Association between adherence to oral therapies in cancer patients and clinical outcome: A systematic review of the literature

Lasala, Ruggero; Santoleri, Fiorenzo

doi:10.1111/bcp.15147

Cited by 11 publications

(7 citation statements)

References 67 publications

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“…Так, отсутствие или низкая приверженность лечению меняют процессы фармакокинетики и фармакодинамики перорального препарата, что, в свою очередь, влияет на клинические исходы и течение заболевания [36]. Наиболее полно характеризуют рассматриваемый вопрос приверженности и клинических исходов в исследованиях с включением пациентов с хроническим миелоидным лейкозом (ХМЛ) [37][38][39]. Корреляция приверженности лечению пациентов с ХМЛ и ответа на терапию научно доказана и не вызывает сомнений, что нашло отражение в клинических рекомендациях по данному заболеванию [40].…”

Section: приверженность и клинические исходыunclassified

Key aspects of medication adherence to oral anticancer drugs: A literature review

Agafonova,

Snegovoy,

Omelyanovskiy

2024

Medicinskij sovet

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At least 25% of the anticancer drugs are intended for oral use, while the creation and rapid introduction of oral drugs into clinical practice is constantly growing. Between 2006 and 2021, 80 oral forms of anticancer drugs were approved. It has been shown that patients prefer oral therapy to intravenous therapy if such a replacement is possible with equal efficacy and toxicity of the drugs. This once again emphasizes that the issue of adherence to treatment with oral forms is open and relevant for study. Currently, adherence, as the main factor in achieving the best clinical outcome, has been studied from different perspectives. However, there are no structured data globally, and, accordingly, evidence-based and specific recommendations in the absence of adherence. The review includes data from studies performed over the past 10 years, studying the adherence of patients to treatment with oral anticancer drugs. The data sources for the review were the Medline, Embase, and Scopus databases. Literature analysis showed that the most important aspects in the field of adherence to treatment with oral anticancer drugs include the level of patient adherence to treatment, factors affecting non-compliance, correlation of treatment adherence and clinical outcomes in malignant neoplasms. At the same time, the article considers the impact of non-compliance on the health care system as a whole. The most reliable and up-to-date data in the study area were examined to subsequently identify measures aimed at achieving the best practice in the organization of healthcare for patients with malignant neoplasms. The research results demonstrate the variability and heterogeneity of data, the lack of unified methodological approaches. In turn, this prevents the formation of objective and reliable conclusions about adherence. To make rational strategic decisions on monitoring adherence to treatment, it is necessary to initiate large-scale studies, adopt uniform standard procedures and recommendations for assessing adherence.

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Section: приверженность и клинические исходыunclassified

Key aspects of medication adherence to oral anticancer drugs: A literature review

Agafonova,

Snegovoy,

Omelyanovskiy

2024

Medicinskij sovet

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“…Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ].…”

Section: Introductionmentioning

confidence: 99%

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Nishioka,

Watabe,

Yanagisawa

et al. 2024

J Med Internet Res

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Background Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed. Methods Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives. Conclusions Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

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“…Persistence in treatment is the time between the first and the last dose taken [7]. Achieving optimal adherence is paramount to reach targeted clinical outcomes [8], as nonadherence to OATs increases drug resistance, leads to treatment failure [9,10] and decreases survival [11][12][13]. According to the literature, it is estimated that one-quarter to one-third of patients do not implement or persist with OATs [14,15] due to a large variety of clinical, personal and contextual determinants (i.e., adverse events, toxicity, regimen complexity, low perceived need for OATs at a distance from diagnosis, forgetfulness, and high out-of-pocket costs in some countries) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A pharmacist-led interprofessional medication adherence program improved adherence to oral anticancer therapies: The OpTAT randomized controlled trial

Bandiera,

Cardoso,

Locatelli

et al. 2024

PLoS ONE

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Background Oral anticancer therapies such as protein kinase inhibitors (PKIs) are increasingly prescribed in cancer care. We aimed to evaluate the impact of a pharmacist-led interprofessional medication adherence program (IMAP) on patient implementation (dosing history), persistence (time until premature cessation of the treatment) and adherence to 27 PKIs prescribed for various solid cancers, as well as the impact on patients’ beliefs about medicines (BAM) and quality of life (QoL). Methods Patients (n = 118) were randomized 1:1 into two arms. In the intervention arm, pharmacists supported patient adherence through monthly electronic and motivational feedback, including educational, behavioral and affective components, for 12 months. The control arm received standard care plus EM without intervention. All PKIs were delivered in electronic monitors (EMs). Medication implementation and adherence were compared between groups using generalized estimating equation models, in which relevant covariables were included; persistence was compared with Kaplan‒Meier curves. Information on all treatment interruptions was compiled for the analysis. Questionnaires to evaluate BAM and QoL were completed among patients who refused and those who accepted to participate at inclusion, 6 and 12 months post-inclusion or at study exit. Results Day-by-day PKI implementation was consistently higher and statistically significant in the intervention arm (n = 58) than in the control arm (n = 60), with 98.1% and 95.0% (Δ3.1%, 95% confidence interval (CI) of the difference 2.5%; 3.7%) implementation at 6 months, respectively. The probabilities of persistence and adherence were not different between groups, and no difference was found between groups for BAM and QoL scores. No difference in BAM or QoL was found among patients who refused versus those who participated. The intervention benefited mostly men (at 6 months, Δ4.7%, 95% CI 3.4%; 6.0%), those younger than 60 years (Δ4.0%, 95% CI 3.1%; 4.9%), those who had initiated PKI more than 60 days ago before inclusion (Δ4.5%, 95% CI 3.6%; 5.4%), patients without metastasis (Δ4.5%, 95% CI 3.4%; 5.7%), those who were diagnosed with metastasis more than 2 years ago (Δ5.3%, 95% CI 4.3%; 6.4%) and those who had never used any adherence tool before inclusion (Δ3.8%, 95% CI 3.1%; 4.5%). Conclusions The IMAP, led by pharmacists in the context of an interprofessional collaborative practice, supported adherence, specifically implementation, to PKIs among patients with solid cancers. To manage adverse drug events, PKI transient interruptions are often mandated as part of a strategy for treatment and adherence optimization according to guidelines. Implementation of longer-term medication adherence interventions in the daily clinic may contribute to the improvement of progression-free survival. Trial registration ClinicalTrials.gov NCT04484064.

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Association between adherence to oral therapies in cancer patients and clinical outcome: A systematic review of the literature

Cited by 11 publications

References 67 publications

Key aspects of medication adherence to oral anticancer drugs: A literature review

Key aspects of medication adherence to oral anticancer drugs: A literature review

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

A pharmacist-led interprofessional medication adherence program improved adherence to oral anticancer therapies: The OpTAT randomized controlled trial

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