Objective To summarize cl inical experience of carotid endarterectomy (CEA) in treating severe carotid stenosis. Methods Between October 1998 and January 2010, 215 patients with carotid stenosis were treated with CEA. There were 140 males and 75 females with an average age of 66 years (range, 51-88 years). Transient ischemic attack (TIA) occurred in127 cases, and 31 cases had history of cerebral infarction. All cases were diagnosed definitely by selective angiography and/or CT angiography, and stenosis degree was more than 80%; contralateral carotid artery was also involved in 45 cases. Ninty-six cases were found to have coronary artery stenosis by coronary angiography. CEA and coronary artery bypass grafting were performed simultaneously in 25 cases. Peripheral arterial disease was found in 43 cases and treated at the same time. Results A total of 155 patients were followed up 6-72 months. The cl inical symptom significantly alleviated in 148 cases postoperatively. Two cases had compl ication of cerebral hemorrhage within 1 week postoperatively; one died and the other was resumed after the conservative treatment. One case had hypoglossal nerve injury. Four cases had injuring marginal mandibular branch of the facial nerve, and no special treatment was given. Restenosis was found in 25 patients, and the stenosis degree was less than 25%; moreover, the patients had no TIA. One case died of heart attack at 3 years of follow-up period. Conclusion CEA is an effective and safe method for treating severe carotid stenosis.
Objective To identify the preoperative risk factors for prolonged mechanical ventilation (PMV) after pulmonary thromboendarterectomy (PTE). MethodsThe clinical data of patients who underwent PTE from December 2016 to August 2021 in our hospital were retrospectively analyzed. The patients were divided into two groups according to the postoperative mechanical ventilation time, including a postoperative mechanical ventilation time≤48 h group (≤48 h group) and a postoperative mechanical ventilation time>48 h (PMV) group (>48 h group). Univariable and logistic regression analysis were used to identify the preoperative risk factors for postoperative PMV. ResultsTotally, 90 patients were enrolled in this study. There were 40 patients in the ≤48 h group, including 30 males and 10 females, with a mean age of 45.48±12.72 years, and there were 50 patients in the >48 h group, including 29 males and 21 females, with a mean age of 55.50±10.42 years. The results showed that in the ≤48 h group, the median postoperative ICU stay was 3.0 days, and the median postoperative hospital stay was 15.0 days; in the >48 h group, the median postoperative ICU stay was 7.0 days, and the median postoperative hospital stay was 20.0 days. The postoperative PMV was significantly correlated with tricuspid annular plane systolic excursion (TAPSE) [OR=0.839, 95%CI (0.716, 0.983), P=0.030], age [OR=1.082, 95%CI (1.034, 1.132), P=0.001] and pulmonary vascular resistance (PVR) [OR=1.001, 95%CI (1.000, 1.003), P=0.028]. ConclusionAge and PVR are the preoperative risk factors for PMV after PTE, and TAPSE is the preoperative protective factor for PMV after PTE.
Objective To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. ResultsFinally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.