FATIGUE PREVALENCE AND ADHERENCE TO TREATMENT: A REAL-WORLD DATA SURVEY AND MATHEMATICAL MODEL APPLICATION
D.A. Vorobiof, E. Malki, I. Deutsch, M. Bivas-Benita
Belong.life Inc, New York, NY, USA
Background & Methods
Patient-reported outcomes (PRO) address a critical need in value assessment of cancer treatments, outcomes, costs and quality of life (QOL). It is a broad representation of patients and can provide insights that are not available from clinical trials. Belong PPN is a social network for cancer patients and caregivers. It provides patients with access to other patients, healthcare professionals and disease management tools. In this study, we used PRO from the Belong patient-powered network (PPN) to explore the patient journey and treatment lines patterns of metastatic pancreatic cancer (MPC) patients in Israel.
Conclusions & future prospects
This survey describes the prevalence and adverse impact of severe fatigue present in certain cancer pts subgroups (advanced breast and lung cancers) which can alter significantly their adherence to planned treatments. Uniquely, while poor treatment adherence was observed in some cancer diagnosis, most of the patients who experienced mild to moderate fatigue maintained their treatment schedule. Effective strategies and efforts should aim to solve this common side effect and its deleterious consequences.
Future research will include machine learning methods, such as decision tree models that enable distinct predictive associations for different measured parameters. This could become a valuable tool for side-effects prediction and lead to early detection, effective treatment and better patients’ outcomes.
505 replies were received from pts (85%) and caregivers (15%). The data was then extracted from the digital platform and analyzed. A statistical mathematical predictive model was utilized. A machine learning analytical model was programmed to obtain the results. The most common diagnosis were Breast Cancer, lung and colorectal cancer (Figure 1). 67% of the pts were on active treatment at the time of the survey and 12% finished the treatment less than 6 months before (Figure 2). 66% of the pts experienced daily fatigue (described as mild, moderate and severe) and 17% experienced it weekly (Figure 3). As a direct result of fatigue, 10% of all pts reported that their ongoing treatments were delayed, stopped or changed (poor adherence; Figure 4). Patients were also asked about the physical impact fatigue has on them and rated it from 1 (no fatigue) to 5(severe fatigue). 137 (27%) of the total number of replies (mainly advanced breast and lung cancers pts) reported severe fatigue and 19% of them confirmed poor treatment adherence (Figure 5). However, better adherence was seen in the subgroup of pts which experienced mild to moderate fatigue.
New machine learning insights
One example for such an association was found in this study for cervix uteri cancer patients reporting fatigue that significantly affected their physical condition and their chance for developing depression.
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Disclaimer: The survey was conceived and implemented by Belong.life on a pro bono basis, no fees were paid to Belong.life. The survey results presented in this report are not statistically valid and should not be considered as an accurate reflection of the opinions of the respondents. Neither the questions nor the survey methodologies conform to standard practices. Participant responses have not been verified. This survey is for information purposes only. Due to sample size, results should be seen as issues for further discussion, not complete representations. We assert that any business or investment decisions should not be made solely based on the information presented in the survey. Although care has been taken to ensure complete and accurate survey results, recipients of this survey accept the possibility of unintended errors or omissions. The provided data is summarized and anonymized and in no event includes personal information.
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