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; Boxun Jin; Qinwei Yao; Guangming Li; Xin Wang; Yingmin Ma

doi:10.1080/07853890.2023.2259410

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

Ann Med. 2023;55(2):2259410. doi: 10.1080/07853890.2023.2259410. Epub 2023 Sep 21.

Authors

Zhipeng Wu¹, Yi Wang², Li He², Boxun Jin², Qinwei Yao², Guangming Li³, Xin Wang², Yingmin Ma^{1

4}

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China.
² Department of Intensive Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China.
³ Department of General Surgery, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China.
⁴ Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing, People's Republic of China.

Abstract

Background: Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI.

Methods: A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model.

Results: In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121-2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316-7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001-1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893-17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737-0.894).

Conclusion: The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.

Keywords: Acute kidney injury; clinical parameters; liver transplantation; nomogram; postoperative cystatin C level; prediction model.

Plain language summary

Acute kidney injury (AKI) is one of the most common and important complications after liver transplantation (LT).We developed a nomogram model to predict post-LT AKI based on clinical parameters and postoperative cystatin C level.The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance, which could aid in the early detection of AKI and improve the prognosis of patients after LT.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Acute Kidney Injury* / diagnosis
Acute Kidney Injury* / etiology
Cystatin C
Humans
Hypotension*
Liver Transplantation* / adverse effects
Nomograms

Substances

Cystatin C

Grants and funding

This work was supported by Scientific Research Project of Beijing Youan Hospital [Grant No. BJYAYY-YN2022-22, CCMU, 2022], Beijing Natural Science Foundation [Grant No. 7232079] and key medical professional development plan of Beijing Hospital Authority [Grant No. ZYLX202124] and Beijing Natural Science Foundation [Grant No. 7222096].