Abstract: FR-PO0634
Validation of the Klinrisk Machine Learning Model in Patients with FSGS
Session Information
- Glomerular Diseases: Clinical, Outcomes, and Therapeutics Research - ANCA/FSGS
October 23, 2026 | Location: Exhibit Hall A, 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
- Ferguson, Thomas W., University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada
- Tangri, Navdeep, University of Manitoba Max Rady College of Medicine, Winnipeg, Manitoba, Canada
Background
Focal segmental glomerulosclerosis (FSGS) is a pattern of glomerular injury with varied causes that collectively represents an important cause of kidney failure. Etiologic diversity makes baseline risk prediction a challenge. The Klinrisk prediction model represents a potential tool for risk stratification at the time of diagnosis using routinely collected laboratory data. We sought to validate the Klinrisk model in a population-based cohort of adults with FSGS in the Canadian province of Manitoba and in the North American CureGN cohort.
Methods
The Manitoba cohort was identified from the Manitoba Glomerular Diseases Registry, and included patients diagnosed with FSGS from 2006 to 2021, followed through 2023. The CureGN cohort comprised all eligible adult patients with FSGS in the Cure Glomerulonephropathy consortium. Inclusion criteria were age 18 or older, biopsy-proven FSGS, with an available basic metabolic panel and urine ACR or PCR within 90 days of biopsy. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Calibration was assessed using Brier score, and calibration plots to compare predicted with observed risk.
Results
A total of 190 patients from Manitoba and 170 patients from CureGN with biopsy-proven FSGS were included, with 78 and 67 patients experiencing events in Manitoba and CureGN, respectively. Mean age at diagnosis was 51.8 years in Manitoba and 47.1 years in CureGN. Mean eGFR was 56.2 and 65.6 mL/min/1.73m2 in Manitoba and CureGN, respectively, while median ACR was 222 and 201 mg/mmol in Manitoba and CureGN. Discrimination was good in both cohorts, with an AUC of 0.76 (95% CI 0.68-0.83) at 2 years and 0.75 (95% CI 0.68-0.83) at 5 years in Manitoba, and 0.80 (95% CI 0.73-0.87) at 2 years and 0.75 (95% CI 0.66-0.84) at 5 years in CureGN. Brier scores were 0.158 and 0.196 at 2 and 5 years in Manitoba, and 0.154 and 0.206 at 2 and 5 years in CureGN.
Conclusion
In two independent cohorts of patients with FSGS, the Klinrisk model accurately predicted CKD progression from routinely collected labs alone, without requiring histologic scoring. These findings support its use for risk stratification at any time during the patient’s disease course and for identifying patients who may benefit from more intensive therapy.
Acknowledgment
Funding for the CureGN consortium is provided by U24DK100845, U01DK100846, U01DK100876, U01DK100866, and U01DK100867 from the NIDDK. Patient Recruitment is supported by NephCure.
Funding
- Commercial Support – Klinrisk Inc.