Longitudinal Changes in Peritoneal Transport and Their Impact on Dialysis Outcomes: A Machine Learning Approach integrating Clinical and Biomarker Data

Sustained or transitioning to a high transporter status significantly increases the risk of adverse outcomes in PD patients. Higher MMP2 AR and PAI-1 AR levels are associated with an increased risk of adverse PET trajectory changes, enhancing risk stratification. Integrating biomarker-based predictive models with clinical data improves prognostic accuracy, supporting early intervention strategies for high risk patients.

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Publication Details

Journal
American Journal of Nephrology
Published
2025-04-18
DOI
https://doi.org/10.1159/000545943
Primary Topic
Dialysis and Renal Disease Management
Type
article
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article

Longitudinal Changes in Peritoneal Transport and Their Impact on Dialysis Outcomes: A Machine Learning Approach integrating Clinical and Biomarker Data

Jo-Yen Chao, Sheng‐Hsiang Lin, Kuan‐Hung Liu, Chin‐Chung Tseng et al.
American Journal of Nephrology
Dialysis and Renal Disease Management
article

Longitudinal Changes in Peritoneal Transport and Their Impact on Dialysis Outcomes: A Machine Learning Approach integrating Clinical and Biomarker Data

Jo-Yen Chao, Sheng‐Hsiang Lin, Kuan‐Hung Liu, Chin‐Chung Tseng, Te-Hui Kuo, Chia-Chun Lee, Ming-Cheng Wang, Wei-Ren Lin, An‐Bang Wu
article en

Abstract

Sustained or transitioning to a high transporter status significantly increases the risk of adverse outcomes in PD patients. Higher MMP2 AR and PAI-1 AR levels are associated with an increased risk of adverse PET trajectory changes, enhancing risk stratification. Integrating biomarker-based predictive models with clinical data improves prognostic accuracy, supporting early intervention strategies for high risk patients.

American Journal of NephrologyVol. 56(6)
National Cheng Kung University Hospital (TW), National Cheng Kung University (TW)
Good health and well-being
Openalex Percentile: Top 90%
Dialysis and Renal Disease Management
DOISourceOpenAlex
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