ArticleJCO clinical cancer informatics2024
Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.
Article in JCO clinical cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study.European heart journal. Digital health · 2026Article
- Population-Scale Precision Safety in Oncology Reveals Clinical and Genetic Determinants of Systemic Therapy Toxicity.medRxiv : the preprint server for health sciences · 2026Article
- A Clinically Grounded Rule-Based Phenotyping Framework for Identifying Immune-Related Adverse Events in Structured Electronic Health Record Data: A Retrospective EHR Study.Research square · 2026Article
- Semaglutide cardiovascular outcomes align more closely with attained dose than achieved weight loss.NPJ cardiovascular health · 2026Article
- Heterogeneous survival impact of immune-related adverse events in US veterans.Journal for immunotherapy of cancer · 2026Article
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.EBioMedicine · 2026Article
- Artificial Intelligence Applications for Automated Data Extraction and Secondary Use of Clinical Information in Uro-oncology: A Systematic Review.European urology open science · 2026Review
- An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.medRxiv : the preprint server for health sciences · 2026Article
- Challenges in Evaluating Clinical Remission in Severe Asthma: Insights from the Mayo Clinic.Advances in therapy · 2026Article
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- HER2-Ultralow: Prevalence, Characteristics, and Treatment Choices Among Advanced Breast Cancer Patients With Tumors Initially Scored as IHC 0.The breast journal · 2026Article
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Elucidating Celecoxib's Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names.JCO clinical cancer informatics · 2025Article
- Cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors: multicenter study using natural language processing on Belgian hospital data.ESMO real world data and digital oncology · 2025Article
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeImmune 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.
methodsIn 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.
resultsFor 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.
conclusionOverall, 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.