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Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis

Mohamed Ghalwash1, Akira Koseki2, Toshiya Iwamori2

  • 1IBM Research, Healthcare and Life Sciences, Yorktown, NY, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 23, 2023
PubMed

Insights

This study identifies key factors for Chronic Kidney Disease (CKD) progression to dialysis. Early identification of these features aids in developing new treatments and preventative strategies for CKD patients.

Area of Science:

  • Nephrology
  • Biostatistics
  • Data Science

Background:

  • Chronic Kidney Disease (CKD) affects kidney function, often undetected early.
  • Progression to dialysis is a significant health consequence for CKD patients.
  • Understanding early CKD progression is crucial for timely intervention.

Purpose of the Study:

  • To stratify CKD patients based on time to dialysis from early stages.
  • To identify key clinical features predicting rapid progression to dialysis.
  • To develop interpretable models for CKD patient stratification.

Main Methods:

  • Feature reduction and predictive modeling on ~40,000 CKD patients.
  • Time-to-event analysis to model time to dialysis.
  • Stratification of a subpopulation (3,522 patients) with anemia and cardiovascular drug prescriptions.

Main Results:

  • Identified top important features for predicting time to dialysis.
  • Stratified a high-risk CKD subpopulation exhibiting anemia and cardiovascular drug use.
  • Conventional clustering methods lacked interpretability for identifying fast progression factors.

Conclusions:

  • Developed the first interpretable feature-based stratification for early CKD patients using time-to-event analysis.
  • The findings can aid in CKD prevention and the development of novel therapeutic strategies.
  • This approach offers a clearer understanding of factors driving rapid CKD progression.

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