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Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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Dynamic Projection of Medication Nonpersistence and Nonadherence Among Patients With Early Breast Cancer
Pierre Rinder1, Théo Marcille1, Paul Sinel-Boucher1
1Sêmeia, Paris, France.
JAMA Network Open
|May 17, 2024
Summary
Predicting nonadherence in early breast cancer patients is crucial. A deep learning model identified age and past adherence as key factors, offering tools to improve oral anticancer treatment persistence.
Area of Science:
- Oncology
- Pharmacology
- Data Science
Background:
- Oral endocrine treatments are vital for early breast cancer management.
- Adherence challenges are significant, impacting treatment effectiveness.
- 17% of patients experience nonpersistence and 41% nonadherence.
Purpose of the Study:
- To develop a model for predicting persistence and adherence to oral anticancer treatment.
- To identify factors associated with nonpersistence and nonadherence in localized breast cancer patients.
Main Methods:
- A deep learning model (gated-recurrent unit architecture) was employed.
- Analysis utilized anonymous French Health Insurance reimbursement data (2013-2018).
- The model predicted nonpersistence and nonadherence episodes 6 months in advance.
Main Results:
- The model achieved an area under the curve of 0.71 for persistence and 0.73 for adherence.
- Older age (>70 years), prior nonadherence, polypharmacy, and low income increased nonpersistence risk.
- Age and past adherence patterns were significant predictors of nonadherence.
Conclusions:
- Patient age and history of adherence are associated with nonpersistence and nonadherence.
- Regular treatment purchase intervals correlate with enhanced adherence.
- This study provides tools to improve oral anticancer treatment adherence in early breast cancer.
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