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Area of Science:

  • Nephrology
  • Public Health
  • Data Science

Background:

  • Early identification of progressive chronic kidney disease (CKD) at health checkups offers a vital opportunity to improve patient prognosis.
  • Current common health tests face challenges in accurately identifying individuals with progressive early CKD.
  • A 7-year worksite-based cohort study in Japan involving 7465 participants was conducted to address this diagnostic gap.

Purpose of the Study:

  • To evaluate the progression of chronic kidney disease (CKD) over a 7-year period.
  • To identify predictive factors and patterns associated with CKD prognosis aggravation.
  • To develop a more effective method for identifying progressive CKD patients, including those initially at low risk.

Main Methods:

  • A 7-year worksite-based cohort study with 7465 male participants (mean age 50.1 years, eGFR 79 mL/min/1.73 m²).
  • Analysis of KDIGO prognostic category changes over time, utilizing vector analysis and Bayesian networks.
  • Application of Support Vector Machines (SVM) incorporating time-series data for outcome prediction.

Main Results:

  • CKD progression was observed to increase starting from the 3-year mark in the study cohort.
  • Vector analysis indicated that CKD stage G1 A1 exhibited greater progression than CKD stage G2 A1.
  • Bayesian networks revealed a correlation between time-series changes in CKD prognostic categories and the study outcome.
  • SVM models, using time-series CKD data from year 3 onwards, successfully identified high-risk outcomes in both initially high-risk and low-risk individuals.

Conclusions:

  • The study highlights the necessity of extended follow-up, beyond initial high-risk assessments, for individuals with early chronic kidney disease.
  • Monitoring patients for at least 3 years, and potentially longer, is recommended, encompassing both high-risk and low-risk individuals identified at baseline.
  • Time-series analysis of CKD prognostic categories, particularly from 3 years post-evaluation, significantly enhances the ability to predict disease progression.