Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 48.2% vs 1.58% hearing loss prevalence (high-risk vs low-risk subtypes)

• Primary Outcome: Hearing loss prevalence stratified by risk subtypes

• Nnt Or Sensitivity: AUC 0.939 (testing set), 0.984 (training/validation)

• Confidence Interval: Not reported

Clinical Application

Web-based tool developed but requires validation; integration into routine CKD care would need workflow adaptation Relevant to Australian CKD management but requires local validation; could inform RACGP chronic disease management and specialist referral pathways CKD patients requiring hearing loss risk assessment, particularly in nephrology and audiology settings

Abstract

This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection via univariate and multivariate logistic regression identified key predictors, which were used to construct predictive models with nine machine learning algorithms. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, with mean area under the curve (AUC) values of 0.984 (training), 0.984 (validation), and 0.939 (testing). SHapley Additive exPlanations (SHAP) interpretation identified age as the predominant risk determinant. Subsequent Gaussian mixture modeling (GMM) clustered patients into two distinct subtypes: a low-risk subgroup (n = 1,075) with a 1.58% HL prevalence and a high-risk subgroup (n = 2,316) characterized by older age, elevated blood urea nitrogen and bicarbonate levels, and a 48.2% HL prevalence. A classifier trained on these subtypes achieved discrimination (AUC = 0.99974). A clinically a web-based tool was also developed based on the six most influential features. The findings establish a dual-level predictive framework integrating explainable ML and unsupervised clustering for HL risk assessment in CKD. This approach provides a robust strategy for the precision screening of high-risk subpopulations and supports the integration of hearing assessments into routine CKD.

References

1. 1.Chen, L., Wang, J., Liu, G., Zhao, Y., Zhou, Z., & Li, Q. (2026). Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD. Renal Failure. https://doi.org/10.1080/0886022X.2026.2649658