Short-Term Prognostic Risk Factors and Nomogram Predictive Model Construction for Acute Ischaemic Stroke after Endovascular Treatment.

Abstract

To investigate the factors influencing the severity of acute ischaemic stroke (AIS) and short-term prognosis after endovascular treatment. Observational study. Place and Duration of the Study: Department of Cerebrovascular Diseases, The First People's Hospital of Qujing City, Yunnan Province, China, from June 2020 to December 2022. An analysis was conducted on 160 AIS patients undergoing endovascular treatment, classified into atherosclerosis subtype (AS) and cardioembolic subtype (CE) based on the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, each comprising 80 cases. The association among baseline and endovascular treatment parameters, clinical characteristics, postoperative complications, and short-term outcomes (risks of mortality and poor prognosis) was tested. Univariate analysis revealed significant positive correlations between increased fasting glucose, diastolic pressure, and the number of thrombectomy procedures with the frequency of cerebral haemorrhage in CE-type stroke. The time from femoral artery puncture to vessel opening was positively associated with the degree of brain oedema. In the AS subtype, univariate regression analysis demonstrated a significant association between heart rate, fasting glucose, cholesterol, time from femoral artery puncture to vessel opening, and degree of brain oedema with short-term prognosis. Fasting glucose and the number of thrombectomy procedures were significantly linked to short-term prognosis in CE-type AIS. A predictive model using line charts was developed for factors associated with postoperative complications and short-term prognosis, achieving predictive accuracies of 95.5% for the risk of death and 92.7% for poor prognosis (mRS >2), notably surpassing traditional prediction methods. Clinical characteristics and endovascular treatment-related factors are important for the short-term prognosis of AIS patients. Development of predictive models can efficiently identify high-risk patients at an early stage. Acute ischaemic stroke, Atherosclerosis, Cardioembolic, Severity, Prognosis.