Abstract: Objective To explore the factors influencing the short-term prognosis of patients after carotid endarterectomy (CEA) by preoperative systemic immune-inflammation index (SII) combined with the controlling nutritional status (CONUT) score. Methods A total of 197 patients who underwent CEA in Department of Neurosurgery, the Affiliated Hospital of Xuzhou Medical University from May 2018 to June 2023 were selected for retrospectively analysis. Their clinical data were collected, including general information, preoperative imaging findings (whether there was moderate to severe contralateral stenosis, the length of stenosis, and the integrity of the Willis circle), preoperative hematological indices, mean arterial pressure (MAP) during the intraoperative occlusion period, and carotid artery occlusion time. The SII and CONUT scores were calculated. The patients were divided into two groups: a complication group (n=18) and a non-complication group (n=179). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative prognosis after CEA, and a nomogram model was constructed. The predictive value of the model for CEA prognosis was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Results Univariate and multivariate logistic regression analyses showed that MAP<20% during the intraoperative occlusion period, CONUT score, and the C-reactive protein to serum albumin ratio (CAR) were independent risk factors for poor postoperative prognosis in patients with carotid artery stenosis undergoing CEA (P<0.05). The ROC curve analysis indicated that the combined prediction model incorporating MAP, CONUT score, and CAR was superior to each predictor alone, with an AUC value of 0.984. Conclusions MAP during the intraoperative occlusion period, CAR, and CONUT score may serve as potential indicators for assessing the prognosis of patients with carotid artery stenosis after CEA. The nomogram model based on these parameters demonstrates high accuracy in predicting patient outcomes, potentially providing clinical benefits.