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Early prediction of growth patterns after pediatric kidney transplantation based on height-related single-nucleotide

Yi Feng1, Yonghua Feng1, Mingyao Hu1

  • 1Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.

Chinese Medical Journal
|September 6, 2023
PubMed

Insights

Machine learning accurately predicts growth patterns in children after kidney transplants using genetic and clinical data. This tool aids in managing growth retardation and optimizing post-transplant care for improved outcomes.

Area of Science:

  • Pediatric Nephrology
  • Genetics
  • Machine Learning

Background:

  • Growth retardation is a significant complication in children with end-stage renal disease (ESRD).
  • Kidney transplantation can partially improve growth, but predicting post-transplant growth remains challenging.
  • Genomic and clinical factors play a role in post-transplant growth trajectories.

Purpose of the Study:

  • To develop and validate a predictive model for growth patterns in pediatric ESRD patients post-kidney transplantation.
  • To utilize machine learning algorithms integrating genomic and clinical variables for enhanced prediction accuracy.
  • To provide a tool for guiding clinical management strategies to mitigate growth retardation.

Main Methods:

  • Retrospective cohort study of 110 pediatric kidney transplant recipients for model development.
  • Whole-exome sequencing (WES) and collection of 729 height-related single-nucleotide polymorphisms (SNPs).
  • Application of seven machine learning algorithms with 10-fold cross-validation, including random forest, for model construction and validation.

Main Results:

  • A predictive model incorporating age and 19 SNPs demonstrated strong performance.
  • The random forest model achieved an accuracy of 0.8125 and an AUC of 0.924 in the primary cohort.
  • The model showed good external validation with accuracy of 0.7949 and AUC of 0.796.

Conclusions:

  • A robust machine learning model was developed and validated for predicting post-transplant growth in children.
  • The model integrates single-nucleotide polymorphisms (SNPs) and clinical data for effective growth pattern prediction.
  • This predictive tool can assist clinicians in tailoring interventions like growth hormone therapy and nutritional support to improve growth outcomes in pediatric ESRD patients.
Abstract

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