[Causes of death and influencing factors of atrial fibrillation patients undergoing anticoagulation therapy].

Objective: To investigate the causes of death and predictors in patients with nonvalvular atrial fibrillation (AF) undergoing anticoagulation therapy. Methods: Consecutive anticoagulated nonvalvular AF patients were recruited from the China Atrial Fibrillation Registry (China-AF) Study from August 2011 to December 2018. After exclusion of patients with hypertrophic cardiomyopathy, dilated cardiomyopathy, or loss of follow-up within 1 year, 2 248 patients were included in this analysis. Enrolled patients were followed up were followed up for 3 and 6 months, and then every 6 months. The primary endpoint was death, including cardiovascular death, non-cardiovascular death and undetermined death. The patients were divided into survival group and death group according to the survival status after follow-up. Clinical information such as age and sex was collected. Cox proportional hazards regression was performed to identify associated risk factors for all-cause mortality, and Fine-Gray competing risk model was used to identify associated risk factors for cardiovascular mortality. Results: A total of 2 248 patients with atrial fibrillation receiving anticoagulant therapy died over a mean follow-up of (42±24) months, mean age was (67±10) years old and 41.1% (923/2 248) patients were female. The mortality rate was 2.8 deaths per 100 patient-years. The most common cause of death was cardiovascular deaths, accounted for 55.0% (120/218). Worsening heart failure was the most common cause of cardiovascular deaths (18.3% (40/218)), followed by bleeding events (12.9% (28/218)) and ischemic stroke (8.7% (19/218)). Multivariate Cox regression analysis showed that age (HR = 1.05, 95%CI 1.04-1.07, P<0.001), anemia (HR = 1.81, 95%CI 1.02-3.18, P = 0.041), heart failure (HR=2.40, 95%CI 1.75-3.30, P<0.001), ischemic stroke/transient ischemic attack (TIA)(HR = 1.59, 95%CI 1.21-2.13, P = 0.001) and myocardial infarction (HR = 2.93, 95%CI 1.79-4.81, P<0.001) were independently associated with all-cause death. Fine-Gray competing risk model showed that age (HR=1.05, 95%CI 1.02-1.08, P<0.001), heart failure (HR=2.81, 95%CI 1.79-4.39, P<0.001), ischemic stroke/TIA (HR=1.50, 95%CI 1.02-2.22, P=0.041) and myocardial infarction (HR=3.31, 95%CI 1.72-6.37, P<0.001) were independently associated with cardiovascular death. Conclusions: In anticoagulated nonvalvular AF patients, ischemic stroke represents only a small subset of deaths, whereas worsening heart failure is the most common cause of cardiovascular deaths. Heart failure, ischemic stroke/TIA, and myocardial infarction are associated with increased mortality.

[Application of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients].

Objective: To investigate the diagnostic efficacy and potential application value of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients. Methods: A total of 403 patients, including 254 males and 149 females aged from 16 to 100 (50±19) years, who received emergency treatment for trauma and chest CT examination in the Eastern Theater General Hospital from September 2019 to November 2019 were retrospectively analyzed. Dr. Wise Lung Analyzer's chest CT auxiliary diagnosis system was applied to detect 5 types of injuries, including pneumothorax, pleural effusion/hemothorax, pulmonary contusion (shown as consolidation and ground glass opacity), rib fractures, and other fractures (including thoracic vertebrae, sternum, scapula and clavicle, etc.) and 6 other abnormalities (bullae, emphysema, pulmonary nodules, stripe, reticulation, pleural thickening). The diagnostic reference standards were labeled by two radiologists independently. The sensitivity and specificity of the auxiliary diagnosis system were evaluated. The imaging diagnostic reports were compared with the results of the auxiliary diagnosis system, and the diagnostic consistency between the two was calculated by using the Kappa test. Results: According to the reference standards, among the 403 patients, 29 were pneumothorax, 75 were pleural effusion/hemothorax, 131 were pulmonary contusion, 124 were rib fractures, and 63 were other fractures. The sensitivity and specificity of the auxiliary diagnosis system for detection of pneumothorax, pleural effusion/hemothorax, rib fractures, and other fractures were 96.6%, 97.6%, 80.0%, 99.7%, 99.2%, 83.9%, 84.1%, and 99.7%, respectively. The sensitivity of detecting lung contusion was 97.7%. There was a high consistency between the auxiliary diagnosis system and imaging diagnosis in the diagnosis of injuries, in which the kappa values of pneumothorax, pleural effusion, rib fracture and other fractures were 0.783, 0.821, 0.706 and 0.813, respectively (all P<0.001). Two cases of pneumothorax, three cases of pleural effusion/hemothorax, nine cases of rib fractures, and six cases of other fractures missed by imaging diagnosis were all detected by the auxiliary diagnosis system. The detection sensitivity of the auxiliary diagnosis system was higher for emphysema, pulmonary nodules and stripe (all>85%), but lower for bullae, reticulation and pleural thickening. Conclusions: The deep learning-based chest CT auxiliary diagnosis system could effectively assist chest CT to detect injuries in emergency trauma patients, which was expected to optimize the clinical workflow.