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Objectives: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. Methods: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. Results: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7–4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69–12.48) for the number of plaques with spotty calcification, 3.73 (1.46–9.52) for the number of plaques with low attenuation component, 2.71 (1.62–4.50) for 25–49% stenosis severity, 1.47 (1.17–1.84) for the number of bifurcation plaques, and 1.21 (1.02–1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676–0.788) and 0.668 (0.583–0.752) in the derivation and validation cohorts, respectively. Conclusions: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. Clinical relevance statement: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-u

Although commercial entities can contribute positively to health and society there is growing evidence that the products and practices of some commercial actors-notably the largest transnational corporations-are responsible for escalating rates of avoidable ill health, planetary damage, and social and health inequity; these problems are increasingly referred to as the commercial determinants of health. The climate emergency, the non-communicable disease epidemic, and that just four industry sectors (ie, tobacco, ultra-processed food, fossil fuel, and alcohol) already account for at least a third of global deaths illustrate the scale and huge economic cost of the problem. This paper, the first in a Series on the commercial determinants of health, explains how the shift towards market fundamentalism and increasingly powerful transnational corporations has created a pathological system in which commercial actors are increasingly enabled to cause harm and externalise the costs of doing so. Consequently, as harms to human and planetary health increase, commercial sector wealth and power increase, whereas the countervailing forces having to meet these costs (notably individuals, governments, and civil society organisations) become correspondingly impoverished and disempowered or captured by commercial interests. This power imbalance leads to policy inertia; although many policy solutions are available, they are not being implemented. Health harms are escalating, leaving health-care systems increasingly unable to cope. Governments can and must act to improve, rather than continue to threaten, the wellbeing of future generations, development, and economic growth.

IMPORTANCE Both nonsuicidal self-injury (NSSI), defined as the direct, deliberate damage of one's body tissue without suicidal intent, and internet addiction among adolescents are public health concerns. However, the possible association of NSSI with internet addiction is not well understood. OBJECTIVE To examine the occurrence of internet addiction with NSSI and any sex differences among Chinese adolescents. DESIGN, SETTING, AND PARTICIPANTS A multicenter, cross-sectional, survey study was conducted from February 18 to October 15, 2015, among adolescents aged 11 to 20 years from 343 classes in 45 public high schools across 5 provinces of China. Data analysis was performed from August 1, 2018, to March 1, 2019. EXPOSURES Possible internet addiction and internet addiction. MAIN OUTCOMES AND MEASURES Less-frequent (1-4 times) NSSI and more-frequent (5 times) NSSI were surveyed using the Chinese version of the Functional Assessment of Self-Mutilation. RESULTS A total of 15 623 students (8043 male [51.5%] and 7580 female [48.5%]) aged 11 to 20 years (mean [SD] age, 15.1 [1.8] years) participated. Of these, 4670 participants (29.9%) met the criteria for possible internet addiction and 509 participants (3.3%) met the criteria for internet addiction. A total of 2667 students (17.1%) engaged in less-frequent NSSI, whil

Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): A Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.