Objective To explore the clinical and inflammatory characteristics and risk factors of severe asthma to improve clinicians' awareness of the disease. Methods The general information of patients with asthma who visited the Department of Respiratory Medicine, the First Hospital of Shanxi Medical University from May 2018 to May 2021, as well as the diagnosis and treatment of asthma, personal history, comorbidities, auxiliary examination, asthma control test (ACT) score were collected. A total of 127 patients were included, including 40 in the severe asthma group and 87 in the mild-to-moderate asthma group. Chi-square test, independent sample t test and logistic regression were used to analyze the clinical characteristics, inflammatory markers and risk factors of severe asthma. Results Compared with the patients with mild to moderate asthma, the patients with severe asthma were more older (51.0±12.0 years vs 40.7±12.8 years, P<0.05), had more smokers (32.5% vs. 14.9%, P<0.05), and more males (67.5% vs. 40.2%, P<0.05). The patients with severe asthma got poor FEV1%pred [(56.1±23.8)% vs. (93.2±18.0)%, P<0.05] and FEV1/FVC [(56.7±13.2)% vs. (75.8±9.0)%, P<0.05)], and more exacerbations in the previous year (2.7±3.1 vs. 0.1±0.4, P<0.05), lower ACT score (14.4±3.7 vs. 18.0±5.0, P<0.05), and higher blood and induced sputum eosinophil counts [(0.54±0.44)×109/L vs. (0.27±0.32)×109/L, P<0.05; (25.9±24.2)% vs. (9.8±17.5)%, P<0.05]. There was no significant difference in the proportion of neutrophils in the induced sputum or FeNO between the two groups (P>0.05). Analysis of related risk factors showed that smoking (OR=2.740, 95%CI 1.053 - 7.130), combined with allergic rhinitis (OR=14.388, 95%CI 1.486 - 139.296) and gastroesophageal reflux (OR=2.514, 95%CI 1.105 - 5.724) were risk factors for severe asthma. Conclusions Compared with patients with mild to moderate asthma, patients with severe asthma are characterized by poor lung function, more exacerbations, and a dominant eosinophil inflammatory phenotype, which is still poorly controlled even with higher level of treatment. Risk factors include smoking, allergic rhinitis, and gastroesophageal reflux, etc.
ObjectiveTo explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). MethodsThe clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). ResultsFour radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. ConclusionThe machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.
Objective
To analyze the curative effect of nitric oxide (NO) and bosentan on treatment of the interruption of aortic arch (IAA) with ventricular septal defect (VSD) and serious pulmonary hypertension (SPH).
Methods
Thirty-two children with IAA and VSD combined SPH from January 2015 to May 2017 confirmed by cardiac CT and ultrasound in Children’s Hospital of Hebei Province were enrolled including 17 males and 15 females, aged 1.10-4.30 months (mean, 2.71±0.98 months) and weighing 3.33-6.10 kg (mean, 4.57±0.88 kg). The 32 children were randomly divided into two groups (n=16 in each), a NO group and a bosentan group. All the patients underwent interruption of aortic arch and ventricular septal defect repair. When patients returned to cardiosurgery intensive care unit (CSICU) half an hour later, patients in the NO group inhaled NO 20 ppm for 36 h and those in the bosentan group were given bosentan by nasogastric feeding 15 mg, twice a day. The cardic index, pulmonary/systemic pressure ratio, oxygenation index at 3 h, 6 h, 12 h, 24 h, 36 h after surgery were evaluated, and the differences between the two groups were compared.
Results
The pulmonary/systemic pressure ratio in the two groups increased at first and then decreased, while oxygenation index in the two groups decreased at first and then increased, and the differences in the same groups at the adjacent time points were statistically significant (P<0.05). The cardiac index in the two groups decreased at first and then increased, the differences in the same groups at the adjacent time points were statistically significant, except for 6 h and 12 h after surgery in the bosentan group (P>0.05). At postoperative 6 h, 12 h, the oxygenation index in the NO group was significantly higher than that in the bosentan group, and the pulmonary/systemic pressure ratio in the NO group was less than that in the bosentan group (P<0.01). The cardiac index in the NO group was higher than that of the bosentan group after 6 h, 12 h, 24 h of operation, which were statistically significant (P<0.05), and the cardic index of children in the NO group was greatly higher than that in the bosentan group after 12 h of surgery (P<0.01); at the same time point, the corresponding indexes were not statistically significant between the two groups (P>0.05).
Conclusion
NO inhalation in the treatment of IAA with VSD and SPH in children with early postoperative SPH is better than the bosentan, but in the late postoperative period, the effect is similar.
【Abstract】 Objective To summarize the current development of the correction of severe and rigid scol iosis. Methods Recent l iterature concerning the correction of severe and rigid scol iosis at home and abroad was extensively reviewed, and current developments of the correction of severe and rigid scol iosis were summarized. Results The correction of severe and rigid scol iosis shows developments as follows: the application of Halo-gravity traction increase and Halo-femoral traction is applied in posterior correction surgery. Fixation and correction technique with all pedicle screws was gradually popularized. The applications of posterior vertebral column resection, one-stage anterior and posterior surgery, and posterior-only correction surgery increase. Conclusion The developments of all kinds of correction techniques improve the correction effects of severe and rigid scol iosis. Now there is no standardized treatment protocol for severe and rigid scol iosis. Greater development can be expected in the future.
