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Peer-reviewedCC BY-NC-NDCritical care (London, England)

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

  • Pablo Lasa-Berasain1,2
  • Amaia Torrecilla Ugarte3
  • Raúl Juárez-Vela4
  • Berta Ibánez-Beróiz5
  • Aitor Ansotegui Hernández6
  • Rafael Sádaba7,8
  • Julio Barado Hualde7,6
8 affiliations
  1. 1Department of Intensive Care Medicine, Reina Sofia Hospital, Tudela, Navarre, Spain. [email protected].
  2. 2Public University of Navarre (UPNA), Pamplona, Navarre, Spain. [email protected].
  3. 3Department of Internal Medicine, Garcia Orcoyen Hospital, Estella, Navarre, Spain.
  4. 4Faculty of Health Sciences, GRUPAC Research Group, University of La Rioja, La Rioja, Spain.
  5. 5Methodology Unit, Navarrabiomed-UPNA, Navarre, Spain.
  6. 6Department of Intensive Care Medicine, University Hospital of Navarre, Pamplona, Navarre, Spain.
  7. 7Public University of Navarre (UPNA), Pamplona, Navarre, Spain.
  8. 8Cardiovascular Translational Research, Navarrabiomed (Fundación Miguel Servet); Instituto de Investigación Sanitaria de Navarra (IdiSNA); University Hospital of Navarre, Navarre, Spain.
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{lasaberasain2026clinical,
  title = {{Clinical prediction models in cardiac surgery-associated acute kidney injury: a systematic review and meta-analysis.}},
  author = {Pablo Lasa-Berasain and Amaia Torrecilla Ugarte and Raúl Juárez-Vela and Berta Ibánez-Beróiz and Aitor Ansotegui Hernández and Rafael Sádaba and Julio Barado Hualde},
  journal = {Critical care (London, England)},
  year = {2026},
  month = oct,
  doi = {10.1186/s13054-026-06380-4},
  url = {https://europepmc.org/article/MED/42919415},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Clinical prediction models in cardiac surgery-associated acute kidney injury: a systematic review and meta-analysis.
AU  - Pablo Lasa-Berasain
AU  - Amaia Torrecilla Ugarte
AU  - Raúl Juárez-Vela
AU  - Berta Ibánez-Beróiz
AU  - Aitor Ansotegui Hernández
AU  - Rafael Sádaba
AU  - Julio Barado Hualde
PY  - 2026
DA  - 2026/10/15
JO  - Critical care (London, England)
DO  - 10.1186/s13054-026-06380-4
UR  - https://europepmc.org/article/MED/42919415
AB  - 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.
SN  - 1364-8535
SN  - 1466-609X
ER  -