Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.

Journal: Scientific reports
PMID: 39994365

Abstract

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (AIC) using electronic medical records. We evaluated the effects of angiotensin II receptor blockers/angiotensin-converting enzyme inhibitors (ARB/ACEIs), beta-blockers (BBs), statins, and calcium channel blockers (CCBs) on AIC using signals extracted from clinical texts via NLP. The study included 2935 patients prescribed anthracyclines at a single hospital, with concomitant prescriptions of ARB/ACEIs, BBs, statins, and CCBs. Upon propensity score matching, groups with and without these medications were compared, and expressions suggestive of cardiotoxicity, extracted via NLP, were considered as the outcome. The hazard ratios for ARB/ACEIs, BBs, statins, and CCBs were 0.58 [95% CI: 0.38-0.88], 0.71 [95% CI: 0.35-1.44], 0.60 [95% CI 0.38-0.95], and 0.63 [95% CI: 0.45-0.88], respectively. ARB/ACEIs, statins, and CCBs significantly suppressed AIC, whereas BBs did not demonstrate statistical significance, possibly due to limited statistical power. NLP-extracted signals from clinical texts reflected the known effects of these medications, demonstrating the feasibility of NLP-based drug repositioning. Further investigation is needed to determine if similar results can be replicated using electronic medical records from other institutions.

Authors

  • Yoshimasa Kawazoe
    Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Masami Tsuchiya
    Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
  • Kiminori Shimamoto
    Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Tomohisa Seki
    Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan; Department of Emergency and Critical Care Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.
  • Emiko Shinohara
    The University of Tokyo Hospital, Tokyo, Japan.
  • Shuntaro Yada
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
  • Shoko Wakamiya
    Nara Institute of Science and Technology (NAIST), Japan.
  • Shungo Imai
    Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan.
  • Eiji Aramaki
    Nara Institute of Science and Technology (NAIST), Japan.
  • Satoko Hori
    Keio University Faculty of Pharmacy, Division of Drug Informatics, Tokyo, Japan.

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