The role of neutrophil and LDL-C levels in predicting intracranial hemorrhage following endovascular thrombectomy in patients with acute ischemic stroke.

OBJECTIVES

To investigate the association between intracranial hemorrhage (ICH) and preoperative levels of neutrophils and low-density lipoprotein-cholesterol (LDL-C) in acute ischemic stroke (AIS) patients following endovascular thrombectomy (EVT), and to assess the predictive value of preoperative levels of neutrophils and LDL-C.

METHODS

A retrospective analysis was performed on the clinical records of patients diagnosed with AIS who underwent EVT at Nanchong Central Hospital between 2019 and 2023. Multivariate regression analysis was employed to examine the association of preoperative levels of neutrophils and LDL-C with the occurrence of ICH. Furthermore, a receiver operating characteristic curve was constructed to assess the predictive efficacy of these parameters.

RESULTS

A total of 300 patients with a mean age of 68.0 years (standard deviation, 11.1 years) and a median baseline National Institutes of Health Stroke scale (NIHSS) score of 15.5 (interquartile range, 12.0-19.75) were identified in this cohort. Of these, 28 (9.3%) patients experienced ICH. Multivariate regression analysis revealed that elevated preoperative neutrophil (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.10-1.38, P < 0.001) and LDL-C (OR 2.64, 95% CI 1.52-4.58, P < 0.001) levels were independently associated with ICH. The combined indicator demonstrated a higher area under the curve (AUC 0.759, 95% CI 0.654-0.865) compared with preoperative neutrophil (AUC 0.647, 95% CI 0.532-0.763) and LDL-C (AUC 0.711, 95% CI 0.607-0.814) levels individually.The specificity and sensitivity of the combined indicator were 67.9% and 83.1%, respectively.

CONCLUSIONS

Preoperative levels of neutrophils and LDL-C may serve as predictive indicators for ICH in patients with AIS who have undergone EVT; moreover, the combination of preoperative neutrophil and LDL-C levels demonstrates enhanced predictive efficacy.