Application of 17 Contrast-Induced Acute Kidney Injury Risk Prediction Models

Introduction: Contrast-induced acute kidney injury (CI-AKI) is a frequent complication of percutaneous coronary interventions (PCI). Various groups have developed and validated risk scores for CI-AKI. Although the majority of these risk scores achieve an adequate accuracy, their usability in clinical practice is limited and greatly debated. Objective: With the present study, we aimed to prospectively assess the diagnostic performance of recently published CI-AKI risk scores (up to 2018) in a cohort of patients undergoing PCI. Methods: We enrolled 1,247 consecutive patients (80% men, mean age 62 ± 10 years) treated with elective or urgent PCI. For each patient, we calculated the individual CI-AKI risk score based on 17 different risk models. CI-AKI was defined as an increase of ≥25% (liberal) or ≥0.5 mg/dL (strict) in pre-PCI serum creatinine 48 h after PCI. Results: CI-AKI definition and, therefore, CI-AKI incidence have a significant impact on risk model performance (median negative predictive value increased from 85 to 99%; median c-statistic increased from 0.516 to 0.603 using more strict definition criteria). All of the 17 published models were characterized by a weak-to-moderate discriminating ability mainly based on the identification of “true-negative” cases (median positive predictive value 19% with liberal criterion and 3% with strict criterion). In none of the models, c-statistic was >0.800 with either CI-AKI definition. Novel, different combinations of the >35 independent variables used in the published models either by down- or by up-scaling did not result in significant improvement in predictive performance. Conclusions: The predictive ability of all models was similar and only modest, derived mainly by identifying true-negative cases. A new approach is probably needed by adding novel markers or periprocedural characteristics.