Management and Outcomes of Heart Failure Patients with CKD: Experience from an Inter-Disciplinary Clinic
Bibliographic record
Abstract
AIMS: CKD-HF patients suffer excess hospitalization and mortality, often under-treated with life-prolonging medications due to fear of worsening renal function and hyperkalaemia. Yet, role of inter-disciplinary working in improving therapy is unknown, which this study aims to investigate. METHODS AND RESULTS: Clinical, biochemical data, and medications at first and last clinic visit were obtained from patient records for 124 patients seen in kidney failure-heart failure clinic (23 March 2017 to 11 April 2019). Medication dose groups (none, low, and high dose), number of RAASi agents, and blood test results were compared between first and last visit in patients with at least two clinic visits (n = 97). Patient characteristics were age 78.5 years (IQR 68.1-84.4 years), male 67.7%, diabetes 51.6%, moderate (45.2%) vs. severe (39.5%) CKD, HF with reduced ejection fraction (HFrEF) (49.2%), follow-up 234 days (IQR 121-441 days). HFrEF was associated with increased risk of death (adjusted OR 4.49, 95% CI 1.43-14.05; P = 0.01). Distributions of patients according to number of RAASi agents they were on differed between first and last visit (P = 0.03). Dosage was increased in 25.9% for beta-blockers, 33.0% for ACEi/ARBs, and 17.5% for MRAs. Distributions of patients across MRA dosage groups was different (P = 0.03), with higher proportions on higher dosages at last visit, without significant changes in serum potassium or creatinine. Serum ferritin improved (131.0 vs. 267.5 μg/L; P < 0.001), and fewer patients had iron deficiency (56.7% vs. 26.8%; P = 0.002) at last visit compared to the first. CONCLUSIONS: This inter-disciplinary clinic improved guideline-recommended medication prescription, MRA dosages in CKD-HF patients without significant biochemical abnormality, and iron status. A prospectively designed study with medication titration protocol and defined patient-centred outcomes is needed to further assess effectiveness of such clinic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".