Risk Factors for Development of Acute Kidney Injury in Critically Ill Patients: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background. Acute kidney injury (AKI) is a frequent complication of critically ill patients. The impact of different risk factors associated with this entity in the ICU setting is unknown. Objectives. The purpose of this research was to assess the risk factors associated with the development of AKI in critically ill patients by meta-analyses of observational studies. Data Extraction. Two reviewers independently and in duplicate used a standardized form to collect data from published reports. Authors were contacted for missing data. The Newcastle-Ottawa scale assessed study quality. Data Synthesis. Data from 31 diverse studies that enrolled 504,535 critically ill individuals from a wide variety of ICUs were included. Separate random-effects meta-analyses demonstrated a significantly increased risk of AKI with older age, diabetes, hypertension, higher baseline creatinine, heart failure, sepsis/systemic inflammatory response syndrome, use of nephrotoxic drugs, higher severity of disease scores, use of vasopressors/inotropes, high risk surgery, emergency surgery, use of intra-aortic balloon pump, and longer time in cardiopulmonary bypass pump. Conclusion. The best available evidence suggests an association of AKI with 13 different risk factors in subjects admitted to the ICU. Predictive models for identification of high risk individuals for developing AKI in all types of ICU are required.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".