Understanding and Overcoming the Challenges Related to Cardiovascular Trials Involving Patients with Kidney Disease
Bibliographic record
Abstract
Cardiovascular disease is a prevalent and prognostically important comorbidity among patients with kidney disease, and individuals with kidney disease make up a sizeable proportion (30%-60%) of patients with cardiovascular disease. However, several systematic reviews of cardiovascular trials have observed that patients with kidney disease, particularly those with advanced kidney disease, are often excluded from trial participation. Thus, currently available trial data for cardiovascular interventions in patients with kidney disease may be insufficient to make recommendations on the optimal approach for many therapies. The Kidney Health Initiative, a public-private partnership between the American Society of Nephrology and the US Food and Drug Administration, convened a multidisciplinary, international work group and hosted a stakeholder workshop intended to understand and develop strategies for overcoming the challenges with involving patients with kidney disease in cardiovascular clinical trials, with a particular focus on those with advanced disease. These efforts considered perspectives from stakeholders, including academia, industry, contract research organizations, regulatory agencies, patients, and care partners. This article outlines the key challenges and potential solutions discussed during the workshop centered on the following areas for improvement: building the business case, re-examining study design and implementation, and changing the clinical trial culture in nephrology. Regulatory and financial incentives could serve to mitigate financial concerns with involving patients with kidney disease in cardiovascular trials. Concerns that their inclusion could affect efficacy or safety results could be addressed through thoughtful approaches to study design and risk mitigation strategies. Finally, there is a need for closer collaboration between nephrologists and cardiologists and systemic change within the nephrology community such that participation of patients with kidney disease in clinical trials is prioritized. Ultimately, greater participation of patients with kidney disease in cardiovascular trials will help build the evidence base to guide optimal management of cardiovascular disease for this population.
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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 | 0.738 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.047 | 0.042 |
| Open science | 0.015 | 0.044 |
| Research integrity | 0.034 | 0.070 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".