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Record W6945944119 · doi:10.26190/unsworks/30079

Improving the Uptake of Evidence-based Therapy in Chronic Kidney Disease and Diabetes

2024· dissertation· en· W6945944119 on OpenAlexaboutno aff

Bibliographic record

VenueUNSWorks (University of New South Wales, Sydney, Australia) · 2024
Typedissertation
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilAustralian Government
KeywordsKidney diseaseGeneralizability theoryDiabetes mellitusPopulationDiseaseCohortIncidence (geometry)Clinical trial

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD), cardiovascular disease, and diabetes are major health challenges in Canada. In the last 5 years, a series of large randomized controlled trials (RCTs) have demonstrated that sodium-glucose cotransporter-2 inhibitors (SGLT2is) reduce the risk of kidney failure, cardiovascular mortality, and hospitalization for heart failure in people with albuminuric CKD irrespective of diabetes status. Ensuring SGLT2is are optimally used provides a key means for Canada to respond to these looming population health challenges. Exploring the generalizability of these landmark clinical trials to understand the potential impact of SGLT2i use in a Canadian population helps establish the support for their widespread use. A cohort of patients under the care of nephrologists in the province of British Columbia was compared with the participants in two landmark SGLT2i RCTs in order to characterize the burden of disease and explore the generalizability of the findings to the people in British Columbia with CKD. The study indicated that the British Columbia CKD population was older, with a greater proportion of females, and a higher risk group with worse outcomes than the cohorts in the clinical trials. Given the discrepancies in age and sex between the British Columbia CKD population and the trial cohorts, a literature review was firstly conducted to better understand the interaction between sex and the burden and progression of cardiovascular disease in those with CKD. The narrative review highlighted that the prevalence and incidence of cardiovascular disease in patients with CKD by sex is not well described. It also illustrated that all patients with CKD, regardless of sex, are at an elevated risk for cardiovascular disease thus creating a compelling case for the use of effective treatments to reduce this risk. A secondary analysis of a landmark SGLT2i trial was conducted according to age categories and sex to explore whether age or sex led to differences in treatment effect or adverse events. This analysis supports the safety and efficacy of canagliflozin, an SGLT2i, with consistent benefits in kidney and cardiovascular outcomes with minimal differences in the risk of adverse events in patients with CKD across a wide age span and in both males and females. This newly established evidence supports the efforts to increase SGLT2i use across the spectrum of age and both sexes in the British Columbia CKD population. Poor medication adherence is a frequently neglected challenge in healthcare that prevents full realization of the benefits of evidence-based therapies. Forgetting to take medication was identified as one of the most significant patient-reported reason for suboptimal medication adherence in the general CKD literature, with evidence of decline in adherence to SGLT2is to 50-70% within the first year. To better understand additional factors that affect the loss of medication adherence and the challenges in management of SGLT2is, a study was conducted to interview clinicians in British Columbia to determine the barriers and facilitators to utilizing SGLT2is in the local context. The major findings from the clinicians were that some patients do not tolerate adverse effects such as mycotic genital infections and have difficulty remembering information regarding SGLT2is, but many were enthusiastic about the potential benefits. This generated the hypothesis that medication taking reminders and repeated education may be a strategy to improve medication adherence. Given the large geographic area and varying available resources in British Columbia, accessibility and scalability are key practical considerations for an adherence intervention targeting this population. A review of digital and mobile health (mHealth) interventions was conducted in search of a potential intervention that could accommodate these design requirements. The review highlighted examples of effective integration of mHealth into clinical medicine, such as mobile alert detecting acute kidney injury in hospitalized patients resulting in substantial cost savings per patient. Leveraging the ubiquity and low cost of mobile phone communication, a text messaging intervention study was developed with the goal of improving medication adherence to SGLT2is in British Columbia. This hybrid effectiveness-implementation study will determine the effectiveness of a 12-month text message-based intervention covering content regarding the benefits of SGLT2is, adverse reactions, and general health advice and medication reminders. The trial will recruit patients with CKD, cardiovascular disease and/or diabetes, starting a new SGLT2i prescription, and with access to a smart phone. The primary endpoint is medication adherence to SGLT2is at 12 months with secondary assessments at 24 months to assess persistence of any effect after cessation of the intervention. The primary outcome will be measured by mean proportion of days covered (PDC) with an active prescription at 12 months, and implementation outcomes will assess any barriers and facilitators to the incorporation of the text messaging intervention in the local setting. Overall, this thesis describes in detail the potential for SGLT2is to reduce the burden of kidney and cardiovascular disease in the context of a Canadian provincial population. A means of maximizing the benefit of these medications via improved adherence is described and serves as the groundwork for a practical and fundable clinical trial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.278
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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