Evaluating the Clinical, Socioeconomic, and Environmental Impact of Guideline-Directed Therapy in the United States: An IMPACT CKD Analysis
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is underdiagnosed and undertreated in the United States (US) despite evidence that therapies can delay disease progression and reduce clinical events. This study aims to illustrate the impact of improved adherence to therapies recommended for patients with CKD (i.e., guideline-directed medical therapy [GDMT]) on clinical, socioeconomic, and environmental outcomes to inform policy decisions. Methods: The US population was simulated for 25-years (baseline: 2022; simulated years: 2023-2047) using the IMPACT CKD model. Two scenarios were compared: 75% adherence to GDMT vs. current practice as observed. GDMT included glucose and lipid lowering, antihypertensive, and lifestyle interventions. It was assumed that patients diagnosed with CKD could be treated with multiple therapies per guideline eligibility and that there would be no changes to guidelines or CKD detection rate over the time horizon. Treatment effects on estimated glomerular filtration rate (eGFR) decline, cardiovascular events, and acute kidney injury (AKI) events were assumed to be multiplicative. Results: Improved adherence to GDMT was associated with a 32% decrease in dialysis due to delayed disease progression. Reductions were projected in myocardial infarction, stroke, hospitalized heart failure, AKI, and death by 21%, 17%, 24%, 9%, and 5%, respectively. Renal replacement therapy (RRT) and total CKD costs were projected to decrease by 25% and 5%, respectively. Freshwater consumption, fossil fuel depletion, and carbon emissions due to RRT were also projected to decrease by 28%. Furthermore, reductions in disease progression and death also contributed to improvements in projected net workdays, gross domestic product, full-time equivalents, and tax revenue among employed patients and caregivers. The benefits of improved adherence to GDMT were seen after six years. Similar trends were observed with a 10-year time horizon but with smaller magnitude. Conclusion: This study predicted significant clinical, socioeconomic, and environmental benefits with improved adherence to GDMT. These findings underscore the importance of policy action to improve adherence and actualize the potential of effective therapies to mitigate CKD burden. Funding: Commercial Support - AstraZeneca
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".