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Abstract: TH-PO819

Development of a Predictive Model for Assessing the Risk of Severe Renal Fibrosis in Kidney Biopsy

Session Information

Category: Pathology and Lab Medicine

  • 1800 Pathology and Lab Medicine


  • Spanuchart, Ittikorn, University of Michigan, Ann Arbor, Michigan, United States
  • Tungphaisal, Veeraphol, Mahidol University Faculty of Medicine Ramathibodi Hospital, Bangkok, Thailand
  • Thammavaranucupt, Kanin, Chakri Naruebodin Medical Institute, Bang Phli, Samut Prakan, Thailand

Kidney biopsy is an essential diagnostic tool for various kidney diseases, but its clinical utility may be limited in cases of severe fibrosis, coupled with an increased risk of bleeding. Although several clinical parameters are used to predict the extent of renal fibrosis, a validated predictive model has not yet been established. This study aimed to construct a predictive model for assessing the risk of severe renal fibrosis.


Medical records of patients who underwent native kidney biopsies at Ramathibodi Hospital between January 2017 and December 2021 were reviewed. Severe renal fibrosis was defined as interstitial fibrosis and tubular atrophy (IFTA) greater than 50% or glomerulosclerosis greater than 50% on the pathology report. Clinical data, laboratory results, and ultrasonographic parameters were collected. Multivariable logistic regression analysis was performed to build the predictive model, and its discriminative performance was assessed using the receiver operating characteristic (ROC) curve. The internal validity of the model was evaluated through bootstrapping techniques.


Among 202 patients, 31% exhibited severe renal fibrosis. The predictive model incorporated five significant predictors: nocturia, CKD, anemia, kidney length, and loss of corticomedullary differentiation. The model had an area under the ROC curve of 0.87 (95% CI: 0.818-0.923). The scoring model ranged from 0 to 18, with a score of 10 or higher indicating a positive likelihood ratio of 26 for severe fibrosis prediction. Internal validation using bootstrap resampling yielded an optimism of 0.024, with a shrinkage factor of 0.869.


The developed predictive model, utilizing routine clinical parameters, has demonstrated exceptional discriminative ability and ease of use in predicting renal fibrosis. It holds great potential in assisting clinicians with risk stratification and the planning of kidney biopsies.