Search

Background: C3 glomerulopathy is a rare and heterogeneous complement-driven disease. It is often challenging to accurately predict in clinical practice the individual kidney prognosis at baseline. We herein sought to develop and validate a prognostic nomogram to predict long-term kidney survival., Methods: We conducted a retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases. The dataset was randomly divided into a training group ( n = 87) and a validation group ( n = 28). The least absolute shrinkage and selection operator (LASSO) regression was used to screen the main predictors of kidney outcome and to build the nomogram. The accuracy of the nomogram was assessed by discrimination and risk calibration in the training and validation sets., Results: The study group comprised 115 patients, of whom 46 (40%) reached kidney failure in a median follow-up of 49 months (range 24-112). No significant differences were observed in baseline estimated glomerular filtration rate (eGFR), proteinuria or total chronicity score of kidney biopsies, between patients in the training versus those in the validation set. The selected variables by LASSO were eGFR, proteinuria and total chronicity score. Based on a Cox model, a nomogram was developed for the prediction of kidney survival at 1, 2, 5 and 10 years from diagnosis. The C-index of the nomogram was 0.860 (95% confidence interval 0.834-0.887) and calibration plots showed optimal agreement between predicted and observed outcomes., Conclusions: We constructed and validated a practical nomogram with good discrimination and calibration to predict the risk of kidney failure in C3 glomerulopathy patients at 1, 2, 5 and 10 years., (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)

Novel coronavirus disease infection (coronavirus disease 2019, COVID-19) was declared a global pandemic in March 2020 and since then has become a major public health problem. The prevalence of COVID-19 infection and acute kidney injury (AKI) is variable depending on several factors such as race/ethnicity and severity of illness. The pathophysiology of renal involvement in COVID-19 infection is not entirely clear, but it could be in part explained by the viral tropism in the kidney parenchyma. AKI in COVID-19 infection can be either by direct invasion of the virus or as a consequence of immunologic response. Diverse studies have focused on the effect of COVID-19 on glomerulonephritis (GN) patients or the 'novo' GN; however, the effect of COVID-19 in acute tubulointerstitial nephritis (ATIN) has been scarcely studied. In this article, we present five cases with different spectrums of COVID-19 infection and ATIN that may suggest that recent diagnosis of ATIN is accompanied by a worse clinical prognosis in comparison with long-term diagnosed ATIN., (© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.)

Background: Renal manifestations of monoclonal gammopathies are of increasing interest among nephrologists. Typical manifestations include light chain cast nephropathy, amyloidosis or renal damage mediated by monoclonal immunoglobulin deposition. Podocytopathies in the setting of an underlying monoclonal gammopathy constitute a rare manifestation of these diseases and, although being described in the literature, remain a challenge since most data derive from case reports., Methods: A retrospective review of the clinical data of Hospital del Mar and Hospital Vall d'Hebron was performed to identify patients with minimal change disease (MCD) or focal and segmental glomerulosclerosis (FSGS) in the setting of neoplasms that produce monoclonal (M) protein. Additionally, a literature review on this topic was performed. This study aims to describe the clinical characteristics and outcomes of these patients., Results: Three patients were identified to have podocytopathy and monoclonal gammopathy between the years 2013 and 2020. All three were males and  >65 years of age. Two patients were diagnosed with MCD and one patient was diagnosed with FSGS. All patients underwent a kidney biopsy and light and electron microscopic studies were performed. The underlying causes of monoclonal gammopathy were multiple myeloma in two cases and Waldeström macroglobulinemia in one case. Two patients developed nephrotic syndrome during the follow-up. All patients were under active hematological treatment. One patient presented a complete remission of proteinuria whereas the other two presented a partial remission., Conclusions: Podocytopathies may infrequently be found in patients with monoclonal gammopathies. Patients with overt glomerular proteinuria and hematological disorders with M protein should undergo a kidney biopsy for prompt diagnosis and to specify a prognosis. In addition, further study on this matter must be done to understand the pathophysiology and treat these patients appropriately., (© The Author(s) 2021. Published by Oxford University Press on behalf of the ERA.)

Background: Models developed to predict hospital-acquired acute kidney injury (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow the establishment of a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI., Methods: The study set was 36 852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21 545 non-critical patients admitted to the validation centre in the period from June 2017 to December 2018., Results: The incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios (ORs) were conferred by chronic kidney disease, urologic disease and liver disease. Among acute complications, the highest ORs were associated with acute respiratory failure, anaemia, systemic inflammatory response syndrome, circulatory shock and major surgery. The model showed an area under the curve (AUC) of 0.907 [95% confidence interval (CI) 0.902-0.908), a sensitivity of 82.7 (95% CI 80.7-84.6) and a specificity of 84.2 (95% CI 83.9-84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (χ 2 = 6.02, P = 0.64). In the validation set, the prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904-0.910), a sensitivity of 81.2 (95% CI 79.2-83.1) and a specificity of 82.5 (95% CI 82.2-83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (χ 2 = 4.2, P = 0.83). An online tool (predaki.amalfianalytics.com) is available to calculate the risk of AKI in other hospital environments., Conclusions: By using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI during the hospital admission period in non-critically ill patients., (© The Author(s) 2021. Published by Oxford University Press on behalf of ERA-EDTA.)