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      The Improved Kidney Risk Score in ANCA-Associated Vasculitis for Clinical Practice and Trials

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 15 , 17 , 18 , 19 , 20 , 15 , 21 , 16 , 13 , 22 , 3 , 4 , 20 , 19 , 17 , 1 , 2 , 23 , 24 , 9 , 25 , 5 , 6 , 5 , 6 , 26 , 27 , 3 , 4 , 18 , 19 , 14 , 9 , 25 , 11 , 12 , 13 , 28 , 1 , 21 , 29
      Journal of the American Society of Nephrology

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          Abstract

          Significance Statement

          Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. More than 1500 patients were collated in an international longitudinal study to revise the ANCA kidney risk score. The score showed satisfactory performance, mimicking the original study (Harrell's C=0.779). In the development cohort of 959 patients, no additional parameters aiding the tool were detected, but replacing the GFR with creatinine identified an additional cutoff. The parameter interstitial fibrosis and tubular atrophy was modified to allow wider access, risk points were reweighted, and a fourth risk group was created, improving predictive ability (C=0.831). In the validation, the new model performed similarly well with excellent calibration and discrimination ( n=480, C=0.821). The revised score optimizes prognostication for clinical practice and trials.

          Background

          Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. A retrospective international longitudinal cohort was collated to revise the ANCA renal risk score.

          Methods

          The primary end point was ESKD with patients censored at last follow-up. Cox proportional hazards were used to reweight risk factors. Kaplan–Meier curves, Harrell's C statistic, receiver operating characteristics, and calibration plots were used to assess model performance.

          Results

          Of 1591 patients, 1439 were included in the final analyses, 2:1 randomly allocated per center to development and validation cohorts (52% male, median age 64 years). In the development cohort ( n=959), the ANCA renal risk score was validated and calibrated, and parameters were reinvestigated modifying interstitial fibrosis and tubular atrophy allowing semiquantitative reporting. An additional cutoff for kidney function (K) was identified, and serum creatinine replaced GFR (K0: <250 µmol/L=0, K1: 250–450 µmol/L=4, K2: >450 µmol/L=11 points). The risk points for the percentage of normal glomeruli (N) and interstitial fibrosis and tubular atrophy (T) were reweighted (N0: >25%=0, N1: 10%–25%=4, N2: <10%=7, T0: none/mild or <25%=0, T1: ≥ mild-moderate or ≥25%=3 points), and four risk groups created: low (0–4 points), moderate (5–11), high (12–18), and very high (21). Discrimination was C=0.831, and the 3-year kidney survival was 96%, 79%, 54%, and 19%, respectively. The revised score performed similarly well in the validation cohort with excellent calibration and discrimination ( n=480, C=0.821).

          Conclusions

          The updated score optimizes clinicopathologic prognostication for clinical practice and trials.

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          Most cited references59

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            A Proportional Hazards Model for the Subdistribution of a Competing Risk

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              Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

              The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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                Journal
                Journal of the American Society of Nephrology
                JASN
                1046-6673
                1533-3450
                2024
                March 2024
                December 12 2023
                : 35
                : 3
                : 335-346
                Affiliations
                [1 ]Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
                [2 ]Division of Population Health, Health Services Research, and Primary Care, Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
                [3 ]Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
                [4 ]School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
                [5 ]Department of Medicine, University of Cambridge, Cambridge, United Kingdom
                [6 ]Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
                [7 ]Division of Nephrology, Dialysis and Transplantation, University of Genova, Genova, Italy
                [8 ]Department of Internal Medicine and IRCCS Ospedale Policlinico San Martino, Genova, Italy
                [9 ]Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
                [10 ]Renal Unit, Northern Health, Victoria, Australia
                [11 ]1st Faculty of Medicine, Charles University, Prague, Czechia
                [12 ]Department of Nephrology, General University Hospital, Prague, Czechia
                [13 ]Trinity Kidney Centre, Trinity College Dublin, Dublin, Ireland
                [14 ]University/BHF Centre for Cardiovascular Science, University of Edinburgh and Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
                [15 ]Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
                [16 ]Renal Department, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
                [17 ]Renal Department, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
                [18 ]Service de Néphrologie-Dialyse-Transplantation, CHU d’Angers, Angers, France
                [19 ]Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
                [20 ]Division of Nephrology, Bursa Uludağ University School of Medicine, Bursa, Turkey
                [21 ]Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom
                [22 ]Department of Pathology, Institute for Clinical and Experimental Medicine, Prague, Czechia
                [23 ]Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
                [24 ]University Medical Center Hamburg-Eppendorf, Institute of Pathology, Hamburg, Germany
                [25 ]Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
                [26 ]Department of Pathology, Groningen University Medical Center, Groningen, The Netherlands
                [27 ]Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
                [28 ]Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
                [29 ]Division of Cell Matrix Biology and Regenerative Medicine, University of Manchester, Manchester, United Kingdom
                Article
                10.1681/ASN.0000000000000274
                38082490
                105883d4-ae67-43d6-87b9-e7f44ec3d9c3
                © 2023
                History

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