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Updated: Sep 12, 2025
A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with
Wentong Liu1,2, Kaiyue Ji1, Qianwen Tang3
1Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
Background:
Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse events can interrupt treatment, affecting patient outcomes. Early prediction of AKI is essential for improved prognosis and personalized therapeutic strategies. Previous research has been constrained by significant limitations, underscoring the necessity for AKI risk prediction models for patients treated with PD-1/PD-L1 inhibitors. This study aimed to develop and validate an interpretable machine learning (ML) model for early AKI prediction in patients undergoing PD-1/PD-L1 inhibitor therapy using a retrospective cohort design.
Methods:
This study collected data from patients treated with PD-1/PD-L1 inhibitors at Zhejiang Provincial People's Hospital between January 2018 and January 2024. Nine ML models were evaluated. SHapley Additive exPlanations (SHAP) were employed to rank feature importance and interpret the final model. Additionally, a web-based calculator based on the model was developed.
Results:
Among the nine ML models evaluated, the Grandient Boosting Machine (GBM) model achieved the best predictive performance. In the validation set, the GBM model achieved an AUC of 0.850 (95%CI: 0.830-0.870). In the test set, the AUC was 0.795(95% CI: 0.747-0.844), demonstrating accurate AKI risk prediction. Calibration curves demonstrated a strong concordance between predicted and observed risk probabilities. An interpretable final GBM model with 13 features was developed after feature reduction based on feature importance ranking. A web-based calculator accessible at https://predicatingaki.shinyapps.io/PDmodel/ has been developed to assist clinicians in AKI risk assessment.
Conclusion:
This study developed and validated an interpretable ML model using a large dataset to predict AKI risk in patients receiving PD-1/PD-L1 inhibitor therapy. This model can assist clinicians in the early identification of high-risk patients, facilitating personalized treatment plans.
Trial Registration:
The study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of Zhejiang Provincial People's Hospital (Approval No. KT2024116) in 3 Jan. 2025. As it was a retrospective study with anonymized data, informed consent was waived.
Insights
This study developed an interpretable machine learning model to predict acute kidney injury (AKI) in patients receiving anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy. The model aids in early identification of high-risk patients for personalized treatment.
Area of Science:
- Oncology
- Nephrology
- Immunotherapy
- Machine Learning
Background:
- Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has transformed cancer treatment but can cause acute kidney injury (AKI).
- Early AKI prediction is crucial for managing treatment interruptions and improving patient outcomes.
- Existing research lacks robust AKI risk prediction models for patients on PD-1/PD-L1 inhibitors.
Purpose of the Study:
- To develop and validate an interpretable machine learning (ML) model for early AKI prediction.
- To identify patients at high risk of developing AKI during PD-1/PD-L1 inhibitor therapy.
- To facilitate personalized therapeutic strategies and improve patient prognosis.
Main Methods:
- Retrospective cohort design utilizing data from January 2018 to January 2024.
- Evaluation of nine ML models, with Gradient Boosting Machine (GBM) selected for optimal performance.
- Application of SHapley Additive exPlanations (SHAP) for feature importance and model interpretability.
Main Results:
- The GBM model demonstrated strong predictive performance with an AUC of 0.850 in the validation set and 0.795 in the test set.
- Calibration curves confirmed good agreement between predicted and observed AKI risks.
- An interpretable GBM model with 13 features was developed, and a web-based calculator is available for clinical use.
Conclusions:
- An interpretable ML model was successfully developed and validated for predicting AKI in patients receiving PD-1/PD-L1 inhibitors.
- The model enables early identification of high-risk individuals, supporting personalized treatment adjustments.
- This tool can assist clinicians in proactive AKI management within cancer immunotherapy settings.
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