Risks of Major Adverse Kidney Events in Non-Hispanic Black Patients with Diabetes or Hypertension: A real-world cohort study.

INTRODUCTION

Chronic kidney disease (CKD) disproportionately burdens non-Hispanic Black (NHB) patients who experience a three- to four-fold higher risk of kidney failure than non-Hispanic White (NHW) individuals. Diabetes and hypertension are the leading causes of CKD, yet the influence of race, social context, and clinical factors on major adverse kidney events (MAKE) remains inadequately understood. This study examined relationships between race, social factors, and MAKE among patients with diabetes or hypertension.

METHODS

Electronic health record data from the CURE-CKD Registry (2013-2022) were used to assemble two mutually-exclusive cohorts of NHB and NHW (reference) adults with 1. diabetes (N=375,605) or 2. Hypertension without diabetes (N=710,768). The primary outcome was time to first MAKE, defined as ≥40% decline in estimated glomerular filtration rate (eGFR), eGFR <15 mL/min/1.73 m², kidney failure, dialysis, transplant, or death. Cox proportional hazards models estimated associations between predictors and MAKE. Extreme gradient boosting (XGBoost) machine learning (ML) models were used as a complement to the traditional survival models to evaluate variable relationships with MAKE and compare predictive performance.

RESULTS

NHB patients were younger (diabetes: 56±15 vs. 62±14 years; hypertension: 50±16 vs. 59±16 years), had more prevalent CKD (diabetes: 19% vs. 15%; hypertension: 8% vs. 7%), and had higher adjusted MAKE risk than NHW patients (diabetes: HR=1.07, 95% CI: 1.04-1.11; hypertension: HR=1.10, 95% CI: 1.06-1.14). MAKE occurred in 24% (n=90,910) of the diabetes population over a median (interquartile range) of 4.1 (2.0-6.3) years, and 15% (n=106,209) of the hypertension population over 4.4 (2.4-6.3) years. Non-commercial insurance (Medicaid, Medicare or unclassified insurance [reference: Commercial]), higher social vulnerability index, more frequent hospitalizations, and urban residence were the top predictors of MAKE. XGBoost outperformed Cox models for MAKE prediction and SHapley Additive exPlanations value rankings confirmed key predictors identified in Cox models.

CONCLUSION

Across two large cohorts at-risk for CKD, NHB patients experienced higher MAKE risk, shaped by neighborhood social and structural conditions rather than demographic and clinical characteristics alone. Integrating social risk assessment, improving guideline-directed testing, and using complementary ML and Cox modeling approaches may enhance early identification of CKD risk, support targeted intervention, and reduce disparities in CKD outcomes.