Kidney Week

Abstract: FR-PO876

Validation of the Klinrisk Kidney Disease Progression Model in Individuals with IgA Nephropathy

Session Information

• IgA Nephropathy: Clinical, Outcomes, and Therapeutics
  October 25, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Glomerular Diseases

• 1402 Glomerular Diseases: Clinical, Outcomes, and Therapeutics

Authors

• Barr, Bryce, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada

Bryce Barr, MD

• Bamforth, Ryan J., Seven Oaks General Hospital, Winnipeg, Manitoba, Canada

Ryan J. Bamforth, MS, MSc

• Ferguson, Thomas W., Seven Oaks General Hospital, Winnipeg, Manitoba, Canada

Thomas W. Ferguson, MS, MSc

• Harasemiw, Oksana, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada

Oksana Harasemiw, MSc

• Gibson, Ian W., University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada

Ian W. Gibson, MD, MBChB

• Tremblay-Savard, Olivier, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada

Olivier Tremblay-Savard, PhD

• Tangri, Navdeep, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada

Navdeep Tangri, MD, PhD

Background

IgA nephropathy is the most common primary glomerulonephritis worldwide, and leads to kidney failure in a significant proportion of those affected. Recent clinical trials have relied on persistence of proteinuria >1 g/day as the best predictor of poor long-term kidney outcomes, and other tools rely on detailed clinopathologic data which may not be readily available. We sought to validate the Klinrisk prediction model in a population-based cohort of all individuals aged 18 years and older with IgA nephropathy in the Canadian province of Manitoba from 2002 to 2019, inclusive.

Methods

Participants were identified from the Manitoba Glomerular Diseases Registry. Klinrisk is a machine learning model constructed using random forests that uses routinely collected laboratory data to predict the risk of a composite of 40% decline in eGFR or kidney failure, defined as receipt of dialysis for at least 3 months or kidney transplantation. Discrimination was assessed using area under the receiver operating characteristic curves (AUC). Calibration was assessed using Brier scores, and calibration plots to compare predicted with observed risk.

Results

A total of 230 individuals with biopsy-proven IgA nephropathy were included in the analysis. Median age at biopsy was 41.5 years, median eGFR was 51.5 mL/min/1.73m², and median ACR was 158 mg/mmol. At 2 and 5 years, 73 and 89 individuals reached the primary outcome, respectively. Model discrimination was good, with an AUC of 0.878 (95% CI 0.830, 0.925) at 2 years and 0.827 (95% CI 0.760, 0.894) at 5 years. Calibration was appropriate, with Brier scores of 0.142 and 0.167 at 2 and 5 years. Visual inspection of the calibration plots showed under-prediction at higher levels of observed risk.

Conclusion

The results demonstrate the utility of the Klinrisk model in predicting kidney failure among individuals with IgA nephropathy. Our results are limited by a small sample size and require confirmation in a separate cohort. If confirmed, Klinrisk could be implemented in a clinical setting to predict individual kidney failure risk to aid treatment decisions, or in a research setting to identify high-risk individuals for participation in clinical trials using routinely available data.