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American Journal of Kidney Diseases

Use of Histologic Parameters to Predict Glomerular Disease Progression: Findings From the China Kidney Biopsy Cohort Study

Published:October 14, 2022DOI:https://doi.org/10.1053/j.ajkd.2022.08.021

      Abstract

      Rationale & Objective

      Challenges in achieving valid risk prediction and stratification impede treatment decisions and clinical research design among patients with glomerular diseases. This study evaluated whether chronic histologic changes, when complementing other clinical data, improved the prediction of disease outcomes across a diverse group of glomerular diseases.

      Study Design

      Multicenter retrospective cohort study.

      Setting & Participants

      4,982 patients with biopsy-proven glomerular disease who underwent native biopsy at eight tertiary care hospitals across China from 2004 to 2020.

      New Predictors & Established Predictors

      Chronicity scores (CS) depicted as 4 categories of histological chronic change, as well as baseline clinical and demographic variables.

      Outcome

      Progression of glomerular disease defined as a composite of kidney failure or a ≥40% decline in eGFR from the value at the time of biopsy.

      Analytical Approach

      Multivariable Cox proportional hazard models. The performance of predictive models was evaluated by C statistic, time-dependent AUC, net reclassification index, integrated discrimination index, and calibration plots.

      Results

      The derivation and validation cohorts included 3,488 and 1,494 patients, respectively. Over a median of 31 months of follow-up, a total of 444 (8.9%) patients had disease progression in two cohorts. For predicting the 2-year risk of disease progression, the AUC of the model combining CS and the Kidney Failure Risk Equation (KFRE) in the validation cohort was 0.76 (95% CI, 0.65-0.87), significantly better than the KFRE model (AUC, 0.68; 95% CI, 0.56-0.79; P=0.04). The combined model also had a better fit with lower AIC and a significant improvement in reclassification as assessed by the IDI and NRI. Similar improvements in predictive performance were observed in subgroups and sensitivity analyses.

      Limitations

      Selection bias, relatively short follow-up, lack of external validation.

      Conclusions

      Adding histologic chronicity scores to the KFRE model improved the prediction of kidney disease progression at the time of kidney biopsy in patients with glomerular diseases.

      Graphical abstract

      Index words

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