Systematic Literature Review of the Impact of Type 2 Diabetes and Heart Failure Guideline Adherence on Clinical and Economic Outcomes
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes mellitus (T2DM) is associated with comorbidities, particularly in the cardiovascular, renal, and metabolic (CVRM) spectrum. Given the complexity of CVRM spectrum diseases and the treatment landscape, treatment guidelines have been established to assist physicians in selecting the most appropriate treatment based on not only patients' primary disease but also their comorbidities. However, the impact of adherence to treatment guidelines on associated outcomes remains unclear. METHODS: A systematic literature review was conducted to evaluate the impact of guideline-based treatment on clinical, economic, and quality-of-life (QoL) outcomes and related comorbidities in the CVRM spectrum or heart failure (HF) alone in individuals with T2DM. The MEDLINE, MEDLINE In-Process, Embase, Cochrane Central Register of Controlled Trials, and EconLit electronic databases were searched to identify relevant peer-reviewed studies published in the United States, Canada, or Germany. The studies were screened and selected for inclusion or exclusion based on populations, interventions, comparators, and study design (PICOS) criteria. RESULTS: Of the 622 records identified, 28 publications met the inclusion criteria. In total, 11 and 16 studies reported adherence to clinical guidelines for T2DM and HF management, respectively. Adherence to T2DM and HF treatment guidelines decreased all-cause mortality and all-cause hospitalizations; furthermore, adherence to T2DM guidelines decreased hospitalizations due to HF, myocardial infarction, and stroke, and reduced T2DM-related long-term complications, while adherence to HF guidelines reduced hospitalizations due to HF. Evidence gaps were identified, including the need to assess the impact of guideline adherence on economic outcomes, the impact of adherence to diagnosis and monitoring guidelines, and the impact of guideline adherence on QoL outcomes. CONCLUSIONS: Adherence to disease management guidelines improves the outcomes of patients with T2DM or HF and reduces disease-related complications and hospitalizations; however, key evidence gaps exist, particularly regarding patients with T2DM along with comorbidities in the CVRM spectrum.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".