Tailored Versus Standard Hydration to Prevent Acute Kidney Injury After Percutaneous Coronary Intervention: Network Meta‐Analysis
Bibliographic record
Abstract
Background Contrast‐induced acute kidney injury (CI‐AKI) is a serious complication after percutaneous coronary intervention. The mainstay of CI‐AKI prevention is represented by intravenous hydration. Tailoring infusion rate to patient volume status has emerged as advantageous over fixed infusion‐rate hydration strategies. Methods and Results A systematic review and network meta‐analysis with a frequentist approach were conducted. A total of 8 randomized controlled trials comprising 2312 patients comparing fixed versus tailored hydration strategies to prevent CI‐AKI after percutaneous coronary intervention were included in the final analysis. Tailored hydration strategies included urine flow rate–guided, central venous pressure–guided, left ventricular end‐diastolic pressure–guided, and bioimpedance vector analysis–guided hydration. Primary endpoint was CI‐AKI incidence. Safety endpoint was incidence of pulmonary edema. Urine flow rate–guided and central venous pressure–guided hydration were associated with a lower incidence of CI‐AKI compared with fixed‐rate hydration (odds ratio [OR], 0.32 [95% CI, 0.19–0.54] and OR, 0.45 [95% CI, 0.21–0.97]). No significant difference in pulmonary edema incidence was observed between the different hydration strategies. P score analysis showed that urine flow rate–guided hydration is advantageous in terms of both CI‐AKI prevention and pulmonary edema incidence when compared with other approaches. Conclusions Currently available hydration strategies tailored on patients' volume status appear to offer an advantage over guideline‐supported fixed‐rate hydration for CI‐AKI prevention after percutaneous coronary intervention. Current evidence suggests that urine flow rate–guided hydration as the most convenient strategy in terms of effectiveness and safety.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".