Objective: The aim of the study was to establish a predictive postoperative nomogram for acute kidney injury (AKI) after intracranial aneurysm clipping surgery, in order to early identify patients with high postoperative AKI risk.
Methods: This is a retrospective study, which included patients who underwent intracranial aneurysm clipping surgery. Multivariate logistic regression was employed to select confound factors that associated with AKI, then incorporated into the nomogram. The predictive accuracy of the model was assessed by concordance index (C-Index).
Results: A total of 365 patients after intracranial aneurysm clipping surgery were enrolled in the study eventually, of which 68 (18.63%) suffered postoperative AKI, and the incidence of stage 1, stage 2 and stage 3 were 92.65% (63/68), 5.88% (4/68), and 1.47% (1/68), respectively. Univariate logistic regression revealed that high density lipoprotein (HDL), prothrombin time (PT), estimated glomerular filtration rate (eGFR), size of aneurysm ≥10 mm, and aneurysm ruptured before surgery were associated with AKI after surgery, while multivariate logistic regression showed same results as the size of aneurysm ≥10 mm and aneurysm ruptured were independent AKI risk factors. In addition, the nomogram demonstrated a good accuracy in estimating intracranial aneurysm clipping associated AKI, as a C-Index and a bootstrap-corrected one of 0.772 and 0.737, respectively. Moreover, calibration plots showed consistency with the actual presence of AKI.
Conclusion: The novel nomogram model can serve as a promising predictive tool to improve the identification of AKI among those who underwent intracranial aneurysm clipping surgery.
Keywords: Intracranial aneurysm clipping surgery; acute kidney injury; nomogram; predictive model.