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.

PMSI Trastuzumab artificial intelligence breast cancer claims database clustering early stage breast cancer observational study real-world evidence

Journal

Cancer informatics
ISSN: 1176-9351
Titre abrégé: Cancer Inform
Pays: United States
ID NLM: 101258149

Informations de publication

Date de publication:
2022
Historique:
received: 24 05 2022
accepted: 30 09 2022
entrez: 17 11 2022
pubmed: 18 11 2022
medline: 18 11 2022
Statut: epublish

Résumé

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. 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. 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. 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.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 36386278
doi: 10.1177/11769351221135134
pii: 10.1177_11769351221135134
pmc: PMC9661546
doi:

Types de publication

Journal Article

Langues

eng

Pagination

11769351221135134

Informations de copyright

© The Author(s) 2022.

Déclaration de conflit d'intérêts

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: OT reports grants and personal fees from Roche and MSD-Merck; grants from BMS; personal fees from Novartis-Sandoz, Pfizer, Lilly, Astra-Zeneca, Daiichi Sankyo, Seagen, Pierre Fabre, and Eisai, outside of the submitted work. JD, MG, and RG are Roche employees. AV, JLT, MP, and ML are HEVA employees, the company hired by Roche to carry out the study.

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Auteurs

Olivier Tredan (O)

Medical Oncology Department, Centre Léon Bérard, Lyon, France.

Marie Laurent (M)

HEVA, Lyon Cedex, France.

Melina Gilberg (M)

Roche S.A.S. Medical Affairs France, Boulogne-Billancourt Cedex, France.

Rim Ghorbal (R)

Roche S.A.S. Medical Affairs France, Boulogne-Billancourt Cedex, France.

Alexandre Vainchtock (A)

HEVA, Lyon Cedex, France.

Joannie Lortet-Tieulent (J)

HEVA, Lyon Cedex, France.

Martin Prodel (M)

HEVA, Lyon Cedex, France.

Julien Dupin (J)

Roche S.A.S. Medical Affairs France, Boulogne-Billancourt Cedex, France.

Classifications MeSH