External Validation of the International IgA Nephropathy Prediction Tool.
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ABSTRACT: Background and objectives
The International IgA Nephropathy Network recently developed and externally validated two models to predict the risk of progression of IgA nephropathy: full models without and with race. This study sought to externally validate the International IgA Nephropathy Prediction Tool in a large, independent, and contemporary cohort in China.Design, setting, participants, & measurements
We included 1373 patients with biopsy-confirmed primary IgA nephropathy from The First Affiliated Hospital of Zhengzhou University from January 2012 to May 2018 and calculated predicted risks for each patient. The outcomes of interest were a 50% decline in eGFR or kidney failure. We assessed the performance of both models using discrimination (concordance statistics and Kaplan-Meier curves between subgroups), calibration (calibration plots), reclassification (net reclassification improvement and integrated discrimination improvement), and clinical utility (decision curve analysis).Results
The median follow-up was 29 months (interquartile range, 21-43 months; range, 1-95 months), and 186 (14%) patients reached the kidney outcomes of interest. Both models showed excellent discrimination (concordance statistics >0.85 and well separated survival curves). Overall, the full model without race generally underestimated the risk of primary outcome, whereas the full model with race was well calibrated for predicting 5-year risk. Compared with the full model without race, the full model with race had significant improvement in reclassification, as assessed by the net reclassification improvement (0.49; 95% confidence interval, 0.41 to 0.59) and integrated discrimination improvement (0.06; 95% confidence interval, 0.04 to 0.08). Decision curve analysis showed that both full models had a higher net benefit than default strategies, and the model with race performed better.Conclusions
In this study, both full models demonstrated remarkable discrimination, acceptable calibration, and satisfactory clinical utility. The relatively short follow-up time may have limited the validation of these models.
SUBMITTER: Zhang J
PROVIDER: S-EPMC7409753 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionTwo prediction models for IgA nephropathy (IgAN) using clinical variables and the Oxford MEST scores were developed and validated in 2 multiethnic cohorts. Additional external validation is required.MethodsBiopsy-proven Chinese and Argentinian patients with IgAN were included. The primary outcome was defined as a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease. C-statistics and stratified analyses were used for model discrimination, coefficient of determination (R2 D) for model fit, and calibration plots for model calibration. Baseline survival function was also evaluated.ResultsA total of 1275 patients were enrolled, with a mean age of 34 (interquartile range: 27-42) years, 50% of whom (638 of 1275) were men. Use of renin-angiotensin system blockers was higher than in previously reported cohorts, whereas other variables were comparable. The C-statistic of the models was 0.81, and R2 D was higher than reported. Survival curves in the subgroups (<16th, ∼16th to <50th, ∼50th to <84th, and ≥84th percentiles of linear predictor) were well separated. Most of the predictor variables, including hazard ratio, predicted 5-year risk, and eGFR decline slope, were worse with risk increasing. The baseline survival function was comparable in our cohort and the reported cohorts. The calibration was acceptable for the full model without race. However, the risk probability over 3 years was overestimated in the full model with race included.ConclusionThe prediction models showed good performance on personalized risk assessment, which may be used as drug-specific, precision-medicine approaches to treatment decisionmaking.
| S-EPMC7572322 | biostudies-literature
Project description: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 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 R2D 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 R2D (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 (ΔC, 0.04; 95% CI, 0.03-0.04 and ΔC, 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 R2D (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.
| S-EPMC6583088 | biostudies-literature
Project description:BackgroundRecently, a new international risk prediction model including the Oxford classification was published which was validated in a large multi-ethnic cohort. Therefore, we aimed to validate this risk prediction model in Korean patients with IgA nephropathy.MethodsThis retrospective cohort study was conducted with 545 patients who diagnosed IgA nephropathy with renal biopsy in three medical centers. The primary outcome was defined as a reduction in estimated glomerular filtration rate (eGFR) of >50% or incident end-stage renal disease (ESRD). Continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) were used to validate models.ResultsDuring the median 3.6 years of follow-up period, 53 (9.7%) renal events occurred. In multivariable Cox regression model, M1 (hazard ratio [HR], 2.22; 95% confidence interval [CI], 1.02-4.82; p = .043), T1 (HR, 2.98; 95% CI, 1.39-6.39; p = .005) and T2 (HR, 4.80; 95% CI, 2.06-11.18; p < .001) lesions were associated with increased risk of renal outcome. When applied the international prediction model, the area under curve (AUC) for 5-year risk of renal outcome was 0.69, which was lower than previous validation and internally derived models. Moreover, cNRI and IDI analyses showed that discrimination and reclassification performance of the international model was inferior to the internally derived models.ConclusionThe international risk prediction model for IgA nephropathy showed not as good performance in Korean patients as previous validation in other ethnic group. Further validation of risk prediction model is needed for Korean patients with IgA nephropathy.
| S-EPMC8248408 | biostudies-literature
Project description:BackgroundAlthough IgA nephropathy (IgAN), an immune-mediated disease with heterogeneous clinical and pathological phenotypes, is the most common glomerulonephritis worldwide, it remains unclear which IgAN patients benefit from immunosuppression (IS) therapy.MethodsClinical and pathological data from 4047 biopsy-proven IgAN patients from 24 renal centres in China were included. The derivation and validation cohorts were composed of 2058 and 1989 patients, respectively. Model-based recursive partitioning, a machine learning approach, was performed to partition patients in the derivation cohort into subgroups with different IS long-term benefits, associated with time to end-stage kidney disease, measured by adjusted Kaplan-Meier estimator and adjusted hazard ratio (HR) using Cox regression.FindingsThree identified subgroups obtained a significant IS benefits with HRs ≤ 1. In patients with serum creatinine ≤ 1·437 mg/dl, the benefits of IS were observed in those with proteinuria > 1·525 g/24h (node 6; HR = 0·50; 95% CI, 0·29 to 0·89; P = 0·02), especially in those with proteinuria > 2·480 g/24h (node 8; HR = 0·23; 95% CI, 0·11 to 0·50; P <0·001). In patients with serum creatinine > 1·437 mg/dl, those with high proteinuria and crescents benefitted from IS (node 12; HR = 0·29; 95% CI, 0·09 to 0·94; P = 0·04). The treatment benefits were externally validated in the validation cohort.InterpretationMachine learning could be employed to identify subgroups with different IS benefits. These efforts promote decision-making, assist targeted clinical trial design, and shed light on individualised treatment in IgAN patients.FundingNational Key Research and Development Program of China (2016YFC0904103), National Key Technology R&D Program (2015BAI12B02).
| S-EPMC7016365 | biostudies-literature
Project description:We aimed to validate three IgAN risk models proposed by an international collaborative study and another CKD risk model generated by an extended CKD cohort with our multicenter Chinese IgAN cohort. Biopsy-proven IgAN patients with an eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. The primary outcomes were a composite outcome (50% decline in eGFR or ESRD) and ESRD. The performance of those models was assessed using discrimination, calibration, and reclassification. A total of 2,300 eligible cases were enrolled. Of them, 288 (12.5%) patients reached composite outcome and 214 (9.3%) patients reached ESRD during a median follow-up period of 30 months. Using the composite outcome for analysis, the Clinical, Limited, Full, and CKD models had relatively good performance with similar C statistics (0.81, 0.81, 0.82, and 0.82, respectively). While using ESRD as the end point, the four prediction models had better performance (all C statistics > 0.9). Furthermore, subgroup analysis showed that the models containing clinical and pathological variables (Full model and Limited model) had better discriminatory abilities than the models including only clinical indicators (Clinical model and CKD model) in low-risk patients characterized by higher baseline eGFR (≥60 ml/min/1.73 m2). In conclusion, we validated recently reported IgAN and CKD risk models in our Chinese IgAN cohort. Compared to pure clinical models, adding pathological variables will increase performance in predicting ESRD in low-risk IgAN patients with baseline eGFR ≥60 ml/min/1.73 m2.
| S-EPMC8554097 | biostudies-literature
Project description:Background and objectivesThe risk assessment for developing ESRD remains limited in patients with IgA nephropathy (IgAN). The aim of this study was to develop and validate a prediction rule for estimating the individual risk of ESRD in patients with IgAN.Design, setting, participants, & measurementsA total of 698 patients with IgAN diagnosed by renal biopsy at Kyushu University Hospital (derivation cohort) between 1982 and 2010 were retrospectively followed. The Oxford classification was used to evaluate the pathologic lesions. The risk factors for developing ESRD were evaluated using a Cox proportional hazard model with a stepwise backward elimination method. The prediction rule was verified using data from 702 patients diagnosed at Japanese Red Cross Fukuoka Hospital (validation cohort) between 1979 and 2002.ResultsIn the derivation cohort, 73 patients developed ESRD during the median 4.7-year follow-up. The final prediction model included proteinuria (hazard ratio [HR], 1.30; 95% confidence interval [95% CI], 1.16 to 1.45, every 1 g/24 hours), estimated GFR (HR, 0.84; 95% CI, 0.74 to 0.96, every 10 ml/min per 1.73 m(2)), mesangial proliferation (HR, 1.85; 95% CI, 1.10 to 3.11), segmental sclerosis (HR, 3.21; 95% CI, 1.37 to 7.51), and interstitial fibrosis/tubular atrophy (T1: HR, 5.30; 95% CI, 2.63 to 10.7; T2: HR, 20.5; 95% CI, 9.05 to 46.5) as independent risk factors for developing ESRD. To create a prediction rule, the score for each variable was weighted by the regression coefficients calculated using the relevant Cox model. The incidence of ESRD increased linearly with increases in the total risk scores (P for trend <0.001). Furthermore, the prediction rule demonstrated good discrimination (c-statistic=0.89) and calibration (Hosmer-Lemeshow test, P=0.78) in the validation cohort.ConclusionsThis study developed and validated a new prediction rule using clinical measures and the Oxford classification for developing ESRD in patients with IgAN.
| S-EPMC3848397 | biostudies-literature
Project description:BackgroundThree tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).MethodsWe analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.ResultsThe AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.ConclusionsCurrent CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.
| S-EPMC8380991 | biostudies-literature
Project description:IgA nephropathy (IgAN) is a leading cause of CKD and renal failure. Recent international collaborative efforts have led to important discoveries that have improved our understanding of some of the key steps involved in the immunopathogenesis of IgAN. Furthermore, establishment of multicenter networks has contributed to rigorous design and execution of clinical trials that have provided important insights regarding immunotherapy in IgAN. In this article, we review emerging developments in clinical and translational IgAN research and describe how these novel findings will influence future strategies to improve the outcome of patients with IgAN.
| S-EPMC5383386 | biostudies-literature
Project description:ObjectivesTo identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes.DesignSystematic review and external validation.Data sourcesPubMed and Embase.Eligibility criteriaStudies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes.MethodsScreening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic).Results41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons.ConclusionsThis study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting.Systematic review registrationPROSPERO CRD42020192831.
| S-EPMC8477272 | biostudies-literature
Project description:Background. MicroRNAs (miRNAs) have been found in virtually all body fluids and used successfully as biomarkers for various diseases. Evidence indicates that miRNAs have important roles in IgA nephropathy (IgAN), a major cause of renal failure. In this study, we looked for differentially expressed miRNAs in IgAN and further evaluated the correlations between candidate miRNAs and the severity of IgAN. Methods. Microarray and RT-qRCR (real-time quantitative polymerase chain reaction) were sequentially used to screen and further verify miRNA expression profiles in urinary sediments of IgAN patients in two independent cohorts. The screening cohort consisted of 32 urine samples from 18 patients with IgAN, 4 patients with MN (membranous nephropathy), 4 patients with MCD (minimal changes disease) and 6 healthy subjects; the validation cohort consisted of 102 IgAN patients, 41 MN patients, 27 MCD patients and 34 healthy subjects. The renal pathological lesions of patients with IgAN were evaluated according to Lee's grading system and Oxford classification. Results. At the screening phase, significance analysis of microarrays analysis showed that no miRNA was differentially expressed in the IgAN group compared to all control groups. But IgAN grade I-II and III subgroups (according to Lee's grading system) shared dysregulation of two miRNAs (miR-3613-3p and miR-4668-5p). At the validation phase, RT-qPCR results showed that urinary level of miR-3613-3p was significantly lower in IgAN than that in MN, MCD and healthy controls (0.47, 0.44 and 0.24 folds, respectively, all P < 0.01 by Mann-Whitney U test); urinary level of miR-4668-5p was also significantly lower in IgAN than that in healthy controls (0.49 fold, P < 0.01). Significant correlations were found between urinary levels of miR-3613-3p with 24-hour urinary protein excretion (Spearman r = 0.50, P = 0.034), eGFR (estimated glomerular filtration rate) (r = - 0.48, P = 0.043) and Lee's grades (r = 0.57, P = 0.014). Similarly, miR-4668-5p was significantly correlated with eGFR (r = - 0.50, P = 0.034) and Lee's grades (r = 0.57, P = 0.013). For segmental glomerulosclerosis according to Oxford classification, patients scored as S0 had significantly lower levels of urinary miR-3613-3p and miR-4668-5p than those scored as S1 (0.41 and 0.43 folds, respectively, all P < 0.05). Conclusions. The expression profile of miRNAs was significantly altered in urinary sediments from patients with IgAN. Urinary expression of miR-3613-3p was down-regulated in patients with IgAN. Moreover, urinary levels of both miR-3613-3p and miR-4668-5p were correlated with disease severity. Further studies are needed to explore the roles of miR-3613-3p and miR-4668-5p in the pathogenesis and progression of IgA nephropathy.
| S-EPMC4458130 | biostudies-literature