement-mediated kidney diseases].

In recent years, the role of complement in various kidney diseases has been gradually elucidated, and drugs targeting complement system have emerged successively, and some of them have been applied in clinical practice. However, there are some problems with complement-targeted therapy. This consensus was initiated by a collaborative group of experts in the diagnosis and treatment of complement-mediated kidney diseases from the medical department of Peking University. Their efforts drew upon domestic and international guidelines/consensuses, as well as the latest literature. The evidence and suggestions on the pathogenesis, diagnosis, complement targeted therapy and vaccine immunization of special pathogenic microorganisms of complement-mediated kidney diseases are briefly described, which aims to provide a reference for the diagnosis and treatment of complement-mediated kidney diseases.

DIAGNOSTIC PERFORMANCE OF BLOOD ANALYTES FOR THE DIAGNOSIS OF RENAL DISEASE IN BLACK-FOOTED FERRETS (MUSTELA NIGRIPES) AT THE PHOENIX ZOO (2001-2020).

Renal disease is an important cause of morbidity and mortality in managed black-footed ferrets (BFF; Mustela nigripes).4,6,12 The objectives of this study were to establish reference intervals for blood analytes of clinically normal BFF (1-2 yr old), summarize the frequency of various renal histopathologic findings in a managed population of BFF, assess the diagnostic performance of blood analytes and urine specific gravity (USG) for the diagnosis of renal disease, and assess if comorbidities or age affects the performance of these analytes in diagnosing renal disease. Reference intervals were established using a cohort (n = 35) of clinically normal, young adult BFF. Postmortem records for all BFF at the Phoenix Zoo between 2001 and 2020 were reviewed, and those with available blood analyte data within 2 wk of death were included (n = 89). Ferrets were placed into one of three groups, based on the organ location of histopathologic abnormalities following necropsy: renal disease as the primary change; those with renal disease and at least one other affected major organ system; or absence of abnormalities in the kidneys. In ferrets with substantial renal changes, the primary diagnosis was amyloidosis (29 of 39; 74.4%). Creatinine, blood urea nitrogen, phosphorus (P), calcium (Ca), Ca:P ratio, USG, globulins, and cholesterol were the best-performing analytes for the diagnosis of renal disease, with an area under the curve of at least 0.90 (95% CI $ 0.80, 1.00). Serum renal markers were within reference intervals in BFF that died without histologic evidence of renal disease. Several blood analytes were significantly affected by age in animals that died of renal disease. This study provides reference intervals for blood analytes in young adult clinically normal BFF and illustrates the clinical utility for the diagnosis of renal disease in this species, particularly creatinine, USG, and P.

Renal Progenitors Derived from Urine for Personalized Diagnosis of Kidney Diseases.

BACKGROUND: Chronic kidney disease affects 10% of the world population, and it is associated with progression to end-stage kidney disease and increased morbidity and mortality. The advent of multi-omics technologies has expanded our knowledge on the complexity of kidney diseases, revealing their frequent genetic etiology, particularly in children and young subjects. Genetic heterogeneity and drug screening require patient-derived disease models to establish a correct diagnosis and evaluate new potential treatments and outcomes. SUMMARY: Patient-derived renal progenitors can be isolated from urine to set up proper disease modeling. This strategy allows to make diagnosis of genetic kidney disease in patients carrying unknown significance variants or uncover variants missed from peripheral blood analysis. Furthermore, urinary-derived tubuloids obtained from renal progenitors of patients appear to be potentially valuable for modeling kidney diseases to test ex vivo treatment efficacy or to develop new therapeutic approaches. Finally, renal progenitors derived from urine can provide insights into acute kidney injury and predict kidney function recovery and outcome. KEY MESSAGES: Renal progenitors derived from urine are a promising new noninvasive and easy-to-handle tool, which improves the rate of diagnosis and the therapeutic choice, paving the way toward a personalized healthcare.

Genomics in nephrology: identifying informatics opportunities to improve diagnosis of genetic kidney disorders using a human-centered design approach.

BACKGROUND: Genomic kidney conditions often have a long lag between onset of symptoms and diagnosis. To design a real time genetic diagnosis process that meets the needs of nephrologists, we need to understand the current state, barriers, and facilitators nephrologists and other clinicians who treat kidney conditions experience, and identify areas of opportunity for improvement and innovation. METHODS: Qualitative in-depth interviews were conducted with nephrologists and internists from 7 health systems. Rapid analysis identified themes in the interviews. These were used to develop service blueprints and process maps depicting the current state of genetic diagnosis of kidney disease. RESULTS: Themes from the interviews included the importance of trustworthy resources, guidance on how to order tests, and clarity on what to do with results. Barriers included lack of knowledge, lack of access, and complexity surrounding the case and disease. Facilitators included good user experience, straightforward diagnoses, and support from colleagues. DISCUSSION: The current state of diagnosis of kidney diseases with genetic etiology is suboptimal, with information gaps, complexity of genetic testing processes, and heterogeneity of disease impeding efficiency and leading to poor outcomes. This study highlights opportunities for improvement and innovation to address these barriers and empower nephrologists and other clinicians who treat kidney conditions to access and use real time genetic information.

A Systematic Literature Review on the Use of Dried Biofluid Microsampling in Patients With Kidney Disease.

BACKGROUND: Kidney disease is fairly unique due to the lack of symptoms associated with disease activity, and it is therefore dependent on biological monitoring. Dried biofluids, particularly dried capillary blood spots, are an accessible, easy-to-use technology that have seen increased utility in basic science research over the past decade. However, their use is yet to reach the kidney patient population clinically or in large-scale discovery science initiatives. The aim of this study was to systematically evaluate the existing literature surrounding the use of dried biofluids in kidney research. METHODS: A systematic literature review was conducted using three search engines and a predefined search term strategy. Results were summarised according to the collection method, type of biofluid, application to kidney disease, cost, sample stability and patient acceptability. RESULTS: In total, 404 studies were identified and 67 were eligible. In total, 34,739 patients were recruited to these studies with a skew towards male participants (> 73%). The majority of samples were blood, which was used either for monitoring anti-rejection immunosuppressive drug concentrations or for kidney function. Dried biofluids offered significant cost savings to the patient and healthcare service. The majority of patients preferred home microsampling when compared to conventional monitoring. CONCLUSION: There is an unmet need in bringing dried microsampling technology to advance kidney disease despite its advantages. This technology provides an opportunity to upscale patient recruitment and longitudinal sampling, enhance vein preservation and overcome participation bias in research.

Symptom Science in Kidney Disease.

Physical and emotional symptoms are highly prevalent among patients with kidney disease and are directly linked to impaired health-related quality of life. Symptom science is a field of research aimed at advancing knowledge of the holistic mechanisms driving symptoms, how best to assess symptoms accurately, and developing novel and patient-centered approaches to symptom management. Patients with kidney disease have identified symptom science as a top research priority, and opportunities abound for ongoing patient engagement in symptom-related research efforts and clinical care. This review describes the burden of symptoms experienced by patients with kidney disease, explores the spectrum of patient engagement in symptom care and research, and discusses approaches for symptom assessment and management, taking into consideration the multitude of factors that may contribute to symptoms.

Mind the gap in kidney care: translating what we know into what we do.

Historically, it takes an average of 17 years to move new treatments from clinical evidence to daily practice. Given the highly effective treatments now available to prevent or delay kidney disease onset and progression, this is far too long. The time is now to narrow the gap between what we know and what we do. Clear guidelines exist for the prevention and management of common risk factors for kidney disease, such as hypertension and diabetes, but only a fraction of people with these conditions worldwide are diagnosed, and even fewer are treated to target. Similarly, the vast majority of people living with kidney disease are unaware of their condition, because in the early stages it is often silent. Even among patients who have been diagnosed, many do not receive appropriate treatment for kidney disease. Considering the serious consequences of kidney disease progression, kidney failure, or death, it is imperative that treatments are initiated early and appropriately. Opportunities to diagnose and treat kidney disease early must be maximized beginning at the primary care level. Many systematic barriers exist, ranging from patient to clinician to health systems to societal factors. To preserve and improve kidney health for everyone everywhere, each of these barriers must be acknowledged so that sustainable solutions are developed and implemented without further delay.

Deep learning applications for kidney histology analysis.

PURPOSE OF REVIEW: Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives. RECENT FINDINGS: Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios. SUMMARY: Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

Selective Partitioned Regression for Accurate Kidney Health Monitoring.

The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.