scholarly journals AN OVERVIEW OF THE NOTTINGHAM RESEARCH PROGRAMME ON PRIMARY BREAST CANCER IN OLDER WOMEN

2021 ◽  
Vol 3 (3) ◽  
pp. 49-52
Author(s):  
Ruth Parks ◽  
Kwok-Leung Cheung
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Older Women ◽  
Geriatric Assessment ◽  
Primary Breast Cancer ◽  
Treatment Decision ◽  
Research Programme ◽  
Complex Needs ◽  
Treatment Decision Making

The incidence of breast cancer increases with age. Despite this, most research in this field is aimed at the younger patient. Preliminary studies have shown that older women with breast cancer have distinct biological features compared to their younger counterparts. In addition, the focus of treatment of older women may not simply be curative but may be a trade-off between risks of treatment and impact on independence and quality of life. The Nottingham research programme is a unique programme dedicated to primary breast cancer in older women. There are two arms of the research programme 1) determining the biological differences of breast cancer in this cohort 2) exploring the use of geriatric assessment to understand the complex needs and factors contributing to treatment decision making in this group of patients. The overall aim of the research programme is to optimise both the biological and geriatric assessment of older women with primary breast cancer, to provide personalised data at diagnosis, on likely survival and quality of life outcomes following breast cancer treatment. This article will outline why this research programme is important, what it has achieved so far and future aspirations.

scholarly journals Why do Older Women with Invasive Breast Cancer Avoid Surgery? A Qualitative Analysis of Factors Influencing the Decision of Patients and Physicians

2021 ◽  
Author(s):  
Fernando A. Angarita ◽  
Ethan Hoppe ◽  
Gary Ko ◽  
Justin Lee ◽  
Danny Vesprini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Decision Making ◽  
Older Women ◽  
Invasive Breast Cancer ◽  
Treatment Decision ◽  
Patient Centered ◽  
Treatment Decision Making ◽  
Cancer Specialists

Abstract Purpose: Limited data exists about why older women (≥70 years old) with breast cancer avoid surgery. This study aimed to identify physician- and patient-perceived attitudes that influence the decision to avoid surgery among older women with invasive breast cancer.Methods: Semi-structured in-depth interviews were conducted with multidisciplinary breast cancer specialists and with older women with breast cancer who declined surgery. Transcripts were iteratively coded using a theoretical framework to guide identification of common themes. Thematic comparison was performed between patients and physicians. Results: Ten breast cancer specialists and eleven patients participated. Physicians believed older women declined surgery because they did not perceive their breast cancer as a life-threatening ailment compared to other medical comorbidities. Physicians did not discuss breast reconstruction, as it was perceived to be unimportant. Treatment side effects, length of treatment, impact on quality of life, and minimal survival benefit strongly influenced a patient’s decision to decline surgery. Patients valued independence and quality of life over quantity of life. Patients felt empowered to participate in the decision-making process but appreciated having support. Both groups had congruent beliefs with respect to age impacting treatment decision, cosmesis playing a minor factor in treatment decisions, and importance of quality of life; however, they were discordant in their perceptions about the amount of support that patients have from their families.Conclusion: The decision to avoid surgery in older women stems from a variety of individual beliefs. Acknowledging patient values early in treatment planning may facilitate a patient-centered approach to treatment decision making.

scholarly journals The quality of life index: a pilot study integrating treatment efficacy and quality of life in oncology

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
A. Basu ◽  
E. J. Philip ◽  
B. Dewitt ◽  
J. Hanmer ◽  
A. Chattopadhyay ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Treatment Decision ◽  
Therapeutic Agents ◽  
Patient Centered ◽  
Life Index ◽  
Treatment Decision Making ◽  
Related Quality ◽  
Quality Of Life Index

Abstract The majority of women diagnosed with breast cancer will experience some form of drug-related toxicity and subsequent impairments in Health-related Quality of Life (HRQoL). Despite this, HRQoL is assessed inconsistently and there is no validated method to integrate HRQoL data into the assessment of therapeutic agents. This proof of concept study utilizes data from the neoadjuvant I-SPY 2 clinical trial to describe the development of the Quality of Life Index (QoLI) measure. The QoLI represents a single composite score that incorporates validated longitudinal measures of clinical efficacy and QoL and one that permits a more comprehensive, direct comparison of individual therapeutic agents. Preliminary data suggest the QoLI is able to distinguish between agents based on their efficacy and toxicity; with further validation, the QoLI has the potential to provide more patient-centered evaluations in clinical trials and help guide treatment decision making in breast cancer and other oncologic diseases.

scholarly journals Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare

Stats ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 776-792
Author(s):  
Yingchao Zhong ◽  
Chang Wang ◽  
Lu Wang
Keyword(s):  
Quality Of Life ◽  
Patient Preference ◽  
Treatment Strategies ◽  
Treatment Decision ◽  
Personalized Treatment ◽  
Model Framework ◽  
Q Learning ◽  
Treatment Decision Making ◽  
Weighted Utility

In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as chronic kidney disease, which typically consists of sequential and adaptive treatment decision making. We investigate a two-stage treatment setting with a survival outcome that could be right censored. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual based on their own medical history in order to maximize a desirable health outcome. We develop a new method, Survival Augmented Patient Preference incorporated reinforcement Q-Learning (SAPP-Q-Learning) to decide between quality of life and survival restricted at maximal follow-up. Our method incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide personalized treatment strategies.

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