Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study

Yao Yu, Changsheng Zhang, Faqiang Zhang, Chang Liu, Hao Li, Jingsheng Lou, Zhipeng Xu, Yanhong Liu, Jiangbei Cao, Weidong Mi, Yao Yu, Changsheng Zhang, Faqiang Zhang, Chang Liu, Hao Li, Jingsheng Lou, Zhipeng Xu, Yanhong Liu, Jiangbei Cao, Weidong Mi

Abstract

Background: Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients.

Methods: A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients.

Results: Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68-0.78) and validation (0.71, 95% CI: 0.63-0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66-0.76).

Conclusion: This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection.

Trial registration: NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.

Keywords: Acute kidney injury; Elderly patients; Hepatectomy; Prediction; Renal injury; Risk score.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Study population enrolled and outcomes in the training and validation datasets. Abbreviations: AKI, Acute kidney injury
Fig. 2
Fig. 2
Development of a nomogram for predicting the probability of postoperative AKI. This nomogram was developed with six perioperative predictors. Find each predictor’s point on the uppermost point scale and add them up. The total point projected to the bottom scale indicates the % probability of postoperative AKI. Abbreviations: AKI, Acute kidney injury; CKD, Chronic kidney disease; NSAIDs, Non-steroidal anti-inflammatory drugs
Fig. 3
Fig. 3
Validation of the nomogram: (A) ROC curve in the training dataset; (B) ROC curve in the validation dataset; (C) Calibration curve for the training dataset; (D) Decision curve analysis for the training dataset. Abbreviations: ROC, Receiver operating characteristic
Fig. 4
Fig. 4
A The summary risk score model for predicting postoperative AKI. One point is assigned to each of the six predictors: age above 67 years, CKD, use of NSAIDs, intraoperative hepatic inflow occlusion, intraoperative blood loss > 300 mL, and blood transfusion. B Comparison between AUC-ROC of the multivariable logistic regression model and summary risk score model. Abbreviations: AKI, Acute kidney injury; AUC, Area under the ROC curve; CKD, Chronic kidney disease; NSAIDs, Non-steroidal anti-inflammatory drugs; ROC, Receiver operating characteristic

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Source: PubMed

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