Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease
Creators: name: Wu, Le-hao
name: Zhao, Dan
name: Niu, Jian-Ying
name: Fan, Qiu-Ling
name: Peng, Ai
name: Luo, Cheng-gong
name: Zhang, Xiao-qin
name: Tang, Tian
name: Yu, Chen
name: Zhang, Ying-ying
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Biochemistry
subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Marine Biology
subject: Cancer
subject: Plant Biology
subject: Virology
subject: Computational Biology

description: Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers. This multi-center retrospective study included 598 patients with kidney fibrosis from four hospitals. A training cohort of 360 patients from Shanghai Tongji Hospital was used to develop a predictive nomogram and ML model, with fibrosis severity classified as mild or moderate-to-severe based on Banff scores. Logistic regression identified key predictors, which were incorporated into a nomogram and ML model. An external validation cohort of 238 patients from three additional hospitals was used for model evaluation. Serum creatinine (Scr), estimated glomerular filtration rate (eGFR), parathyroid hormone (PTH), brain natriuretic peptide (BNP), and sex were identified as independent predictors of kidney fibrosis severity. The nomogram demonstrated superior discriminative ability in the training cohort (AUC: 0.89, 95% CI: 0.85–0.92) compared to eGFR (AUC: 0.83, 95% CI: 0.78–0.87) and Scr (AUC: 0.87, 95% CI: 0.83–0.91). Among ML models, the Random Forest (RF) model achieved the highest AUC (0.98). In external validation, the nomogram and RF models maintained robust performance with AUCs of 0.86 and 0.79, respectively. This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation. Kidney fibrosis being the most important pathological change in the progression of CKD, is currently diagnosed by a kidney biopsy. Given the limitations of biopsy, we propose a novel and practical noninvasive evaluation method for clinical use. Our study combine nomogram with machine learning to develop models for evaluating the severity of kidney fibrosis. The model we developed is based on clinical biochemical indicators to predict different degrees of kidney fibrosis, and external validation shows a significant discriminative ability (nomogram model AUC = 0.86, machine learning model AUC = 0.79). The developed prediction model may serve as a useful tool for assessing kidney fibrosis in CKD patients.