Advanced chronic kidney disease coexisting with heart failure: navigating patients’ management
Bibliographic record
Abstract
Chronic kidney disease (CKD) and heart failure (HF) are interrelated, mutually exacerbating conditions. HF in patients with moderate to severe CKD poses unique clinical problems. Indeed, considerations related to specific concomitant derangements, such as vascular calcification, inflammation, and proteinuria, inform and demand personalized treatment strategies. Pharmacological interventions, including renin-angiotensin system antagonists, sodium-glucose cotransporter 2 (SGLT2) inhibitors and novel mineralocorticoid receptor blockers are valuable in managing these complex conditions, although frequently difficult or impossible to use in advanced kidney disease. Precision medicine, innovative treatments, and the incorporation of digital health tools, artificial intelligence, remote monitoring, and advanced imaging techniques into patient care are redesigning the scenario of HF associated with CKD. AI-driven predictive analytics for early detection of decompensation, telemedicine for remote consultations, and electronic health records with decision-support systems. These innovations enhance personalized treatment, improve early intervention, and optimize disease management, ultimately leading to better outcomes for patients with HF and CKD. Collaborative care models are being implemented and evaluated to advance the management of such conditions. Thus, the integration of novel therapeutic approaches and personalized medicine holds promise for improving patient outcomes, while ongoing research is essential to enabling innovation in this area. Here we review the current management of concomitant kidney disease and HF, highlighting areas for proposed future refinements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".