Establishment and evaluation of a clinical prediction model for acute renal injury related to anti-tumor drugs using artificial intelligence

LI Ze-Wei; XU Jian; ZHANG Yi; PU Li-Tian; LI Li; YANG Meng; XIONG Li-Yan; CUI Jian-Chen; SHEN Ying; WANG Xin-Yu; DENG Qin-Yuan

doi:10.3969/j.issn.1671-4091.2026.06.012

Chinese Journal of Blood Purification ›› 2026, Vol. 25 ›› Issue (06) : 501-507. DOI: 10.3969/j.issn.1671-4091.2026.06.012

Establishment and evaluation of a clinical prediction model for acute renal injury related to anti-tumor drugs using artificial intelligence

LI Ze-wei， ZHANG Y， PU Li-tian， LI Li，YANGXIONG Li-yan， CUI Jian-chen， SHEN Ying， WANG Xin-yu， DENG Qin-yuan，XU Jian

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Abstract

Objective This study aimed to investigate the risk factors for acute kidney injury (AKI) associated with antineoplastic drugs and to develop an artificial intelligence (AI)-based prediction model. Methods A cohort of 272 cancer patients diagnosed and treated at the First People's Hospital of Yunnan Province between January 1, 2015, and August 1, 2023 were enrolled. These patients were divided into the AKI group (n=89) and the non-AKI group (n=183) based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Both univariate and multivariate logistic regression analyses were employed to identify independent risk factors, and machine learning models, such as decision trees and random forests algorithms, were constructed. The performance of these models was assessed using metrics including the area under the receiver operating characteristic curve (AUC). Results Of the 272 patients included, 89 (32.7%) developed AKI. Univariate analysis showed that patients with normal body mass index (BMI) (OR=0.533, 95%CI：0.295～0.964，P=0.037), previous radiotherapy (OR=0.369, 95%CI：0.147～0.922，P=0.033), or surgery (OR=0.142, 95%CI：0.065～0.311，P＜0.001) were associated with a lower risk of AKI. However, multivariate analysis identified respiratory system cancer (OR=2.162, 95%CI：1.172～3.991, P=0.014), the use of cisplatin (OR=2.135, 95% CI: 1.178～3.869, P=0.012), and non-platinum chemotherapy drugs (OR=9.247, 95% CI：4.271～20.017, P＜0.001) as independent risk factors. The decision tree model achieved an AUC of 0.660, the LASSO regression model an AUC of 0.864, and the neural network model an AUC of 0.833. Among all models, the random forest model exhibited the optimal predictive performance, with an AUC of 0.870 and a recall of 0.919. Conclusions The AI model utilizing the random forest algorithm enables early identification of high-risk patients for AKI associated with antineoplastic drugs, thereby providing valuable decision support for clinical intervention.

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Artificial intelligence / Acute kidney injury / Machine learning / Predictive modeling

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LI Ze-wei, ZHANG Y, PU Li-tian, LI Li, YANGXIONG Li-yan, CUI Jian-chen, SHEN Ying, WANG Xin-yu, DENG Qin-yuan, XU Jian. Establishment and evaluation of a clinical prediction model for acute renal injury related to anti-tumor drugs using artificial intelligence[J]. Chinese Journal of Blood Purification. 2026, 25(06): 501-507 https://doi.org/10.3969/j.issn.1671-4091.2026.06.012

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