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. 2023 Jan 19:36:10887.
doi: 10.3389/ti.2023.10887. eCollection 2023.

A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Jianfeng Zeng  1 , Qiaoyun Li  2 , Qixing Wu  3 , Li Li  1 , Xijiu Ye  1 , Jing Liu  4 , Bingbing Cao  1
Affiliations

A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Jianfeng Zeng et al. Transpl Int. .

Abstract

Acute kidney injury (AKI) after liver transplantation (LT) is a common complication, and its development is thought to be multifactorial. We aimed to investigate potential risk factors and build a model to identify high-risk patients. A total of 199 LT patients were enrolled and each patient data was collected from the electronic medical records. Our primary outcome was postoperative AKI as diagnosed and classified by the KDIGO criteria. A least absolute shrinkage and selection operating algorithm and multivariate logistic regression were utilized to select factors and construct the model. Discrimination and calibration were used to estimate the model performance. Decision curve analysis (DCA) was applied to assess the clinical application value. Five variables were identified as independent predictors for post-LT AKI, including whole blood serum lymphocyte count, RBC count, serum sodium, insulin dosage and anhepatic phase urine volume. The nomogram model showed excellent discrimination with an AUC of 0.817 (95% CI: 0.758-0.876) in the training set. The DCA showed that at a threshold probability between 1% and 70%, using this model clinically may add more benefit. In conclusion, we developed an easy-to-use tool to calculate the risk of post-LT AKI. This model may help clinicians identify high-risk patients.

Keywords: acute kidney injury; liver transplantation; nomogram; online calculator; risk factors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flowchart of our study.
FIGURE 2
FIGURE 2
Feature selection of LT patients using the LASSO logistic regression model. (A) A Lasso coefficient profile plot was built for the prediction of AKI after LT. (B) The optimal parameter (λ) was selected by the LASSO model using 10-fold cross-validation via 1 standard error of the minimum criteria.
FIGURE 3
FIGURE 3
(A) The post-LT AKI nomogram model was developed. This nomogram was constructed in the training set, with whole blood serum lymphocyte count, RBC count, sodium, intraoperative insulin dosage and anhepatic phase urine volume. (B) Online calculator predicting acute kidney injury after liver transplantation accessible at https://caobingbing.shinyapps.io/Nomogram_for_AKI_after_liver_transplantation/, depicting an example for predicting the probability of an LT patient with normal preoperative whole blood serum lymphocyte count, RBC count and serum sodium. The intraoperative insulin dosage was 50 IU, and anhepatic phase urine volume was 200 ml. Abbreviations: AKI, acute kidney injury; LT, liver transplantation; RBC, red blood cell.
FIGURE 4
FIGURE 4
Diagnostic accuracy of the post-LT AKI nomogram model in the training and validation sets. ROC curves showed AUCs of 0.817 and 0.906 in the training (A) and validation sets (B).
FIGURE 5
FIGURE 5
Calibration curves of the post-LT AKI nomogram model in the training (A) and validation sets (B) (Bootstrap = 1,000 repetitions).
FIGURE 6
FIGURE 6
Decision curve analysis of the nomogram for the estimation of post-LT AKI in the training set (A) and validation set (B).
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