Risk prediction models for contrast-induced acute kidney injury in patients with acute coronary syndromes: a systematic review and meta-analysis

Background Percutaneous coronary intervention (PCI) has become a crucial method for the treatment of acute coronary syndromes (ACS), which includes ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina (UA). However, contrast-induced acute kidney injury(CI-AKI) is one of its serious complications. A growing number of models have been used to predict ACS patients undergoing coronary angiography (CAG) or PCI, but the predictive efficacy of these models is unclear. Methods We systematically searched PubMed, Web of Science, The Cochrane Library, and Embase from the inception to May 18, 2024. This study excluded non-English studies to reduce potential language bias. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate bias risk and applicability of the studies in the prediction model, and the area under the curve (AUC) values of the models were meta-analyzed by Stata 15.0 software. Results 13,834 articles were retrieved, and 16 studies were finally included after screening. The incidence of CI-AKI in patients with ACS underwent PCI or CAG ranged from 4.66 to 19.85%. The developed models exhibited a pooled AUC of 0.804 (95% CI: 0.772–0.836), while the validation models demonstrated a pooled AUC of 0.785 (95% CI: 0.747–0.823). However, significant heterogeneity was observed in both the development and validation cohorts (89.7 and 84.8%, respectively), along with publication bias ( p 0.05). All included studies were assessed as having a high risk of bias, mainly due to inappropriate data sources and bias in statistical analysis. Conclusion No existing model for CI-AKI after CAG or PCI can currently be recommended for routine use due to the high risk of bias and the lack of external validation. Researchers should follow PROBAST and use a prospective design with a large sample size to improve the quality of prediction models and provide better clinical value. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024573128 .