Cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors: multicenter study using natural language processing on Belgian hospital data

D. Delombaerde , C.L. Oeste , V. Geldhof , L. Croes , I. Bassez , A. Verbiest , L. Tack , D. Hens , C. Franssen , P.R. Debruyne , H. Prenen , M. Peeters , J. De Sutter , C. Vulsteke
{"title":"Cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors: multicenter study using natural language processing on Belgian hospital data","authors":"D. Delombaerde ,&nbsp;C.L. Oeste ,&nbsp;V. Geldhof ,&nbsp;L. Croes ,&nbsp;I. Bassez ,&nbsp;A. Verbiest ,&nbsp;L. Tack ,&nbsp;D. Hens ,&nbsp;C. Franssen ,&nbsp;P.R. Debruyne ,&nbsp;H. Prenen ,&nbsp;M. Peeters ,&nbsp;J. De Sutter ,&nbsp;C. Vulsteke","doi":"10.1016/j.esmorw.2024.100111","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Immune checkpoint inhibitor (ICI) use may be associated with diverse cardiovascular (CV) adverse events (AEs), but their baseline prevalence and incidence after ICI initiation are poorly known. We aimed to describe CV events using real-world hospital data from Belgian cancer patients.</div></div><div><h3>Materials and methods</h3><div>Electronic health records (EHRs) from patients receiving at least one ICI between March 2017 and August 2022 at three Belgian hospitals were processed into an Observational Medical Outcomes Partnership Common Data Model warehouse. Structured data were enriched with unstructured data that were processed using a natural language processing (NLP) pipeline. We analyzed CV events from first ICI administration until last follow-up, identifying and validating the first detection of a CV event at the patient level.</div></div><div><h3>Results</h3><div>We included 1571 patients (66% male, median age 67 years); CV events were detected in 196 (12.5%) patients [median (min-max) follow-up: 8 (0-63) months]. The CV AEs detected were heart failure (5.3%), atrial fibrillation (4.6%), myocardial infarction (2.0%), atrioventricular block (1.9%), myocarditis (1.2%), vasculitis (0.8%), pericarditis (0.4%), and Takotsubo cardiomyopathy (&lt;0.3%). Median time (min-max) to onset ranged from 109 days (17-849 days) for myocarditis to 529 days (91-967 days) for Takotsubo cardiomyopathy.</div></div><div><h3>Conclusions</h3><div>To our knowledge, this is the first study using a dataset enriched with NLP-processed EHRs that describes the frequency and onset time of CV events. CV event frequencies were higher than those reported in clinical trials, but similar to other real-world studies. However, we observed a later time to onset. Hence, clinicians should note that CV AEs can present in various ways and at any time during or after treatment.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"7 ","pages":"Article 100111"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ESMO Real World Data and Digital Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949820124000894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Immune checkpoint inhibitor (ICI) use may be associated with diverse cardiovascular (CV) adverse events (AEs), but their baseline prevalence and incidence after ICI initiation are poorly known. We aimed to describe CV events using real-world hospital data from Belgian cancer patients.

Materials and methods

Electronic health records (EHRs) from patients receiving at least one ICI between March 2017 and August 2022 at three Belgian hospitals were processed into an Observational Medical Outcomes Partnership Common Data Model warehouse. Structured data were enriched with unstructured data that were processed using a natural language processing (NLP) pipeline. We analyzed CV events from first ICI administration until last follow-up, identifying and validating the first detection of a CV event at the patient level.

Results

We included 1571 patients (66% male, median age 67 years); CV events were detected in 196 (12.5%) patients [median (min-max) follow-up: 8 (0-63) months]. The CV AEs detected were heart failure (5.3%), atrial fibrillation (4.6%), myocardial infarction (2.0%), atrioventricular block (1.9%), myocarditis (1.2%), vasculitis (0.8%), pericarditis (0.4%), and Takotsubo cardiomyopathy (<0.3%). Median time (min-max) to onset ranged from 109 days (17-849 days) for myocarditis to 529 days (91-967 days) for Takotsubo cardiomyopathy.

Conclusions

To our knowledge, this is the first study using a dataset enriched with NLP-processed EHRs that describes the frequency and onset time of CV events. CV event frequencies were higher than those reported in clinical trials, but similar to other real-world studies. However, we observed a later time to onset. Hence, clinicians should note that CV AEs can present in various ways and at any time during or after treatment.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evolving treatment patterns and outcomes among patients with metastatic urothelial carcinoma post-avelumab maintenance approval: insights from The US Oncology Network Collaborating across sectors in service of open science, precision oncology, and patients: an overview of the AACR Project GENIE (Genomics Evidence Neoplasia Information Exchange) Biopharma Collaborative (BPC) Data analytics for real-world data integration in TKI-treated NSCLC patients using electronic health records Cardiovascular toxicities in cancer patients treated with immune checkpoint inhibitors: multicenter study using natural language processing on Belgian hospital data Human epidermal growth factor receptor 2 (HER2) expression dynamics between diagnosis and recurrence in patients with breast cancer using artificial intelligence and electronic health records: the RosHER study
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1