A Prediction Model for Acute Kidney Injury in Adult Patients With Minimal Change Disease

Area of Science:

• Nephrology
• Internal Medicine

Background:

• Predicting acute kidney injury (AKI) in minimal change disease (MCD) patients is challenging due to limited predictive methods.
• Timely interventions for AKI are crucial but hindered by the lack of convenient predictive tools, especially for MCD patients.

Purpose of the Study:

• To develop and validate a predictive model for AKI in hospitalized adult patients with biopsy-proven MCD.
• To identify independent risk factors for AKI onset in this specific patient population.

Main Methods:

• Retrospective analysis of 401 adult MCD patients, split into training (n=283) and validation (n=118) cohorts.
• Lasso and logistic regression were used to identify independent risk factors for AKI.
• A predictive nomogram was developed and validated using these factors.

Main Results:

• AKI incidence was 19% in the training cohort and 13% in the validation cohort.
• Mean arterial pressure, serum albumin, uric acid, and lymphocyte counts were identified as independent predictors of AKI.
• The nomogram demonstrated good predictive performance with concordance indices of 0.84 (training) and 0.75 (validation).

Conclusions:

• A feasible predictive nomogram was developed to identify MCD patients at high risk of developing AKI.
• This tool can facilitate timely treatment and potentially improve patient outcomes.
• The nomogram offers a practical approach for clinical decision-making in AKI risk stratification for MCD patients.