Seven risk prediction models for acute kidney injury after major noncardiac surgery demonstrated c-statistics ranging from 0.79 to 0.90, though none were externally validated.
Systematic Review
What is the performance and validation status of risk prediction models for acute kidney injury following major noncardiac surgery?
While several risk prediction models for AKI after major noncardiac surgery exist with good discrimination (c-statistics 0.79-0.90), their clinical utility is currently limited by a complete lack of external validation and impact analyses.
Effect estimate: c-statistics 0.79 to 0.90
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.
Wilson et al. (Thu,) conducted a systematic review in Acute kidney injury following major noncardiac surgery. Risk prediction models for acute kidney injury was evaluated on Model discrimination (c-statistic) (c-statistics 0.79 to 0.90). Seven risk prediction models for acute kidney injury after major noncardiac surgery demonstrated c-statistics ranging from 0.79 to 0.90, though none were externally validated.