Compared with left atrial (LA) dimension, LA emptying fraction (LAEF) has received less emphasis as a predictor of atrial fibrillation (AF) recurrence after radiofrequency catheter ablation (RFCA). In addition, patients experiencing post-RFCA AF recurrence may respond to previously ineffective antiarrhythmic drugs (AADs). Classifying these patients into a third RFCA outcome category is recommended. To identify predictors of RFCA outcome classified into three categories, and to build proportional odds logistic regression models for clinical applicability to predict AF recurrence. Data were retrospectively collected from 483 consecutive patients with drug-refractory AF undergoing RFCA (328 men; age 58.4 ± 11.5 years; 383 paroxysmal). Patients were classified into 3 groups based on the last RFCA outcome: group 1, free from AF without AADs; group 2, free from AF with AADs; and group 3, recurrence of AADs-refractory atrial tachyarrhythmia. After a mean follow-up duration of 64.5 ± 43.2 months and mean ablation procedure number of 1.37 ± 0.68, the RFCA outcome showed 76.0%, 9.5% and 14.5% of patients in groups 1, 2, and 3, respectively. In multivariate analysis, LAEF was the most stable and important predictor of AF recurrence, followed by body mass index, stroke, AF duration, mitral regurgitation, and LA linear ablation. For patients undergoing repeat RFCA, LAEF was the only independent predictor (cutoffs: 43% and 35% for groups 1 and 3, respectively). LAEF provides optimal prognostic information regarding the risk stratification of AF patients undergoing RFCA.

Energy expenditure (EE) monitoring is crucial to tracking physical activity (PA). Accurate EE monitoring may help people engage in adequate activity and therefore avoid obesity and reduce the risk of chronic diseases. This study proposes a depth-camera-based system for EE estimation of PA in gyms. Most previous studies have used inertial measurement units for EE estimation. By contrast, the proposed system can be used to conveniently monitor subjects’ treadmill workouts in gyms without requiring them to wear any devices. A total of 21 subjects were recruited for the experiment. Subjects’ skeletal data acquired using the depth camera and oxygen consumption data simultaneously obtained using the K4b$^{2}$ device was used to establish an EE predictive model. To obtain a robust EE estimation model, depth cameras were placed in the side view, rear side view, and rear view. A comparison of five different predictive models and these three camera locations showed that the multilayer perceptron model was the best predictive model and that placing the camera in the rear view provided the best EE estimation performance. The measured and predicted metabolic equivalents of task exhibited a strong positive correlation, with r = 0.94 and coefficient of determination r$^{2}$ = 0.89. Furthermore, the mean absolute error was 0.61 MET, mean squared error was 0.67 MET, and root mean squared error was 0.76 MET. These results indicate that the proposed system is handy and reliable for monitoring user's EE when performing treadmill workouts.

Aims: Curved M-mode images of global strain (GS) and strain rate (GSR) provide sufficiently detailed spatiotemporal information of deformation mechanics. This study investigated whether a deep convolutional neural network (CNN) could accurately classify these images in patients with atrial fibrillation (AF) who underwent radiofrequency catheter ablation (RFCA) with different outcomes.Methods and Results: We retrospectively evaluated 606 consecutive patients who underwent RFCA for drug-refractory AF. Patients were divided into AF-free (n = 443) and AF-recurrent (n = 163) groups. Transthoracic echocardiography was performed within 24 h after RFCA. Left atrial curved M-mode speckle-tracking images were acquired from randomly selected 163 patients in AF-free group and 163 patients in AF-recurrent group as the dataset for deep CNN modeling. We used the ReLu activation function and repeatedly performed CNN model for 32 times to evaluate the stability of hyperparameters. Logistic regression models with the left atrial dimension, emptying fraction, and peak systolic GS as predictor variables were used for comparisons. Images from the apical 2-chamber (2-C) and 4-chamber (4-C) views had distinct features, leading to different CNN performance between settings; of them, the “4-C GS+4-C GSR” setting provided the highest performance index values. All four predictor variables used for logistic regression modeling were significant; however, none of them, individually or in any combined form, could outperform the optimal CNN model.Conclusion: The novel approach using deep CNNs for learning features of left atrial curved M-mode speckle-tracking images seems to be optimal for classifying outcome status after AF ablation.

Purpose: The purpose of this study is to review the levels of open government data (OGD) among various countries that are not consistent with the development levels of those countries. This study evaluates the associativity between OGD Index (OGD) and the characteristics of those countries as well as to compare the degree of OGD among countries. Accordingly, an advanced discussion to explore how a country’s characteristics affect how that country’s government opens data was presented.Design/methodology/approach: The stakeholder relationships of OGD is analysed with the characteristics of a country. The usage data are compared with the data availability according to nine indicators. These data collected from the statistics and OGDI websites are grouped for comparative statistical analyses based on basic descriptive statistics, one-way analysis of variance and a regression model with variance inflation faction.Findings: The results 1) revealed the reasons some countries have high-ranking indexes and 2) verified the high index values of countries in terms of their degrees of development. This study, thus, attempted to derive a balanced appraisal of national development and OGD.Research limitations/implications: The study sample is limited only to countries 1) which open the statistical data; and 2) are of uneven population density and development degree. The OGDI is limited to expert evaluation. The score might be vary to experts and users with diverse countries at different evaluation period. The limitations can be attributed to the differences between OGDI and real open levels. These differences might influence the reliability and validity.Practical implications: Government departments with OGD policies provide raw data in various formats and with application interfaces for user access. This study, thus, attempts to derive a balanced appraisal of national development and OGD. The factors that evaluate which types of countries open the level of data are explored.Originality/value: This study establishes stakeholder relationships of OGD and extends to analyse the characteristics of a country and OGD that affect the government data open level. The relationships are evaluated through the OGDI with design score scheme. The measurement results indicated that a country possesses high relation to open data with high DI and nature resource.

Patients with end-stage kidney disease (ESKD) have a greater risk of comorbidities, including diabetes and anemia, and have higher hospital admission rates than patients with other diseases. The cause of hospital admissions is associated with ESKD prognosis. This retrospective cohort study involved patients with ESKD who received hemodialysis and investigated whether the cause of hospital admission changed before versus after they started hemodialysis. This study recruited 592 patients with ESKD who received hemodialysis at any period between January 2005 and November 2017 and had been assigned the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM) code for ESKD. The patients’ demographic data and hospitalization status one year before and two years after they received hemodialysis were analyzed. A McNemar test was conducted to analyze the diagnostic changes from before to after hemodialysis in patients with ESKD. The study’s sample of patients with ESKD comprised more women (51.86%) than men and had an average age of 67.15 years. The numbers of patients admitted to the hospital for the following conditions all decreased significantly after they received hemodialysis: type 2 (non-insulin-dependent and adult-onset) diabetes; native atherosclerosis; urinary tract infection; gastric ulcer without mention of hemorrhage, perforation, or obstruction; pneumonia; reflux esophagitis; duodenal ulcer without mention of hemorrhage, perforation, or obstruction; and bacteremia. Most patients exhibited one or more of the following comorbidities: diabetes (n = 407, 68.75%), hypertension (n = 491, 82.94%), congestive heart failure (n = 161, 27.20%), ischemic heart disease (n = 125, 21.11%), cerebrovascular accident (n = 93, 15.71%), and gout (n = 96, 16.22%). An analysis of variance (ANOVA) indicated that changes in the ICD-9-CM codes for native atherosclerosis, urinary tract infection, pneumonia, and hyperkalemia were associated with age. Patients who developed pneumonia before or after they received hemodialysis tended to be older (range: 69–70 years old). This study investigated the causes of hospital admission among patients with ESKD one year before and two years after they received hemodialysis. This study’s results revealed hypertension to be the most common comorbidity. Regarding the cause of admission, pneumonia was more prevalent in older than in younger patients. Moreover, changes in the ICD-9-CM codes of native atherosclerosis, urinary tract infection, pneumonia, and hyperkalemia were significantly correlated with age. Therefore, when administering comprehensive nursing care and treatment for ESKD, clinicians should not only focus on comorbidities but also consider factors (e.g., age) that can affect patient prognosis.