Risk factors for acute renal failure: inherent and modifiable risks
Bibliographic record
Abstract
PURPOSE OF REVIEW: Our purpose is to discuss established risk factors in the development of acute renal failure and briefly overview clinical markers and preventive measures. RECENT FINDINGS: Findings from the literature support the role of older age, diabetes, underlying renal insufficiency, and heart failure as predisposing factors for acute renal failure. Diabetics with baseline renal insufficiency represent the highest risk subgroup. An association between sepsis, hypovolemia, and acute renal failure is clear. Liver failure, rhabdomyolysis, and open-heart surgery (especially valve replacement) are clinical conditions potentially leading to acute renal failure. Increasing evidence shows that intraabdominal hypertension may contribute to the development of acute renal failure. Radiocontrast and antimicrobial agents are the most common causes of nephrotoxic acute renal failure. In terms of prevention, avoiding nephrotoxins when possible is certainly desirable; fluid therapy is an effective prevention measure in certain clinical circumstances. Supporting cardiac output, mean arterial pressure, and renal perfusion pressure are indicated to reduce the risk for acute renal failure. Nonionic, isoosmolar intravenous contrast should be used in high-risk patients. Although urine output and serum creatinine lack sensitivity and specificity in acute renal failure, they remain the most used parameters in clinical practice. SUMMARY: There are identified risk factors of acute renal failure. Because acute renal failure is associated with a worsening outcome, particularly if occurring in critical illness and if severe enough to require renal replacement therapy, preventive measures should be part of appropriate management.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".