Cardiothoracic & vascular anaesthesia
Clinical prediction models in cardiac surgery-associated acute kidney injury: a systematic review and meta-analysis
Authors' 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.
Study context
- Population
- Adult patients undergoing cardiac surgery with cardiopulmonary bypass; studies that developed or validated clinical prediction models (CPMs) for cardiac surgery-associated acute kidney injury (CSA-AKI)
- Comparison
- Clinical prediction models for the risk of developing CSA-AKI
- Outcome
- Pooled incidence of CSA-AKI
Methodological detailsrandom-effects models, calibration and sensitivity analyses, two independent reviewers
- Random-effects models
- Calibration and sensitivity analyses
- Two independent reviewers
Pooled result
As reported in the abstract
CSA-AKI, pooled incidence
26.3%
95% CI 26.0–26.4
CSA-AKI requiring renal replacement therapy (CSA-AKI RRT), patients
2.2%
95% CI 2.1–2.2
Further pooled results
| Outcome | Values |
|---|---|
| Predictive performance of Thakar 2005 for CSA-AKI RRT | Thakar 2005 pooled AUC 0.80 (95% CI 0.77-0.82) · sensitivity at a threshold of ≥ 3 points 0.87 · specificity at a threshold of ≥ 3 points 0.49 |
Predictive performance of Thakar 2005 for CSA-AKI RRT
- Best predictive performance for CSA-AKI RRT among validated CPMs
- Calibration analyses indicated underestimation of risk
Discrimination of Mehta 2006 and Wijeysundera 2007 models
- The Mehta 2006 and Wijeysundera 2007 models also demonstrated good discrimination
Discrimination for milder stages of CSA-AKI
- Discrimination poorer; newer models such as the Demirjian 2022 model showed promising performance
Original publication
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