Construction of a risk prediction model for post-CABG acute kidney injury based on clinical data and retrospective analysis of blood purification therapeutic outcomes
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background Patients undergoing Coronary artery bypass grafting (CABG) surgery often experience acute kidney injury, which dramatically increases mortality and prolongs hospital stays. Our research had two objectives: first, creating a model to predict acute kidney injury (AKI) risk following CABG, and second, investigating how various blood purification techniques influence recovery outcomes. Methods We conducted a retrospective review of 125 CABG cases from our hospital's records (Jan 2018-Dec 2022). Risk factors were determined through logistic regression, allowing us to build a prediction model. We evaluated this model using ROC curves, calibration methods, and internal validation. In a separate analysis, we examined 32 post-CABG patients who developed AKI and received blood purification, comparing the effectiveness of different treatment protocols. Results Through comprehensive analysis, we identified several independent factors associated with increased risk: advanced age (≥70 years), pre-existing cardiac insufficiency, compromised baseline renal function, diabetes mellitus, surgical procedures of extended duration, prolonged cardiopulmonary bypass (>120 minutes), and intraoperative hypotensive episodes. The predictive framework we developed exhibited excellent discriminatory capacity (AUC=0.81, 95% CI: 0.75-0.87) with appropriate calibration metrics. Conclusion our findings provide compelling evidence that initiating CRRT early in the treatment course yields substantial benefits over conventional dialysis methods when addressing this severe post-operative complication.
Source: Current Problems in Surgery
Authors: Jianmei Lin, Xiujun Lan, Shengfeng Li, Xiaoyong Li
Language: English
Topic: Acute Kidney Injury Research
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,780
OpenAlex ID: W4411090726