Innovative Approach for a Typology of Treatment Sequences in Early Stage HER2 Positive Breast Cancer Patients Treated With Trastuzumab in the French National Hospital Database.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-11-09 eCollection Date: 2022-01-01 DOI:10.1177/11769351221135134
Olivier Tredan, Marie Laurent, Melina Gilberg, Rim Ghorbal, Alexandre Vainchtock, Joannie Lortet-Tieulent, Martin Prodel, Julien Dupin
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引用次数: 1

Abstract

Background: Our objective was to describe the hospital-based systemic treatment sequences in early stage HER2+ breast cancer patients treated with trastuzumab in France in 2016.

Methods: This retrospective observational study was based on the national hospital discharge database (PMSI). Patients hospitalized for breast cancer in 2016 and administration of trastuzumab between 6 months prior and 1 year after surgery were included. The following treatments were identified: (1) trastuzumab ± chemotherapy; (2) chemotherapy alone; (3) q3w trastuzumab weekly chemotherapy. Hospital admissions for cardiac events before and after the surgery were investigated. An unsupervised machine learning technic called TAK (Time-sequence Analysis through K-clustering) was used to identify and visualize typical systemic treatment sequences.

Results: Overall, 3531 patients were included: 2619 adjuvant cohort patients (74.2%) and 912 neoadjuvant cohort patients (25.8%). The mean age was 56.4 years (±12.3), 99.7% patients were female. Treatment initiation occurred within 6 weeks of the surgery in 58% and 92% of patients, and trastuzumab treatment lasted 12 months (±1 month) in 75% and 66% of patients in the adjuvant and neoadjuvant cohorts, respectively. Nevertheless, 12% and 22% of patients were treated with trastuzumab for <11 months in the adjuvant and neoadjuvant cohorts, respectively. There was not one standard sequence of treatments per cohort, but 4 and 3 typical treatment sequences in the adjuvant and the neoadjuvant cohorts, respectively, plus 2 treatment sequences with an early treatment withdrawal. The frequency of patients with ⩾1 hospital stay with a cardiac event was higher among patients with an early treatment withdrawal.

Conclusions: The treatment sequences of most patients were in line with the recommendations in force. The machine learning approach provided a telling visual display of the results, thereby allowing healthcare professionals, health authorities, patients, and care givers to see the whole picture of the hospital-administered drug strategies.

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法国国家医院数据库中曲妥珠单抗治疗的早期HER2阳性乳腺癌患者治疗序列类型的创新方法
背景:我们的目的是描述2016年在法国接受曲妥珠单抗治疗的早期HER2+乳腺癌患者基于医院的全身治疗序列。方法:本回顾性观察研究基于国家医院出院数据库(PMSI)。纳入了2016年因乳腺癌住院并在手术前6个月至术后1年内使用曲妥珠单抗的患者。确定以下治疗方法:(1)曲妥珠单抗±化疗;(2)单纯化疗;(3)每周曲妥珠单抗化疗q3w。调查了手术前后心脏事件的住院情况。一种称为TAK(通过k -聚类的时间序列分析)的无监督机器学习技术被用于识别和可视化典型的系统治疗序列。结果:共纳入3531例患者:辅助队列患者2619例(74.2%),新辅助队列患者912例(25.8%)。平均年龄56.4岁(±12.3岁),女性占99.7%。在辅助治疗组和新辅助治疗组中,58%和92%的患者在手术后6周内开始治疗,75%和66%的患者曲妥珠单抗治疗持续了12个月(±1个月)。然而,12%和22%的患者接受了曲妥珠单抗治疗。结论:大多数患者的治疗顺序符合现行建议。机器学习方法为结果提供了生动的视觉显示,从而使医疗保健专业人员、卫生当局、患者和护理人员能够看到医院管理药物策略的全貌。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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