Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study
Abstract 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 p...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-03142-0 |
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| Summary: | Abstract 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. |
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| ISSN: | 1472-6947 |