Article ; Online: Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.
JCO clinical cancer informatics
2024 Volume 8, Page(s) e2300151
Abstract: Purpose: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is ... ...
| Abstract | Purpose: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. Methods: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. Results: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. Conclusion: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs. |
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| MeSH term(s) | Humans ; Natural Language Processing ; Electronic Health Records ; Immune Checkpoint Inhibitors/adverse effects ; Female ; Male ; Drug-Related Side Effects and Adverse Reactions/epidemiology ; Drug-Related Side Effects and Adverse Reactions/diagnosis ; Drug-Related Side Effects and Adverse Reactions/etiology ; Neoplasms/drug therapy ; Middle Aged ; Aged |
| Chemical Substances | Immune Checkpoint Inhibitors |
| Language | English |
| Publishing date | 2024-04-30 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ISSN | 2473-4276 |
| ISSN (online) | 2473-4276 |
| DOI | 10.1200/CCI.23.00151 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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