Association between platelet counts and morbidity and mortality after endovascular repair for type B aortic dissection.

This study aimed to assess the association of postoperative platelet counts with early and late outcomes after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection (TBAD). We retrospectively evaluated 892 patients with TBAD who underwent TEVAR from a prospectively maintained database. Postoperative nadir platelet counts were evaluated as a continuous variable, and a categorical variable (thrombocytopenia), which was defined as platelet count≤ the lowest 10% percentile (108 × 109/l). Multivariable logistic regression analyses were conducted to assess the impact of postoperative thrombocytopenia on early outcomes, and multivariable cox regression analyses on long-term mortality. Patients with postoperative thrombocytopenia experienced significantly higher rates of postoperative mortality, prolonged intensive care unit stay, death, stroke, limb ischemia, mesenteric ischemia, acute kidney injury (AKI), and puncture-related hematoma (P< .05 for each), but similar rates of immediate type I endoleak and spinal cord ischemia. Multivariable logistic analyses showed that postoperative thrombocytopenia was independently associated with postoperative stroke, limb ischemia, and AKI. Similar results were observed when postoperative nadir platelet count was modeled as a continuous predictor (P< .05 for each). By multivariable Cox analyses, postoperative thrombocytopenia was an independent predictor for long-term all-cause mortality (hazard ratio 2.72, 95% CI, 1.72-4.29, P< .001). For every 30 × 109/L decrease in postoperative platelet count, the risk of long-term all-cause mortality increased by 15% (HR 1.15; 95% CI 1.07-1.25; P< .001). Therefore, postoperative thrombocytopenia might be a useful tool for risk stratification after TEVAR.

Outcomes of Chimney Technique for Preservation of the Left Subclavian Artery in Type B Aortic Dissection.

OBJECTIVE: To report outcomes of the chimney technique for preservation of the left subclavian artery (LSA) in patients with type B aortic dissection (TBAD). METHODS: A retrospective analysis was performed of a prospectively maintained database from August 2012 to October 2017. Primary endpoints were 30 day and overall mortality. Secondary endpoints were technical success, type Ia endoleak, chimney stent occlusion, aortic rupture, stroke, spinal cord ischaemia, and re-intervention rate. RESULTS: A total of 159 patients (mean age 54 ± 11 years; 141 men) with TBAD were treated using the chimney technique for LSA revascularisation. Acute, subacute, and chronic TBAD accounted for 64%, 28%, and 8% of cases, respectively. One hundred and six cases (67%) were complicated TBAD. One hundred and fifty-six patients (98%) were treated electively, while three (2%) were treated urgently because of intestinal or lower extremity ischaemia. The 30 day mortality and morbidity rates were 2% (3/159) and 4% (7/159), respectively. The technical success rate was 81% (129/159) and immediate type Ia endoleak occurred in 30 (19%) patients. Three major strokes, two spinal cord ischaemia and one aortic rupture, occurred early on. During a mean follow up of 23 ± 16 months (range 1-65 months), three more patients died: from aortic rupture, cerebral haemorrhage, and rectal cancer, respectively. Chimney stent occlusions were observed in four patients and all these chimney stents were self expanding. During follow up, two major strokes, one late type Ia endoleak and one re-intervention, occurred. According to the Kaplan-Meier curve, the estimated one and three year survival rates were 98.1 ± 1.1% and 94.4 ± 2.4%, respectively. CONCLUSION: Short and mid-term outcomes in the present study demonstrate that the chimney technique is safe and feasible for preservation of the LSA in patients with TBAD, but the durability of chimney stent needs to be evaluated carefully and immediate type Ia endoleak is a concern.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.