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Development and validation of a risk prediction model for acute kidney injury in patients with malignant obstructive

Pingping Ju1, Zhaoting Li2, Yu Zhu1

  • 1Department of Nephrology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Jiangsu, China.

Renal Failure
|October 13, 2025
PubMed

Insights

A new model accurately predicts acute kidney injury (AKI) in patients with malignant obstructive jaundice (MOJ). This tool uses routine clinical parameters to assess individual risk, aiding early intervention for this serious complication.

Area of Science:

  • Nephrology
  • Oncology
  • Clinical Prediction Models

Background:

  • Acute kidney injury (AKI) is a significant complication for patients with malignant obstructive jaundice (MOJ).
  • Currently, no validated predictive models exist specifically for AKI in the MOJ population.
  • Early identification of at-risk patients is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for AKI in hospitalized patients with MOJ.
  • To identify key clinical parameters that can predict AKI development in this cohort.
  • To provide a practical tool for individualized risk assessment and clinical decision-making.

Main Methods:

  • Retrospective analysis of 557 hospitalized MOJ patients, with 103 developing AKI.
  • Propensity score matching was employed to create balanced cohorts for analysis.
  • Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to build the predictive model.
  • Model performance was assessed using AUC, concordance index, Brier score, calibration curves, and decision curve analysis.

Main Results:

  • A final model incorporating seven variables (NLR, CRP, UA, TBil, K, CO2CP, PT) was developed.
  • The model demonstrated excellent discrimination with an AUC of 0.891 and good calibration.
  • Internal validation confirmed the model's robustness, and a nomogram facilitated individualized risk stratification.
  • Risk stratification revealed a sharp increase in AKI incidence across predicted risk tertiles (11.6%, 47.1%, 91.3%).

Conclusions:

  • The developed model offers an accurate and practical method for predicting AKI in MOJ patients using readily available clinical data.
  • The nomogram allows for precise, individualized risk assessment, supporting clinical management strategies.
  • Further prospective multicenter studies are recommended to validate the generalizability of this predictive model.
Keywords:
Acute kidney injurylogistic regressionmalignant obstructive jaundicenomogramprediction modelpropensity score matching

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