Diagnostic value of 320-slice coronary CT angiography in coronary artery disease: A systematic review and meta-analysis

The aim of this study is to perform a systematic review and meta-analysis of the diagnostic value of 320-slice coronary computed tomography (CT) angiography in the diagnosis of coronary artery disease when compared to invasive coronary angiography. A search of different databases was conducted to identify studies investigating the diagnostic value of 320-slice coronary CT angiography. Sensitivity, specificity, positive and negative predictive value estimates pooled across studies were tested using a fixed effects model and analysed at patient-, vessel- and segment-based assessment. Twelve studies comprising 1592 patients (median, 63 patients, range, 37-240 patients) with a total of 2974 vessels and 21623 segments met selection criteria for inclusion in the analysis. Patients with a high prevalence of coronary artery disease were included in more than 70% of these studies. The mean values and 95% confidence interval (CI) of sensitivity, specificity, positive predictive value and negative predictive value of 320-slice coronary CT angiography were 96.3% (95% CI: 92.9%, 99.8%), 86.4% (95% CI: 77.8%, 94.9%), 89.6% (95% CI: 85.6%, 93.6%) and 93.2% (95% CI: 84.1%, 100%), at patient-based analysis; 91.8% (95% CI: 85.8%, 97.8%), 95.4% (95% CI: 93.6%, 97.1%), 85.9% (95% CI: 79.7%, 92%) and 97.4% (95% CI: 95.9%, 99.1%), at vessel-based analysis; 86.2% (95% CI: 81.8%, 90.6%), 96.5% (95% CI: 95.2%, 98%), 79.9% (95% CI: 75.3%, 84.6%) and 97.8% (95% CI: 96.7%, 99%), at segment-based analysis, respectively. The mean effective dose of 320-slice coronary CT angiography was 10.5 mSv (95% CI: 6.1, 14.8 mSv). Diagnostic value of 320-slice coronary CT angiography was not affected by different heart rates and calcium scores (p>0.05). This analysis shows that 320-slice coronary CT angiography has high diagnostic value in patients with high coronary artery disease prevalence. Relatively high radiation dose is mainly due to inclusion of patients with high heart rates and without using the advanced dose-reduction techniques, thus, further dose-saving strategies should be implemented to minimise the resultant radiation dose

Ensemble methods for testing a global null

Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or classification, we propose an ensemble framework for testing that mimics the spirit of random forests to deal with the challenges. Our ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power for global nulls. We apply the framework to four problems about global testing in different classes of alternatives arising from Whole Genome Sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and an analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.Comment: Accepted by NeurIPS 202

Optimization of chest radiographic imaging parameters: a comparison of image quality and entrance skin dose for digital chest radiography systems

We studied the performance of three computed radiography and three direct radiography systems with regard to the image noise and entrance skin dose based on a chest phantom. Images were obtained with kVp of 100, 110, and 120 and mA settings of 1, 2, 4, 8, and 10. Significant differences of image noise were found in these digital chest radiography systems (Pb<0001). Standard deviation was significantly different when the mAs were changed (Pb<001), but it was independent of the kVp values (P=.08–.85). Up to 44% of radiation dose could be saved when kVp was reduced from 120 to 100 kVp without compromising image quality

Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations. Our proposed method combines bottom-up and top-down cues for novel class detection. Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes. With the estimated regions on the web images, we then utilize the top-down attention cues as the guidance for region classification. Furthermore, we propose a residual feature refinement (RFR) block to tackle the domain mismatch between web domain and the target domain. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits. Without any target-domain novel-class images and annotations, our proposed webly supervised object detection model is able to achieve promising performance for novel classes. Moreover, we also conduct transfer learning experiments on large scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance

Follow-up management service and health outcomes of hypertensive patients in China: A cross-sectional analysis from the national health service survey in Jiangsu province

BackgroundHypertension is a major cause of early mortality worldwide. Health follow-up management services can encourage patients with hypertension to improve their health behavior and outcomes. However, a lack of studies on the relationship between specific factors of follow-up management and both subjective and objective health outcome among hypertensive patients exists. The current study investigated the relationship between service content, frequency, mode, and institutions of follow-up management and health outcomes among Chinese hypertensives.MethodsData were obtained from the sixth National Health Service Survey (NHSS) of Jiangsu Province, which was conducted in 2018. Descriptive statistics were used to analyze the sample characteristics and the utilization of follow-up management services. Both multiple linear regression and logistic regression were used to estimate the association of follow-up management service and other factors with hypertensives' subjective and objective health outcomes.ResultSome respondents (19.30%) reported hypertension, and 75.36% of these patients obtained follow-up management services. Hypertensive patients' subjective health outcome self-reported health status and objective health outcome blood pressure (BP) control were found to be significantly associated with follow-up management services. The outcomes were both significantly improved by a high frequency of management services, a high level of follow-up providers, the mode of visiting healthcare facilities and/or calling, and receiving instructions on medication use. However, inquiring about patients' symptoms was negatively associated with self-reported health status and BP control. In addition, BP measurement was significantly and positively associated with hypertensive patients' self-reported health status; the patients receiving lifestyle guidance were more likely to have their BP levels under control.ConclusionsHypertension management strategies should further focus on the frequency of healthcare follow-up management via categorization of the follow-up services and appropriate adjustment of service delivery modes to optimize health follow-up management for hypertensives further improve their outcomes. Meanwhile, complementary policies are also needed to address other socioeconomic factors that can promote good health conditions for hypertension patients