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Published on: June 18, 2020
Use of Histologic Parameters to Predict Glomerular Disease Progression: Findings From the China Kidney Biopsy Cohort
Xiaodong Zhang1, Fan Luo1, Ruixuan Chen1
1National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University.
Insights
Adding histologic chronicity scores to the kidney failure risk equation significantly improved the prediction of kidney disease progression in patients with glomerular diseases. This enhances risk stratification for better treatment decisions and clinical research design.
Area of Science:
- Nephrology
- Pathology
- Biostatistics
Background:
- Accurate risk prediction and stratification are crucial for managing glomerular diseases but remain challenging.
- Histologic findings, particularly chronic changes, are key components of kidney biopsy evaluations.
Purpose of the Study:
- To evaluate if incorporating histologic chronicity scores into existing clinical data improves the prediction of disease outcomes in patients with glomerular diseases.
- To assess the utility of chronicity scores in enhancing risk stratification for better clinical decision-making and research design.
Main Methods:
- A multicenter retrospective cohort study involving 4,982 patients with biopsy-proven glomerular disease.
- Utilized multivariable Cox proportional hazard models and evaluated predictive performance using AUROC, net reclassification index, and integrated discrimination index.
- Compared a model combining chronicity scores with the Kidney Failure Risk Equation (KFRE) against the KFRE model alone.
Main Results:
- The combined model incorporating chronicity scores and KFRE demonstrated a significantly improved area under the receiver operating characteristic curve (AUROC) of 0.76 compared to KFRE alone (0.68) for predicting 2-year disease progression (P=0.04).
- The combined model showed better model fit and significant improvements in reclassification metrics, including integrated discrimination improvements and net reclassification improvements.
- Similar performance enhancements were observed in subgroup and sensitivity analyses, reinforcing the robustness of the findings.
Conclusions:
- Histologic chronicity scores significantly enhance the predictive accuracy of the Kidney Failure Risk Equation for kidney disease progression in patients with glomerular diseases.
- Integrating chronicity scores into risk prediction models offers a valuable tool for improving patient management, treatment decisions, and clinical trial design.
Rationale & Objective:
Challenges in achieving valid risk prediction and stratification impede treatment decisions and clinical research design for 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 8 tertiary care hospitals across China in 2004-2020.
New Predictors & Established Predictors:
Chronicity scores 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% decrease in estimated glomerular filtration rate from the measurement at the time of biopsy.
Analytical Approach:
Multivariable Cox proportional hazard models. The performance of predictive models was evaluated by C statistic, time-dependent area under the receiver operating characteristic curve (AUROC), net reclassification index, integrated discrimination index, and calibration plots.
Results:
The derivation and validation cohorts included 3,488 and 1,494 patients, respectively. During a median of 31 months of follow-up, a total of 444 (8.9%) patients had disease progression in the 2 cohorts. For prediction of the 2-year risk of disease progression, the AUROC of the model combining chronicity score and the Kidney Failure Risk Equation (KFRE) in the validation cohort was 0.76 (95% CI, 0.65-0.87); in comparison with the KFRE model (AUROC, 0.68 [95% CI, 0.56-0.79]), the combined model was significantly better (P = 0.04). The combined model also had a better fit, with a lower Akaike information criterion and a significant improvement in reclassification as assessed by the integrated discrimination improvements and net reclassification improvements. Similar improvements in predictive performance were observed in subgroup 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.
Plain-Language Summary:
Risk prediction and stratification remain big challenges for treatment decisions and clinical research design for patients with glomerular diseases. The extent of chronic changes is an important component of kidney biopsy evaluations in glomerular disease. In this large multicenter cohort including 4,982 Chinese adults undergoing native kidney biopsy, we evaluated whether histologic chronicity scores, when added to clinical data, could improve the prediction of disease prognosis for a diverse set of glomerular diseases. We observed that adding histologic chronicity scores to the kidney failure risk equation improved the prediction of kidney disease progression at the time of kidney biopsy in patients with glomerular diseases.
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