Introduction: In 2020, a working group of 13 renal pathologists published consensus definitions for 47 individual glomerular lesions found on light microscopy (LM) and 47 glomerular lesions and 9... Show moreIntroduction: In 2020, a working group of 13 renal pathologists published consensus definitions for 47 individual glomerular lesions found on light microscopy (LM) and 47 glomerular lesions and 9 normal structures found on electron microscopy (EM).Methods: To test the impact of these definitions on identification of these lesions and structures, 2 surveys were circulated to all members of the Renal Pathology Society (RPS), each having 32 images (19 LM, 13 EM) and accompanying questions with 5 multiple-choice answers, one being the consensus choice of the working group. The first survey (survey 1 [S1]), answered by 297 RPS members, was sent in September 2020, before publication of the consensus definitions. The second (survey 2 (S2]), with images of the same lesions and structures (but not the same images) and the same questions and multiple choices in different order, was sent in April 2020, 5 months after the publication of the definitions.Results: S2 was taken by 181 RPS members; 64% also took 51 and 61% reported having read the definitions paper (def. paper). Mean agreement with the consensus answers increased modestly between the 2 surveys (65.2% vs. 72.0%, P = 0.097); the increase was greater and significant when only respondents to S2 who read the def. paper were considered (65.2% vs. 74.8%, P = 0.026). Furthermore, in S2 agreement with consensus answers was greater among respondents who read this paper versus those who did not (66.9% vs. 74.8%, P < 0.0001).Conclusions: Publication of the consensus definitions modestly improved interobserver agreement in identification of glomerular lesions. Show less
Over the past 2 decades, scoring systems for multiple glomerular diseases have emerged, as have consortia of pathologists and nephrologists for the study of glomerular diseases, including... Show moreOver the past 2 decades, scoring systems for multiple glomerular diseases have emerged, as have consortia of pathologists and nephrologists for the study of glomerular diseases, including correlation of pathologic findings with clinical features and outcomes. However, one important limitation faced by members of these consortia and other renal pathologists and nephrologists in both investigative work and routine practice remains a lack of uniformity and precision in clearly defining the morphologic lesions on which the scoring systems are based. In response to this issue, the Renal Pathology Society organized a working group to identify the most frequently identified glomerular lesions observed by light microscopy and electron microscopy, review the literature to capture the published definitions most often used for each, and determine consensus terms and definitions for each lesion in a series of online and in-person meetings. The defined lesions or abnormal findings are not specific for any individual disease or subset of diseases, but rather can be applied across the full spectrum of glomerular diseases and within the context of the different scoring systems used for evaluating and reporting these diseases. In addition to facilitating glomerular disease research, standardized terms and definitions should help harmonize reporting of medical kidney diseases worldwide and lead to more-precise diagnoses and improved patient care. Show less
Barbour, S.J.; Coppo, R.; Zhang, H.; Liu, Z.H.; Suzuki, Y.; Matsuzaki, K.; ... ; Int IgA Nephropathy Network 2019
ImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk... Show moreImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk stratification and treatment decisions, clinical trial recruitment, and biomarker validation. ObjectiveTo derive and externally validate a prediction model for disease progression in IgAN that can be applied at the time of kidney biopsy in multiple ethnic groups worldwide. Design, Setting, and ParticipantsWe derived and externally validated a prediction model using clinical and histologic risk factors that are readily available in clinical practice. Large, multi-ethnic cohorts of adults with biopsy-proven IgAN were included from Europe, North America, China, and Japan. Main Outcomes and MeasuresCox proportional hazards models were used to analyze the risk of a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, and were evaluated using the R-D(2) measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots. ResultsThe study included 3927 patients; mean age, 35.4 (interquartile range, 28.0-45.4) years; and 2173 (55.3%) were men. The following prediction models were created in a derivation cohort of 2781 patients: a clinical model that included eGFR, blood pressure, and proteinuria at biopsy; and 2 full models that also contained the MEST histologic score, age, medication use, and either racial/ethnic characteristics (white, Japanese, or Chinese) or no racial/ethnic characteristics, to allow application in other ethnic groups. Compared with the clinical model, the full models with and without race/ethnicity had better R-D(2) (26.3% and 25.3%, respectively, vs 20.3%) and AIC (6338 and 6379, respectively, vs 6485), significant increases in C statistic from 0.78 to 0.82 and 0.81, respectively (Delta C, 0.04; 95% CI, 0.03-0.04 and Delta C, 0.03; 95% CI, 0.02-0.03, respectively), and significant improvement in reclassification as assessed by the NRI (0.18; 95% CI, 0.07-0.29 and 0.51; 95% CI, 0.39-0.62, respectively) and IDI (0.07; 95% CI, 0.06-0.08 and 0.06; 95% CI, 0.05-0.06, respectively). External validation was performed in a cohort of 1146 patients. For both full models, the C statistics (0.82; 95% CI, 0.81-0.83 with race/ethnicity; 0.81; 95% CI, 0.80-0.82 without race/ethnicity) and R-D(2) (both 35.3%) were similar or better than in the validation cohort, with excellent calibration. Conclusions and RelevanceIn this study, the 2 full prediction models were shown to be accurate and validated methods for predicting disease progression and patient risk stratification in IgAN in multi-ethnic cohorts, with additional applications to clinical trial design and biomarker research. Show less
