Switching Inhalers:A Practical Approach to Keep on UR RADAR

Abstract The choice of an inhaler device is often as important as the medication put in it to achieve optimal outcomes for our patients with asthma and/or COPD. With a multitude of drug–device combinations available, optimization of respiratory treatment could well be established by switching devices rather than changing or even augmenting pharmacological or non-pharmacological therapies. Importantly, while notable between-device differences in release mechanism, particle size, drug deposition and required inspiratory flow exist, a patient uncomfortable with their device is unlikely to use it regularly and certainly will not use it properly. Switching requires a careful process and should not be done without patient consent. Switching devices entails several steps that need to be considered, which can be guided using the UR-RADAR mnemonic. It starts with (i) UncontRolled asthma/COPD (or UnaffoRdable device), followed by RADAR: (ii) review the patient’s condition (e.g. diagnosis, phenotype, co-morbidities) and address reasons for suboptimal control (e.g. triggers, smoking, non-adherence, poor inhaler technique) to be ruled out before switching; (iii) assess patient’s skills related to inhalation (e.g. inspiratory force); (iv) discuss inhaler switch options, patient preferences (e.g. size, daily regimen) and treatment goals; (v) allow patients input and use shared decision-making to decide final treatment choice, acknowledging individual patient skills, preferences and goals; and (vi) re-educate to the new device (at minimum, physical demonstration, verbal explanation and patient repetition, both verbally and physically) and prime the patient for the follow-up (i.e. explain the future patient journey, including multidisciplinary work flows with physicians, nurses and pharmacists)

Cost comparison of asthma treatments in 12-week study : caution about matching and short observational follow-up

This review was funded by Observational and Pragmatic Research Institute Pte Ltd.Peer reviewedPublisher PD

Shared decision making and medication adherence in patients with COPD and/or asthma:the ANANAS study

Background: Medication adherence to inhalation medication is suboptimal in patients with COPD and asthma. Shared decision making (SDM) is proposed as an intervention to improve medication adherence. Despite its wide promotion, evidence of SDM's association with greater medication adherence is scarce. Also, it is unknown to what degree patients presently experience SDM and how it is associated with medication adherence.Objective: To (i) assess the level of SDM and (ii) medication adherence, (iii) explore the relation between SDM and medication adherence and iv) investigate possible underlying mechanisms.Methods: Cross-sectional observational study. A survey was distributed among Dutch patients with COPD and/or asthma using inhaled medication. Medication adherence was measured using the Test of Adherence to Inhalers (TAI-10), and SDM by the 9-item Shared Decision-Making questionnaire (SMD-Q-9). Feeling of competence, relatedness and feeling of autonomy from the Self-Determination Theory (SDT) were considered as possible mechanisms. The primary outcome was adherence.Results: A total of 396 patients with complete information on relevant covariates were included. Mean SDM-Q-9 score was 26.7 (SD 12.1, range 0-45) and complete adherence was 41.2%. The odds ratio for the association of SDM with adherence was 1.01 (95% CI: 0.99, 1.02). This only changed minimally when adjusted for mediators (mediating effect &lt;3%).Conclusion: The patient experienced level of SDM in daily practice and medication adherence have room for improvement. No association between SDM and medication adherence was observed. Factors related to feeling of competence, relatedness and feeling of autonomy did not meaningfully explain this finding. </p

Predicting Short-term and Long-term HbA1c Response after Insulin Initiation in Patients with Type 2 Diabetes Mellitus using Machine Learning

AIM: To assess the potential of supervised machine learning techniques to identify clinical variables for predicting short-term and long-term glycated hemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We included patients with T2DM from the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007-2013 with a minimum follow-up of 2 years. Short-term and long-term response were defined at 6 (± 2) and 24 (± 2) months after insulin initiation, respectively. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and elastic net regularization technique was used for variables selection. The performance of three traditional machine learning algorithms was compared to predict short-term and long-term responses and the area under the receiver operator characteristic curve (AUC) was used to assess the performance of the prediction model. RESULTS: The elastic net regularization based generalized linear model, including baseline HbA1c and eGFR, correctly classified short-term and long-term HbA1c response after treatment initiation with an AUC (95% CI) = 0.80 (0.78 - 0.83) and 0.81 (0.79 - 0.84), respectively, and outperformed other machine learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (0.65 - 0.73) and 0.72 (0.66 - 0.75) was obtained for predicting short-term and long-term response, respectively. CONCLUSIONS: Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables. This article is protected by copyright. All rights reserved

Self-reported medication adherence instruments and their applicability in low-middle income countries:a scoping review

INTRODUCTION: Medication non-adherence is an important public health issue, associated with poor clinical and economic outcomes. Globally, self-reported instruments are the most widely used method to assess medication adherence. However, the majority of these were developed in high-income countries (HICs) with a well-established health care system. Their applicability in low- and middle-income countries (LMICs) remains unclear. The objective of this study is to systematically review the applicability of content and use of self-reported adherence instruments in LMICs.METHOD: A scoping review informed by a literature search in Pubmed, EBSCO, and Cochrane databases was conducted to identify studies assessing medication adherence using self-reported instruments for patients with five common chronic diseases [hypertension, diabetes, dyslipidemia, asthma, or Chronic Obstructive Pulmonary Disease (COPD)] in LMICs up to January 2022 with no constraints on publication year. Two reviewers performed the study selection process, data extraction and outcomes assessment independently. Outcomes focused on LMIC applicability of the self-reported adherence instruments assessed by (i) containing LMIC relevant adherence content; (ii) methodological quality and (iii) fees for use.FINDINGS: We identified 181 studies that used self-reported instruments for assessing medication adherence in LMICs. A total of 32 distinct types of self-reported instruments to assess medication adherence were identified. Of these, 14 self-reported instruments were developed in LMICs, while the remaining ones were adapted from self-reported instruments originally developed in HICs. All self-reported adherence instruments in studies included presented diverse potential challenges regarding their applicability in LMICs, included an underrepresentation of LMIC relevant non-adherence reasons, such as financial issues, use of traditional medicines, religious beliefs, lack of communication with healthcare provider, running out of medicine, and access to care. Almost half of included studies showed that the existing self-reported adherence instruments lack sufficient evidence regarding cross cultural validation and internal consistency. In 70% of the studies, fees applied for using the self-reported instruments in LMICs.CONCLUSION: There seems insufficient emphasis on applicability and methodological rigor of self-reported medication adherence instruments used in LMICs. This presents an opportunity for developing a self-reported adherence instrument that is suitable to health systems and resources in LMICs.SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022302215.</p