ArticlePharmacy (Basel, Switzerland)2024
Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis.
Article in Pharmacy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are novel oral anti-hyperglycemic drugs that demonstrate cardiovascular and metabolic benefits for patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD). There is limited knowledge of real-world data to predict adherence to SGLT-2i in an ambulatory setting. The study aims to predict SGLT-2i adherence in patients with T2D and/or HF and/or CKD by building a prediction model using electronic prescription claims data presented within EPIC datasets. This is a retrospective study of 174 adult patients prescribed SGLT-2i at UC San Diego Health ambulatory pharmacies between 1 January 2020 to 30 April 2021. Adherence was measured by the proportion of days covered (PDC). R packages were used to identify regression and non-linear regression predictive models to predict adherence. Age, gender, race/ethnicity, hemoglobin A1c, and insurance plan were included in the model. Diabetes control based on hemoglobin A1c (HbA1c) and the glomerular filtration rate (GFR) was also evaluated using Welch
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.