Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

Journal: BMC medical informatics and decision making
Published Date:

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.

Authors

  • Wentong Liu
    Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
  • Kaiyue Ji
    Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
  • Qianwen Tang
    School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang, 311399, China.
  • Weiqi Xia
    College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Lina Shao
    State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Science, Changchun, China.
  • Jiana Shi
    Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital(Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
  • Yukun Li
    Pharmacy Department, The First People's Hospital of Aksu Prefecture, Aksu, Xinjiang, 843000, China.
  • Ping Huang
    Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Xiaolan Ye
    School of Insurance, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China.