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Elucidating Celecoxib's Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language

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Natural language processing (NLP) effectively identifies capecitabine-induced hand-foot syndrome (HFS) in electronic health records. This validated NLP approach aids in real-world adverse event analysis for cancer patients.

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Area of Science:

  • Pharmacovigilance
  • Computational medicine
  • Oncology

Background:

  • Capecitabine, an oral anticancer drug, commonly causes hand-foot syndrome (HFS), impacting patient quality of life and treatment adherence.
  • Detecting symptomatic toxicities like HFS in structured electronic health record (EHR) data is challenging.

Purpose of the Study:

  • To validate a natural language processing (NLP) approach for identifying capecitabine-induced HFS from unstructured clinical text.
  • To demonstrate the application of NLP in evaluating real-world medication-associated adverse event trends.

Main Methods:

  • Retrospective cohort study using EHR data (2004-2021) from the University of Tokyo Hospital.
  • Identification of HFS cases using the MedNERN-CR-JA NLP model.
  • Propensity score matching to compare capecitabine users with and without celecoxib; Cox proportional hazards models for time to HFS onset.
  • Validation of NLP-based HFS detection through manual annotation of clinical notes.

Main Results:

  • Among 669 capecitabine users, HFS incidence was significantly higher (HR, 1.93; P < .001).
  • Celecoxib use showed a suggestive association with reduced HFS risk (HR, 0.51; P = .073).
  • The NLP model achieved high accuracy for HFS detection (precision 0.875, recall 1.000, F1 score 0.933).

Conclusions:

  • NLP is effective for detecting HFS from real-world clinical records.
  • This NLP approach shows potential for retrospective drug safety analysis, as illustrated by celecoxib-HFS investigation.
  • Further research is needed to assess the generalizability of this NLP method across different clinical settings.