Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a serious complication of major noncardiac surgery. Risk prediction models for AKI following noncardiac surgery may be useful for identifying high-risk patients to target with prevention strategies. METHODS: We conducted a systematic review of risk prediction models for AKI following major noncardiac surgery. MEDLINE, EMBASE, BIOSIS Previews and Web of Science were searched for articles that (i) developed or validated a prediction model for AKI following major noncardiac surgery or (ii) assessed the impact of a model for predicting AKI following major noncardiac surgery that has been implemented in a clinical setting. RESULTS: We identified seven models from six articles that described a risk prediction model for AKI following major noncardiac surgeries. Three studies developed prediction models for AKI requiring renal replacement therapy following liver transplantation, three derived prediction models for AKI based on the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE) criteria following liver resection and one study developed a prediction model for AKI following major noncardiac surgical procedures. The final models included between 4 and 11 independent variables, and c-statistics ranged from 0.79 to 0.90. None of the models were externally validated. CONCLUSIONS: Risk prediction models for AKI after major noncardiac surgery are available; however, these models lack validation, studies of clinical implementation and impact analyses. Further research is needed to develop, validate and study the clinical impact of such models before broad clinical uptake.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 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".