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December 8, 2025BloodOpen Access

Assessing replicability in chronic kidney disease severity scoring classifications using the sickle cell outcome grading system

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Authors

SLSophie Lanzkron

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Overview

Assessment shows a low scoring rate for chronic kidney disease in populations with sickle cell disease, suggesting improvements are needed for data availability.

Key Points

  • Utilization of SCOGS for chronic kidney disease proved feasible across two large sickle cell disease cohorts, but few patients could be scored.
  • In the SCCRIP cohort, 21.3% of patients met scoring criteria, with 49.2% classified as grade 1 kidney impairment.
  • Patients were evaluated using established severity scoring criteria, assessing eGFR alongside albuminuria levels for classification.
  • Machine learning algorithms may help expand the SCOGS application for patients with missing data points in chronic kidney disease assessments.

Cite This Study

Sophie Lanzkron (2025) studied this question.

synapsesocial.com/papers/69362f6e4fa91c937236e13chttps://doi.org/10.1182/blood-2025-4749
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning replicates and extends clinician-informed disease severity grading classification for acute pain and CKD in sickle cell disease2025
  2. 2Application and challenges of the sickle cell outcome grading system (SCOGS) for classifying acute chest syndrome severity2025
  3. 3KDIGO-Defined Kidney Dysfunction Predicts Long-Term Outcomes in a Multi-Center Cohort of Adults with Sickle Cell Disease2025 · 4 citations
  4. 4Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort2025
  5. 5Clinical and laboratory findings in adults with sickle cell disease which increase risk for severe acute chest syndrome2025