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December 8, 2025Blood

Machine learning replicates and extends clinician-informed disease severity grading classification for acute pain and CKD in sickle cell disease

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Authors

MZMinzhang ZhengJHJane S. Hankins

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Overview

Assessment using machine learning improves classification for acute pain and CKD in sickle cell disease, indicating broad applicability in clinical practice.

Key Points

  • Model predicts acute pain and chronic kidney disease grades with over 90% accuracy, demonstrating machine learning's effectiveness.
  • Random forest classifiers showed an AUC of 0.97 for acute pain and 0.98 for CKD classification, indicating strong performance.
  • Cross-validation employed to evaluate model reliability using clinical data from the Sickle Cell Clinical Research and Intervention Program.
  • Machine learning enhances grading coverage for CKD, expanding from 21% to over 90%, supporting structured phenotyping.

Cite This Study

Zheng et al. (2025) studied this question.

synapsesocial.com/papers/69362f634fa91c937236ddfchttps://doi.org/10.1182/blood-2025-4733
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Also Consider

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

  1. 1Assessing replicability in chronic kidney disease severity scoring classifications using the sickle cell outcome grading system2025
  2. 2Application and challenges of the sickle cell outcome grading system (SCOGS) for classifying acute chest syndrome severity2025
  3. 3Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort2025
  4. 4Early prediction of severity progression in patients with chronic kidney disease: A Machine Learning Predictive Modelling analysis with retrospective data of a tertiary care hospital2025
  5. 5Machine Learning-Based Identification of Sickle Cell Disease Subphenotypes in Clinical Trial Data2025