Combination Treatment for Cardiorenal Protection in US Adults
Bibliographic record
Abstract
Background: Clinical guidelines recommend combined use of renin-angiotensin system inhibitors (RASi), sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1RA) and nonsteroidal mineralocorticoid receptor antagonists (nsMRA). Few data exist on eligibility for combined treatment in US adults. Methods: Using the 2017-2020 National Health and Nutrition Examination Survey (NHANES), we estimated eligibility for combination treatment based on grade 1A to 2B recommendations for CKD or cardiovascular (CV) events in 6 clinical guidelines from KDIGO, AHA/ACC, ADA, and RCTs published after the guidelines (SELECT, FLOW, FINEARTS-HF). For combination treatment, we accounted for acute changes in eGFR, UACR and serum potassium at 3 months with each medication class before determining eligibility for the next medication class in the sequence. We stratified results by diabetes (DM) and CKD. We linked NHANES 2007-2016 to the 2018 Death Index to estimate all-cause and CV deaths in eligible adults. Using direct treatment effects of combination treatment from meta-analyses, we projected the number of prevented deaths. Results: Of 8152 adults, 15% had DM, 14% CKD and 5% DM and CKD (DKD). Mean age, eGFR, and UACR were 48yrs, 97ml/min/1.73m2, and 31mg/g. The dual-therapy with highest eligibility was RASi-SGLT2i, with 12% of the US population eligible (DM: 67%; CKD: 43%; DKD: 89%). The triple-therapy with highest eligibility was RASi-SGLT2i-GLP1RA, with 9% overall eligibility (DM: 58%; CKD: 26%; DKD: 67%). Eligibility for quadruple-therapy was 2% overall (DM: 15%; CKD:13%; DKD: 32%). Despite an overall 5% lower eligibility for triple therapy with RASi-SGLT2i-GLP1RA vs. dual therapy with RASi-SGLT2i, all-cause and CV deaths were reduced by ≥33% for RASi-SGLT2i-GLP1RA vs. RASi-SGLT2i, due to synergistic benefits. Conclusion: Eligibility for combined treatment with novel cardiorenal protective therapies is highly prevalent, particularly in DKD. Funding: Government Support – Non-U.S.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".