Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

Wu, Zhipeng; Wang, Yi; He, Li; Jin, Boxun; Yao, Qinwei; Li, Guangming; Wang, Xin; Ma, Yingmin

doi:10.1080/07853890.2023.2259410

Annals of Medicine

2023

DOI: 10.1080/07853890.2023.2259410

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Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

Zhipeng Wu,

Yi Wang,

Li He

et al.

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Cited by 2 publications

References 60 publications

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Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

Huang,

Chen,

Yang

et al. 2025

Intensive and Critical Care Nursing

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Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

Huang,

Chen,

Yang

et al. 2025

Intensive and Critical Care Nursing

View full text Add to dashboard Cite

Nomogram to Predict Postoperative Acute Kidney Injury after Liver Transplantation

Wang

2024

Asp Biomed Clin Case Rep

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Background: Liver transplantation serves as an essential therapeutic intervention for patients with end-stage liver disease. However, the occurrence of postoperative acute kidney injury (AKI) can markedly affect the clinical prognosis of these patients. Existing models to predict AKI after liver transplantation have limitations in specificity and accuracy, necessitating an updated model. Methods: We conducted a study adhering to the TRIPOD guidelines, including patients who underwent liver transplantation at West China Hospital from 2016 to 2020. Clinical data encompassing demographics, comorbidities, and intraoperative variables were collected. The LASSO regression was used to identify optimal predictors of AKI, leading to the development of a predictive nomogram. The model’s discrimination and calibration were assessed using AUC and calibration curves, respectively. Results: The nomogram, developed from 296 patients in the development cohort and validated on 142 patients, identified surgery duration, intraoperative blood loss, and preoperative serum creatinine as predictors of AKI. It demonstrated good discrimination with AUCs of 0.720 and 0.725 for the development and validation cohorts, respectively. The calibration curve confirmed the model’s accuracy in predicting AKI probabilities. Conclusion: The developed nomogram offers a novel model for predicting AKI risk after liver transplantation, with robust discrimination and calibration. Further multicenter validation and potential integration of genetic and molecular biomarkers for improved accuracy are needed.

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Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

Cited by 2 publications

References 60 publications

Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

Nomogram to Predict Postoperative Acute Kidney Injury after Liver Transplantation

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