# Prognostic Value of Blood Urea Nitrogen for Acute Kidney Injury and Mortality in Vasculitis: A Large Cohort Study Using Multivariate Joint Model and Machine Learning

## TL;DR

This study shows that blood urea nitrogen levels can predict kidney injury and death in vasculitis patients, helping with early risk assessment.

## Contribution

The study introduces BUN as a novel and practical predictor for AKI and mortality in vasculitis using joint modeling and machine learning.

## Key findings

- AKI occurred in 25.1% of vasculitis patients and was linked to higher mortality rates.
- BUN was identified as an independent predictor of AKI and mortality in multivariable models.
- A BUN threshold of 32 mg/dL was found to significantly increase mortality risk.

## Abstract

Background: Acute kidney injury (AKI) is a serious complication in vasculitis patients and may adversely affect prognosis. However, the role of blood urea nitrogen (BUN) as a predictor of AKI and mortality in vasculitis has not been fully elucidated. Methods: We retrospectively analyzed 701 patients with large-, medium-, and small-vessel vasculitis from the MIMIC-III/IV databases to evaluate the relationship between BUN, AKI occurrence, and mortality. AKI was defined according to the KDIGO serum creatinine criteria. Logistic and Cox regression models, restricted cubic spline (RCS) analyses, and multiple machine learning models were employed to identify risk factors and assess predictive performance. Results: AKI occurred in 25.1% (176/701) of vasculitis patients and was associated with significantly higher 30- and 365-day mortality rates (p < 0.05). Multivariable logistic regression identified BUN as an independent predictor of AKI (OR: 1.03; 95% CI: 1.02–1.05; p < 0.0001). Patients in the highest BUN tertile had a 5.67-fold greater risk of AKI compared to the lowest tertile (p < 0.0001). The Cox regression confirmed BUN as an independent predictor of 30- and 365-day mortality among patients with AKI (p < 0.05). The RCS analysis identified a critical BUN threshold of 32 mg/dL, above which the mortality risk markedly increased. Machine learning models further validated the prognostic significance of BUN and age, with the logistic regression model achieving the highest predictive accuracy (area under the curve: 0.904). Conclusions: BUN is a practical predictor of AKI and mortality in vasculitis and may assist early risk stratification in this population.