Combining cardiac and renal biomarkers to establish a clinical early prediction model for cardiac surgery-associated acute kidney injury: a prospective observational study.

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a prevalent complication with poor outcomes, and its early prediction remains a challenging task. Currently available biomarkers for acute kidney injury (AKI) include serum cystatin C (sCysC) and urinary N-acetyl-β-D-glucosaminidase (uNAG). Widely used biomarkers for assessing cardiac function and injury are N-terminal pro B-type natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI). In light of this, our study aimed to evaluate the effectiveness of these four biomarkers in predicting CSA-AKI. Methods: This prospective observational study enrolled adult patients who had undergone cardiac surgery. The clinical prediction model for CSA-AKI was developed using the least absolute shrinkage and selection operator (LASSO) regression method. The model's performance was assessed using the area under the curve of the receiver operating characteristic (ROC-AUC), decision curve analysis (DCA), and calibration curves. Furthermore, a separate validation cohort was constructed to externally validate the prediction model. Additionally, a risk nomogram was created to facilitate risk assessment and prediction. Results: In the modeling cohort consisting of 689 patients and the validation cohort consisting of 313 patients, the total incidence of CSA-AKI was 33.4%. The LASSO regression identified several predictors, including age, history of hypertension, baseline serum creatinine (sCr), coronary artery bypass grafting combined with valve surgery, cardiopulmonary bypass duration, preoperative albumin, hemoglobin, postoperative NT-proBNP, cTnI, sCysC, and uNAG. The constructed clinical prediction model demonstrated robust performance, with a ROC-AUC of 0.830 (0.800-0.860) in the modeling cohort and 0.840 (0.790-0.880) in the validation cohort. Furthermore, both calibration and DCA indicated good model fit and clinical benefit. Conclusions: This study demonstrates that incorporating the immediately postoperative renal biomarkers, sCysC and uNAG, along with the cardiac biomarkers, NT-proBNP and cTnI, into a clinical early prediction model can significantly enhance the accuracy of predicting CSA-AKI. These findings suggest that a comprehensive approach combining both renal and cardiac biomarkers holds promise for improving the early detection and prediction of CSA-AKI.