Risk Factors for the Development of Postoperative Acute Kidney Injury in Patients Undergoing Joint Replacement Surgery: A Meta-Analysis
Bibliographic record
Abstract
A new-onset acute kidney injury (AKI) after arthroplasty impairs rehabilitation and outcome. A prior knowledge of risk factors contributes to a planned preventive management and prognostication. Although many studies have addressed the issue, our objective was to perform a meta-analysis to bring a consensus on the perioperative risk factors promoting AKI postoperatively. We conducted a systematic review and meta-analysis of observational studies reporting risk factors with odds of development of AKI according to the existing criteria after hip or knee replacement surgery. We searched the PubMed and Google Scholar databases for free English articles published until June 2018. Two authors independently screened the articles and extracted data. Discrepancies were resolved by consensus or consulting the third author. Methodological quality of the articles was assessed using the Newcastle-Ottawa Scale. A total of five studies were included in this meta-analysis. The following risk factors were found to contribute to new kidney injury: advanced age; male gender; preoperative liver, cardiac, or kidney diseases; presence of heart failure; American Society of Anesthesiologists grade ≥ 3; requirement of perioperative blood transfusion, revision arthroplasty, and knee arthroplasty; body mass index; and use of angiotensin-converting enzyme inhibitors. Diabetes, hypertension, duration of surgery, type of anesthesia, and preoperative serum creatinine were not found to be associated with renal injury. The key limitation was the availability of small number of studies. More longitudinal observational studies addressing the issue are the need of the hour, and, till then, a preventive strategy aimed at the identified risk factors should help.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.070 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".