A Validation Study Comparing Risk Prediction Models of IgA Nephropathy
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ABSTRACT:
SUBMITTER: Ouyang Y
PROVIDER: S-EPMC8554097 | 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:BackgroundImmunoglobulin A nephropathy (IgAN) is among the commonest glomerulonephritides in Greece and an important cause of end-stage kidney disease (ESKD) with an insidious chronic course. Thus, the recently published International IgAN prediction tool could potentially provide valuable risk stratification and guide the appropriate treatment module. This study aimed to externally validate this prediction tool using a patient cohort from the IgAN registry of the Greek Society of Nephrology.MethodsWe validated the predictive performance of the two full models (with or without race) derived from the International IgAN Prediction Tool study in the Greek Society of Nephrology registry of patients with IgAN using external validation of survival prediction models (Royston and Altman). The discrimination and calibration of the models were tested using the C-statistics and stratified analysis, coefficient of determination ( RD2 ) for model fit, and the regression coefficient of the linear predictor (βPI), respectively.ResultsThe study included 264 patients with a median age of 39 (30-51) years where 65.2% are men. All patients were of Caucasian origin. The 5-year risk of the primary outcome (50% reduction in estimated glomerular filtration rate or ESKD) was 8%. The RD2 for the full models with and without race when applied to our cohort was 39 and 35%, respectively, and both were higher than the reported RD2 for the models applied to the original validation cohorts (26.3, 25.3, and 35.3%, respectively). Harrel's C statistic for the full model with race was 0.71, and for the model without race was 0.70. Renal survival curves in the subgroups (<16th, ~16 to <50th, ~50 to <84th, and >84th percentiles of linear predictor) showed adequate separation. However, the calibration proved not to be acceptable for both the models, and the risk probability was overestimated by the model.ConclusionsThe two full models with or without race were shown to accurately distinguish the highest and higher risk patients from patients with low and intermediate risk for disease progression in the Greek registry of IgAN.
| S-EPMC8885590 | biostudies-literature
Project description:Background and objectivesThe 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, & measurementsWe 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).ResultsThe 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.ConclusionsIn 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.
| S-EPMC7409753 | 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: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:In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called "hyper-parameters"). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the need to define notions of relevance in the difference between their performances. Regarding the latter, we borrow the idea of equivalence margins from clinical research and introduce new statistical tests. We conclude that most hyper-parameters can be learnt from the data by either minimizing REML or by using weakly-informative priors, with good predictive results. In particular, the default options in a popular software are generally competitive with the optimal values. With regard to the performance assessments themselves, we conclude that the paired k-fold cross validation is a generally applicable and statistically powerful methodology to assess differences in model accuracies. Coupled with the definition of equivalence margins based on expected genetic gain, it becomes a useful tool for breeders.
| S-EPMC8639521 | biostudies-literature
Project description:ObjectivesTo look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.DesignSystematic review.Data sourcesMedline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.Review methodsDatabases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).Results16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.ConclusionsMost predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.
| S-EPMC4784870 | 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:BackgroundA working group on the Oxford classification of IgA nephropathy (IgAN) recently reported that crescents detected in kidney tissue predicted a worse renal outcome. However, this finding must be validated in independent cohorts before it can be widely applied to clinical practice.MethodsBiopsy-proven IgAN patients were continuously recruited from two large renal centers in China from 1989 to 2014. All patients were followed for more than 1 year unless end stage renal disease (ESRD) occurred within 12 months. Crescents were defined as focal cellular or fibrocellular crescent formations. IgAN patients without detectable crescents were recruited to the C0 group. Patients with crescents in less than or more than 1/4 of all glomeruli were recruited to the C1 or C2 group, respectively. Primary outcome was defined as the time to ESRD, and the secondary outcome was defined as the time to an estimated glomerular filtration rate (eGFR) decline equal to or greater than 50% or to ESRD.ResultsIn total, 1152 IgAN patients were recruited in this study. Among all patients, 53.7% were in the C0 group, 38.8% were in the C1 group, and 7.5% were in the C2 group. Compared to patients in the C0 group, patients in the C1 or C2 group were younger, had more urinary protein excretion and lower eGFR, and presented with more severe mesangial hypercellularity, endocapillary proliferation or tubular atrophy/interstitial fibrosis. After 45 months of follow-up, ESRD had occurred in 80 (12.9%), 46 (10.3%) and 18 (20.9%) of patients in the C0, C1 and C2 groups, respectively. By multivariable Cox regression analysis, inclusion in the C1 (HR = 1.07, 95% CI 0.71-1.63), C2 (HR = 0.84, 95% CI 0.41-1.73), or C1 or C2 group (HR = 1.02, 95% CI 0.68-1.52) was not associated with a higher rate of ESRD than inclusion in the C0 group after adjusting for age, gender, eGFR, mean arterial pressure (MAP), MEST scores, and immunosuppressive treatment. However, in patients with nephrotic-range proteinuria, patients in either the C1 or C2 group had a higher rate of the primary outcome, ESRD (HR = 2.54, 95% CI 1.14-5.66) after adjusting for age, gender, eGFR, MAP, MEST scores, and immunosuppressive treatment. Similar results were found when we evaluated the association between crescents and the secondary outcome.ConclusionsIgAN patients with crescents had more severe clinical and pathological manifestations than those without crescents. However, we failed to replicate the association between crescents and renal function progression in Chinese IgAN patients followed for more than 1 year.
| S-EPMC5934890 | biostudies-literature
Project description:Background:IgA nephropathy (IgAN) is the commonest glomerulonephritis worldwide. Its prevalence is difficult to estimate, as people with mild disease do not commonly receive a biopsy diagnosis. We aimed to generate an IgA nephropathy genetic risk score (IgAN-GRS) and estimate the proportion of people with hematuria who had IgAN in the UK Biobank (UKBB). Methods:We calculated an IgAN-GRS using 14 single-nucleotide polymorphisms (SNPs) drawn from the largest European Genome-Wide Association Study (GWAS) and validated the IgAN-GRS in 464 biopsy-proven IgAN European cases from the UK Glomerulonephritis DNA Bank (UKGDB) and in 379,767 Europeans in the UKBB. We used the mean of IgAN-GRS to calculate the proportion of potential IgAN in 14,181 with hematuria and other nonspecific renal phenotypes from 379,767 Europeans in the UKBB. Results:The IgAN-GRS was higher in the IgAN cohort (4.30; 95% confidence interval [95% CI: 4.23-4.38) than in controls (3.98; 3.97-3.98; P < 0.0001). The mean GRS in UKBB participants with hematuria (n = 12,858) was higher (4.04; 4.02-4.06) than UKBB controls (3.98; 3.97-3.98; P < 0.0001) and higher in those with hematuria, hypertension, and microalbuminuria (n = 1323) (4.07; 4.02-4.13) versus (3.98; 3.97-3.98; P = 0.0003). Using the difference in these means, we estimated that IgAN accounted for 19% of noncancer hematuria and 28% with hematuria, hypertension, and microalbuminuria in UKBB. Conclusions:We used an IgAN-GRS to estimate the prevalence of IgAN contributing to common phenotypes that are not always biopsied. The noninvasive use of polygenic risk in this setting may have further utility to identify likely etiology of nonspecific renal phenotypes in large population cohorts.
| S-EPMC7572308 | biostudies-literature