SEALONE (Safety and Efficacy of Coronary Computed Tomography Angiography with Low Dose in Patients Visiting Emergency Room) trial: study protocol for a randomized controlled trial

Objective Chest pain is one of the most common complaints in the emergency department (ED). Cardiac computed tomography angiography (CCTA) is a frequently used tool for the early triage of patients with low- to intermediate-risk acute chest pain. We present a study protocol for a multicenter prospective randomized controlled clinical trial testing the hypothesis that a low-dose CCTA protocol using prospective electrocardiogram (ECG)-triggering and limited-scan range can provide sufficient diagnostic safety for early triage of patients with acute chest pain. Methods The trial will include 681 younger adult (aged 20 to 55) patients visiting EDs of three academic hospitals for acute chest pain or equivalent symptoms who require further evaluation to rule out acute coronary syndrome. Participants will be randomly allocated to either low-dose or conventional CCTA protocol at a 2:1 ratio. The low-dose group will undergo CCTA with prospective ECG-triggering and restricted scan range from sub-carina to heart base. The conventional protocol group will undergo CCTA with retrospective ECG-gating covering the entire chest. Patient disposition is determined based on computed tomography findings and clinical progression and all patients are followed for a month. The primary objective is to prove that the chance of experiencing any hard event within 30 days after a negative low-dose CCTA is less than 1%. The secondary objectives are comparisons of the amount of radiation exposure, ED length of stay and overall cost. Results and Conclusion Our low-dose protocol is readily applicable to current multi-detector computed tomography devices. If this study proves its safety and efficacy, dose-reduction without purchasing of expensive newer devices would be possible

Healthcare Information Technology and Hospital Performance: Diversity and Velocity

This article examines how two features of technology adoption affect hospital performance. The current study contributes to our understanding of the IT-performance link in a hospital setting by exploring the effects of healthcare information technology (HIT) diversity and the pace of HIT diversity change in a hospital over time on hospital performance. Employing a panel data set of U.S. hospitals from 2008 to 2013, the research finds that HIT diversity is positively associated with hospital performance, while velocity is negatively associated with it

Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors

Under thin film deposition, when used in conjunction with the semiconductor atomic layer deposition (ALD) method, the choice of precursor determines the properties and quality of the thin film. Organometallic precursors such as alkaline earth metals (Sr and Ba) and group 4 transition metals (Zr and Hf) with cyclopentadienyl and tetrakis (ethylmethylamino) ligands have recently gained attention for their stable deposition within high-temperature windows in the ALD. The design of organometallic precursors with an ab initio molecular dynamics (AIMD) simulations-based approach ensures high accuracy but comes with significant computational costs. In this study, we aim to develop a machine-learning interatomic potential (MLIP) through moment tensor potential (MTP) for fast and accurate potential development of Sr, Ba, Zr, and Hf precursors. To establish the reliable training database for MTP construction, we conducted AIMD simulations on each precursor across a range of temperature settings, resulting in a variety of atomic structures. Constructed MTPs enable efficient utilization of molecular dynamics (MD) simulations as well as calculations that achieve an accuracy that approximates density functional theory (DFT). MTP construction coupled with active learning ensures that the MTP for each precursor is reliable and that databases can be expanded. High prediction accuracy is demonstrated by a mean absolute error (MAE) of less than 0.04 eV/atom in all structures. In addition, generalization performance is confirmed for general structures (structures with the same chemical elements but different proportions) and is extended to cluster structures. The constructed MTP exhibits an MAE of less than 0.15 eV/atom, even for untrained cluster structures. These results demonstrate adequate representation and scalability as a basis for the development of MLIPs capable of atomic simulations of organometallic precursors under various thermodynamic conditions

Long-term cardiovascular risk of hypertensive events in emergency department: A population-based 10-year follow-up study.

Hypertension-related visits to the emergency department (ED) are increasing every year. Thus, ED could play a significant role in detecting hypertension and providing necessary interventions. However, it is not known whether a hypertensive event observed in the ED is an independent risk factor for future major adverse cardiovascular events (MACE).A population-based observational study was conducted using a nationally representative cohort that contained the claim data of 1 million individuals from 2002 to 2013. We included non-critical ED visits without any history of MACE, and compared the new occurrences of MACE according to the presence of hypertensive events using extended Cox regression model. The disease-modifying effect of a follow-up visit was assessed by analyzing the interaction between hypertensive event and follow-up visit.Among 262,927 first non-critical ED visits during the study period (from 2004 to 2013), 6,243 (2.4%) visits were accompanied by a hypertensive event. The hypertensive event group had a higher risk of having a first MACE at 3 pre-specified intervals: 0-3 years (HR, 4.25; 95% CI, 3.83-4.71; P<0.001), 4-6 years (HR, 3.65; 95% CI, 3.14-4.24; P<0.001), and 7-10 years (HR, 3.20; 95% CI, 2.50-4.11; P<0.001). Follow-up visits showed significant disease-modifying effect at 2 intervals: 0-3 years (HR 0.65, 95% CI, 0.50-0.83) and 4-7 years (HR 0.68, 95% CI, 0.48-0.95).A hypertensive event in the ED is an independent risk factor for MACE, and follow-up visits after the event can significantly modify the risk

Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural networkResearch in context

Background: Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images. Methods: Three hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital. Findings: For the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%. Interpretation: DL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features. Fund: This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund. Keywords: Convolutional neural network, Deep learning, Moyamoya, Skul

Baseline characteristics of study population.

<p>Baseline characteristics of study population.</p

Multivariable Cox-regression model for major adverse cardiovascular event.

<p>Multivariable Cox-regression model for major adverse cardiovascular event.</p

Effect size of ED hypertensive event with and without follow-up visit.

<p>Effect size of ED hypertensive event with and without follow-up visit.</p

Flow diagram of case selection and group allocation.

<p>(ED, emergency department; HTN, hypertension; OPD, out-patient department).</p