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Background <p>Acute kidney injury (AKI) is a potentially serious complication that may arise during the course of anti-tuberculosis therapy in patients with pulmonary tuberculosis (PTB). Reliable tools for predicting AKI risk in this population remain limited. This study aimed to construct a clinical model for early detection of individuals at increased risk of AKI during treatment.</p> Methods <p>This study retrospectively analyzed 1,045 hospitalized PTB patients treated with standard anti-TB regimens. Multivariable logistic regression was used to identify independent predictors of AKI. A clinical nomogram was developed and validated using discrimination, calibration, and decision curve analysis.</p> Results <p>AKI occurred in 16.6% of patients. Eight predictors were identified: age, body mass index (BMI), CA-125, microalbuminuria, hematuria, albumin (ALB), cystatin-C (CYS-C), and estimated glomerular filtration rate (eGFR). The model demonstrated excellent predictive performance (AUC: 0.97 in the training cohort and 0.96 in the validation cohort) and good clinical applicability.</p> Conclusions <p>This study developed and internally validated a practical nomogram for risk prediction of AKI in patients with PTB. The tool, based on routinely available variables, may support early risk stratification and targeted renal monitoring.</p>

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Establishing a clinical tool to predict acute kidney injury in pulmonary tuberculosis: insights from a large-scale retrospective study

  • 期刊论文

  • 发表日期：

    2025年12月30日

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  • Cheng Qiu,
  • Gang-Feng Zhou,
  • Guo-Biao Li,
  • Jian Ao

摘要

Background

Acute kidney injury (AKI) is a potentially serious complication that may arise during the course of anti-tuberculosis therapy in patients with pulmonary tuberculosis (PTB). Reliable tools for predicting AKI risk in this population remain limited. This study aimed to construct a clinical model for early detection of individuals at increased risk of AKI during treatment.

Methods

This study retrospectively analyzed 1,045 hospitalized PTB patients treated with standard anti-TB regimens. Multivariable logistic regression was used to identify independent predictors of AKI. A clinical nomogram was developed and validated using discrimination, calibration, and decision curve analysis.

Results

AKI occurred in 16.6% of patients. Eight predictors were identified: age, body mass index (BMI), CA-125, microalbuminuria, hematuria, albumin (ALB), cystatin-C (CYS-C), and estimated glomerular filtration rate (eGFR). The model demonstrated excellent predictive performance (AUC: 0.97 in the training cohort and 0.96 in the validation cohort) and good clinical applicability.

Conclusions

This study developed and internally validated a practical nomogram for risk prediction of AKI in patients with PTB. The tool, based on routinely available variables, may support early risk stratification and targeted renal monitoring.