Opportunities for personalised follow-up in breast cancer: the gap between daily practice and recurrence risk

Madelon Voets, Noa Hassink, Jeroen Veltman, Cornelis Herman Slump, Hendrik Koffijberg, Sabine Siesling*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Purpose: Follow-up guidelines barely diverge from a one-size-fits-all approach, even though the risk of recurrence differs per patient. However, the personalization of breast cancer care improves outcomes for patients. This study explores the variation in follow-up pathways in the Netherlands using real-world data to determine guideline adherence and the gap between daily practice and risk-based surveillance, to demonstrate the benefits of personalized risk-based surveillance compared with usual care.
Methods: Patients with stage I–III invasive breast cancer who received surgical treatment in a general hospital between 2005 and 2020 were selected from the Netherlands Cancer Registry and included all imaging activities during follow-up from hospital-based electronic health records. Process analysis techniques were used to map patients and activities to investigate the real-world utilisation of resources and identify the opportunities for improvement. The INFLUENCE 2.0 nomogram was used for risk prediction of recurrence.
Results: In the period between 2005 and 2020, 3478 patients were included with a mean follow-up of 4.9 years. In the first 12 months following treatment, patients visited the hospital between 1 and 5 times (mean 1.3, IQR 1–1) and received between 1 and 9 imaging activities (mean 1.7, IQR 1–2). Mammogram was the prevailing imaging modality, accounting for 70% of imaging activities. Patients with a low predicted risk of recurrence visited the hospital more often.
Conclusions: Deviations from the guideline were not in line with the risk of recurrence and revealed a large gap, indicating that it is hard for clinicians to accurately estimate this risk and therefore objective risk predictions could bridge this gap.
Keywords Breast cancer · Follow-up · Real-world data · Risk of recurrence · Process mining · Resource utilisation
Original languageEnglish
Pages (from-to)313-322
Number of pages10
JournalBreast cancer research and treatment
Volume205
Early online date26 Feb 2024
DOIs
Publication statusE-pub ahead of print/First online - 26 Feb 2024

Keywords

  • UT-Hybrid-D

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