Impact of the reduction of calcineurin inhibitors on renal function in heart transplant patients: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: Calcineurin inhibitors (CNIs) taken after heart transplantation lead to excellent short-term outcomes, but long-term use may cause chronic nephrotoxicity. Our aim was to identify, appraise, select and analyse all high-quality research evidence relevant to the question of the clinical impact of CNI-sparing strategies in heart transplant patients. METHODS: We carried out a systematic review and meta-analysis of randomized controlled trials on CNI reduction in heart transplant recipients. Primary outcomes were kidney function and acute rejection after 1 year. Secondary outcomes included graft loss, all-cause mortality and adverse events. RESULTS: Eight open-label studies were included, with 723 patients (four tested de novo CNI reduction and four maintenance CNI reduction). Calcineurin inhibitor reduction did not improve creatinine clearance at 12 months 5.46 [-1.17, 12.03] P = 0.32 I(2) = 65.4%. Acute rejection at 12 months (55/360 vs. 52/332), mortality (18/301 vs. 15/270) and adverse event rates (55/294 vs. 52/281) did not differ between the low-CNI and standard-CNI groups. There was significant benefit on creatinine clearance in patients with impaired renal function at 6 months [+12.23 (+5.26, +18.82) ml min(-1) , P = 0.0003] and at 12 months 4.63 [-4.55, 13.82] P = 0.32 I(2) = 75%. CONCLUSIONS: This meta-analysis did not demonstrate a favourable effect of CNI reduction on kidney function, but there was no increase in acute rejection. To provide a better analysis of the influence of CNI reduction patterns and associated treatments, a meta-analysis of individual patient data should be performed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".