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BackgroundDespite effective treatments, more than 30% of patients with diabetes will present with diabetic kidney disease (DKD) at some point. Patients with DKD are among the most complex as their care is multifactorial and involves different groups of health care providers. Suboptimal adherence to polypharmacy is frequent and contributes to poor outcomes. As self-management is one of the keys to clinical success, structured medication adherence programs are crucial. The PANDIA-IRIS (patients diabétiques et insuffisants rénaux: un programme interdisciplinaire de soutien à l’adhésion thérapeutique) study is based on a routine medication adherence program led by pharmacists. ObjectiveThe aim of this study is to define the impact of the duration of this medication adherence program on long-term adherence and clinical outcomes in patients with DKD. MethodsThis monocentric adherence program consists of short, repeated motivational interviews focused on patients’ medication behaviors combined with the use of electronic monitors containing patients’ medications. When patients open the electronic monitor cap to take their medication, the date and hour at each opening are registered. In total, 73 patients are randomized as 1:1 in 2 parallel groups; the adherence program will last 6 months in the first group versus 12 months in the second group. After the intervention phases, patients continue using their electronic monitors for a total of 24 months but without receiving feedback. Electronic monitors and pill counts are used to assess medication adherence. Persistence and implementation will be described using Kaplan-Meier curves and generalized estimating equation multimodeling, respectively. Longitudinal adherence will be presented as the product of persistence and implementation and modelized by generalized estimating equation multimodeling. The evolution of the ADVANCE (Action in Diabetes and Vascular disease: Preterax and Diamicron Modified-Release Controlled Evaluation) and UKPDS (United Kingdom Prospective Diabetes Study) clinical scores based on medication adherence will be analyzed with generalized estimating equation multimodeling. Patients’ satisfaction with this study will be assessed through qualitative interviews, which will be transcribed verbatim, coded, and analyzed for the main themes. ResultsThis study was approved by the local ethics committee (Vaud, Switzerland) in November 2015. Since then, 2 amendments to the protocol have been approved in June 2017 and October 2019. Patients’ recruitment began in April 2016 and ended in October 2020. This study was introduced to all consecutive eligible patients (n=275). Among them, 73 accepted to participate (26.5%) and 202 (73.5%) refused. Data collection is ongoing and data analysis is planned for 2022. ConclusionsThe PANDIA-IRIS study will provide crucial information about the impact of the medication adherence program on the adherence and clinical outcomes of patients with DKD. Monitoring medication adherence during the postintervention phase is innovative and will shed light on the duration of the intervention on medication adherence. Trial RegistrationClinicaltrials.gov NCT04190251_PANDIA IRIS; https://clinicaltrials.gov/ct2/show/NCT04190251 International Registered Report Identifier (IRRID)DERR1-10.2196/25966

Background: This study aims to create a comprehensive framework for the development and implementation of digital medication adherence technologies (DMATech), focusing on critical stages where engagement of medication users (MU) is considered meaningful, i.e. adds significant value, as agreed upon by participating stakeholders. Methods: Through a literature review and expert consensus, a framework was outlined covering key DMATech development and implementation phases and steps. An in-person workshop with MU representatives and adherence experts, using the Nominal Group Technique, further refined these stages for MU engagement. Results: The DMATech framework included three phases: 'Innovation,' 'Research and Development,' and 'Launch and Implementation,' each encompassing multiple steps. The workshop, attended by five MU representatives and nine adherence experts, identified critical stages for MU input including context analysis, ideation, proof of concept, prototype creation, DMATech's iteration, critical evaluation, healthcare implementation, real-world assessment, and improvement. Nevertheless, there was a divergence of consensus regarding the importance of MUs engagement in regulatory, financial, and marketing aspects. Conclusions: This study provides a holistic framework for DMATech development and implementation and underscores the necessity of MU engagement at various stages. Modes of MU engagement cannot be generalized; a case-by-case evaluation of engagement strategies is essential. [ABSTRACT FROM AUTHOR]

Objective: To assess the effectiveness of an interprofessional support program (Siscare) that includes motivational interviews (patient-pharmacist), electronic monitoring (EM) of medications, patient-reported and clinical outcomes monitoring, and interactions with physicians for patients with type 2 diabetes in French-speaking Switzerland. Methods: This was a prospective, multicenter, observational cohort study using a hybrid implementation-effectiveness design. Individual daily adherence to at least one oral antidiabetic medication was measured by EM. A global adherence score was estimated by the product of a model-estimated implementation and a nonparametric estimate of persistence over time. Clinical outcomes (A1C, blood glucose, BMI, blood pressure, heart rate, and cholesterol levels) and quality of life (QoL) were analyzed over time using linear mixed-effect models. Results: A total of 212 patients were included from 27 pharmacies; 120 patients (57%) were followed up for at least 15 months. In total, 140 patients (66%) were male, the mean age was 64 ± 11 years, and the mean number of chronic medications per patient at baseline was 5 ± 3. Of 178 patients who used EM, 95% (95% CI 92–99%) remained persistent at the end of the follow-up period. The percentage of persistent patients taking their medications appropriately (implementation) was stable during follow-up and was estimated to be 90% (95% CI 87–92%) at baseline and 88% (95% CI 84–91%) at month 15. At baseline, the mean A1C and BMI were 7.5% and 31 kg/m2, respectively, which decreased by 0.5% (P = 0.012) and 0.6 kg/m2 (P = 0.017), respectively, after 15 months. QoL remained stable during follow-up. Conclusion: The program supports medication adherence and improves clinical outcomes, illustrating the overall preventive effect of coordinated care. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Swiss HIV Cohort Study (SHCS). Daily electronic medication adherence between 2008–2016 were analyzed retrospectively. Predictor variables included: RNA viral load (VL), CD4 count, duration of ART, and adherence. Random Forest, was used with 10 fold cross validation to predict the RNA class for each data observation. Classification accuracy metrics were calculated for each of the 10-fold cross validation holdout datasets. The values for each range from 0 to 1 (better accuracy). 383 HIV+ patients, 56% male, 52% white, median (Q1, Q3): age 43 (36, 50), duration of electronic monitoring of adherence 564 (200, 1333) days, CD4 count 406 (209, 533) cells/mm3, time since HIV diagnosis was 8.4 (4, 13.5) years, were included. Average model classification accuracy metrics (AUC and F1) for RNA VL were 0.6465 and 0.7772, respectively. In conclusion, combining adherence with other clinical predictors improve predictions of RNA. [ABSTRACT FROM AUTHOR]