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

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. 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. 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. 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. Cardiac surgery-associated acute kidney injury is common, and clinical prediction models may improve risk stratification and support future clinical research. This systematic review and meta-analysis evaluated validated CPMs and pooled their diagnostic performance. Thakar 2005 had the most extensive validation and demonstrated good discrimination for CSA-AKI requiring renal replacement therapy, whereas newer CPMs targeting milder stages show promising discriminatory performance but remain supported by limited validation evidence.

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Publication Details

Journal
Critical Care
Published
2026-10-15
DOI
https://doi.org/10.1186/s13054-026-06380-4
Primary Topic
Acute Kidney Injury Research
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Amaia Torrecilla Ugarte, J. Barado Hualde, Rafael Sádaba, Pablo Lasa-Berasain et al.
Critical Care
Acute Kidney Injury Research
article

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

Amaia Torrecilla Ugarte, J. Barado Hualde, Rafael Sádaba, Pablo Lasa-Berasain, Raúl Juárez‐Vela, Berta Ibáñez, Aitor Ansotegui Hernández
article en

Abstract

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. 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. 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. 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. Cardiac surgery-associated acute kidney injury is common, and clinical prediction models may improve risk stratification and support future clinical research. This systematic review and meta-analysis evaluated validated CPMs and pooled their diagnostic performance. Thakar 2005 had the most extensive validation and demonstrated good discrimination for CSA-AKI requiring renal replacement therapy, whereas newer CPMs targeting milder stages show promising discriminatory performance but remain supported by limited validation evidence.

Critical Care
Universidad de La Rioja (ES), Universidad Pública de Navarra (UPNA) (ES), Navarrabiomed (ES), Hospital Universitario Reina Sofía (ES), Universidad de Navarra (ES)
Gobierno de Navarra
Good health and well-being
Openalex Percentile: Top 13%
Acute Kidney Injury Research
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