Suppr超能文献

在线讨论乳腺癌幸存者的药物副作用和停药问题。

Online discussion of drug side effects and discontinuation among breast cancer survivors.

机构信息

Department of Family Medicine and Community Health, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.

出版信息

Pharmacoepidemiol Drug Saf. 2013 Mar;22(3):256-62. doi: 10.1002/pds.3365. Epub 2013 Jan 16.

Abstract

PURPOSE

While patients often use the internet as a medium to search for and exchange health-related information, little is known about the extent to which patients use social media to discuss side effects related to medications. We aim to understand the frequency and content of side effects and associated adherence behaviors discussed by breast cancer patients related to using aromatase inhibitors (AIs), with particular emphasis on AI-related arthralgia.

METHODS

We performed a mixed methods study to examine content related to AI associated side effects posted by individuals on 12 message boards between 2002 and 2010. We quantitatively defined the frequency and association between side effects and AIs and identified common themes using content analysis. One thousand randomly selected messages related to arthralgia were coded by two independent raters.

RESULTS

Among 25 256 posts related to AIs, 4589 (18.2%) mentioned at least one side effect. Top-cited side effects on message boards related to AIs were joint/musculoskeletal pain (N = 5093), hot flashes (1498), osteoporosis (719), and weight gain (429). Among the authors posting messages who self-reported AI use, 12.8% mentioned discontinuing AIs, while another 28.1% mentioned switching AIs. Although patients often cited severe joint pain as the reason for discontinuing AIs, many also offered support and advice for coping with AI-associated arthralgia.

CONCLUSION

Online discussion of AI-related side effects was common and often related to drug switching and discontinuation. Physicians should be aware of these discussions and guide patients to effectively manage side effects of drugs and promote optimal adherence.

摘要

目的

尽管患者通常将互联网作为搜索和交流健康相关信息的媒介,但对于患者在多大程度上使用社交媒体讨论与药物相关的副作用知之甚少。我们旨在了解乳腺癌患者在使用芳香化酶抑制剂(AIs)时讨论副作用及其相关服药依从性的频率和内容,特别强调与 AI 相关的关节痛。

方法

我们开展了一项混合方法研究,以检查 2002 年至 2010 年间个人在 12 个留言板上发布的与 AI 相关副作用相关内容。我们使用内容分析法定量定义了副作用与 AI 之间的频率和关联,并确定了常见主题。两位独立的评估者对与关节痛相关的 1000 条随机选择的信息进行了编码。

结果

在 25256 条与 AI 相关的帖子中,有 4589 条(18.2%)提到了至少一种副作用。留言板上与 AI 相关的副作用中最常被引用的是关节/肌肉骨骼疼痛(N=5093)、热潮红(1498)、骨质疏松症(719)和体重增加(429)。在自我报告使用 AI 的发布消息的作者中,12.8%提到停止使用 AI,而另有 28.1%提到改用 AI。尽管患者经常因严重关节痛而停止使用 AI,但许多人也为应对 AI 相关关节痛提供了支持和建议。

结论

在线讨论 AI 相关副作用很常见,通常与药物更换和停药有关。医生应该了解这些讨论,并指导患者有效管理药物的副作用,促进最佳的服药依从性。

相似文献

1
Online discussion of drug side effects and discontinuation among breast cancer survivors.
Pharmacoepidemiol Drug Saf. 2013 Mar;22(3):256-62. doi: 10.1002/pds.3365. Epub 2013 Jan 16.
2
Ageing perceptions and non-adherence to aromatase inhibitors among breast cancer survivors.
Eur J Cancer. 2018 Mar;91:145-152. doi: 10.1016/j.ejca.2017.12.006. Epub 2018 Jan 9.
3
Side effects of aromatase inhibitors versus tamoxifen: the patients' perspective.
Am J Surg. 2006 Oct;192(4):496-8. doi: 10.1016/j.amjsurg.2006.06.018.
4
Improving Adherence to Endocrine Therapy in Women With HR-Positive Breast Cancer.
Oncology (Williston Park). 2018 May 15;32(5):235-7, 249.
5
[Aromatase inhibitors and arthralgia].
Magy Onkol. 2011 Mar;55(1):32-9. Epub 2011 Mar 31.
6
Aromatase inhibitor-associated arthralgia syndrome.
Breast. 2007 Jun;16(3):223-34. doi: 10.1016/j.breast.2007.01.011. Epub 2007 Mar 21.
8
Molecular basis of aromatase inhibitor associated arthralgia: known and potential candidate genes and associated biomarkers.
Expert Opin Drug Metab Toxicol. 2017 Feb;13(2):149-156. doi: 10.1080/17425255.2017.1234605. Epub 2016 Sep 20.

引用本文的文献

1
Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques.
Heliyon. 2024 Sep 26;10(19):e38563. doi: 10.1016/j.heliyon.2024.e38563. eCollection 2024 Oct 15.
4
Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning.
Healthcare (Basel). 2022 Mar 25;10(4):618. doi: 10.3390/healthcare10040618.
7
Active neural networks to detect mentions of changes to medication treatment in social media.
J Am Med Inform Assoc. 2021 Nov 25;28(12):2551-2561. doi: 10.1093/jamia/ocab158.
8
Therapeutic potential of thymoquinone in combination therapy against cancer and cancer stem cells.
World J Clin Oncol. 2021 Jul 24;12(7):522-543. doi: 10.5306/wjco.v12.i7.522.

本文引用的文献

1
Identifying potential adverse effects using the web: a new approach to medical hypothesis generation.
J Biomed Inform. 2011 Dec;44(6):989-96. doi: 10.1016/j.jbi.2011.07.005. Epub 2011 Jul 26.
3
Pharmaceutical marketing and the new social media.
N Engl J Med. 2010 Nov 25;363(22):2087-9. doi: 10.1056/NEJMp1004986.
4
Differences in information seeking among breast, prostate, and colorectal cancer patients: results from a population-based survey.
Patient Educ Couns. 2010 Dec;81 Suppl:S54-62. doi: 10.1016/j.pec.2010.09.010. Epub 2010 Oct 8.
5
Internet use leads cancer patients to be active health care consumers.
Patient Educ Couns. 2010 Dec;81 Suppl(0):S63-9. doi: 10.1016/j.pec.2010.09.004.
6
Early discontinuation and non-adherence to adjuvant hormonal therapy are associated with increased mortality in women with breast cancer.
Breast Cancer Res Treat. 2011 Apr;126(2):529-37. doi: 10.1007/s10549-010-1132-4. Epub 2010 Aug 28.
8
Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients.
J Clin Oncol. 2010 Sep 20;28(27):4120-8. doi: 10.1200/JCO.2009.25.9655. Epub 2010 Jun 28.
9
The missing voice of patients in drug-safety reporting.
N Engl J Med. 2010 Mar 11;362(10):865-9. doi: 10.1056/NEJMp0911494.
10
Weight gain is associated with increased risk of hot flashes in breast cancer survivors on aromatase inhibitors.
Breast Cancer Res Treat. 2010 Nov;124(1):205-11. doi: 10.1007/s10549-010-0802-6. Epub 2010 Feb 25.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验