Clinical prediction models in cardiac surgery-associated acute kidney injury: a systematic review and meta-analysis.

PURPOSE: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a frequent postoperative complication associated with substantial morbidity and mortality. Clinical prediction models (CPM) may identify high-risk patients enabling targeted preventive interventions and improved outcomes. The objective of the study was to identify available CPMs for the risk of developing CSA-AKI and summarize their performance.

METHODS

A systematic search of studies published in Pubmed, Scopus, the Chocrane Library and Web of Science was conducted, including studies that developed or validated CPMs for CSA-AKI in adult patients undergoing cardiac surgery with cardiopulmonary bypass. Data were extracted by two independent reviewers. Summary measures of diagnostic performance were calculated when feasible using random-effects models. Calibration and sensitivity analyses were also performed.

RESULTS

The pooled incidence of CSA-AKI was 26.3% (95% CI, 26.0-26.4), whereas CSA-AKI requiring renal replacement therapy (CSA-AKI RRT) occurred in 2.2% of patients (95% CI, 2.1-2.2). Among validated CPMs, Thakar 2005 demonstrated the best predictive performance for CSA-AKI RRT: pooled AUC 0.80 (95% CI, 0.77-0.82), sensitivity of 0.87, and a specificity of 0.49 at a threshold of ≥ 3 points. Calibration analyses indicated underestimation of risk. The Mehta 2006 and Wijeysundera 2007 models also demonstrated good discrimination. Discrimination for the prediction of milder stages of CSA-AKI was poorer, but newer models such as the Demirjian 2022 model showed promising performance.

CONCLUSION

Accurate and well-validated CPMs can facilitate risk stratification for CSA-AKI which can influence clinical practice and research. Thakar 2005 demonstrated very good discrimination for predicting CSA-AKI RRT, whereas more validation studies for the prediction of milder stages of CSA-AKI are warranted.