Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence.

This paper introduces a new control strategy for robot manipulators, specifically designed to tackle the challenges associated with traditional model-based sliding mode (SM) controller design. These challenges include the need for accurately computed system models, knowledge of disturbance upper bounds, fixed-time convergence, prescribed performance, and the generation of chattering. To overcome these obstacles, we propose the incorporation of a neural network (NN) that effectively addresses these issues by removing the constraint of a precise system model. Additionally, we introduce a novel fixed-time prescribed performance control (PPC) to enhance response performance and position-tracking accuracy, while effectively limiting overshoot and maintaining steady-state error within the predefined range. To expedite the convergence of the SM surface to its equilibrium point, we introduce a faster terminal sliding mode (TSM) surface and a novel fixed-time reaching control algorithm (RCA) with adaptable factors. By integrating these approaches, we develop a novel control strategy that successfully achieves the desired goals for robot manipulators. The effectiveness and stability of the proposed approach are validated through extensive simulations on a 3-DOF SAMSUNG FARA-AT2 robot manipulator, utilizing both Lyapunov criteria and performance evaluations. The results demonstrate improved convergence rate and tracking accuracy, reduced chattering, and enhanced controller robustness.

Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT.

OBJECTIVES: To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems.

For magnetic levitation systems subject to dynamical uncertainty and exterior perturbations, we implement a real-time Prescribed Performance Control (PPC). A modified function of Global Fast Terminal Sliding Mode Manifold (GFTSMM) based on the transformed error of the novel PPC is introduced; hence, the error variable quickly converges to the equilibrium point with the prescribed performance, which means that maximum overshoot and steady-state of the controlled errors will be in a knowledge-defined boundary. To enhance the performance of Global Fast Terminal Sliding Mode Control (GFTSMC) and to reduce chattering in the control input, a modified third-order sliding mode observer (MTOSMO) is proposed to estimate the whole uncertainty and external disturbance. The combination of the GFTSMC, PPC, and MTOSMO generates a novel solution ensuring a finite-time stable position of the controlled ball and the possibility of performing different orbit tracking missions with an impressive performance in terms of tracking accuracy, fast convergence, stabilization, and chattering reduction. It also possesses a simple design that is suitable for real-time applications. By using the Lyapunov-based method, the stable evidence of the developed method is fully verified. We implement a simulation and an experiment on the laboratory magnetic levitation model to demonstrate the improved performance of the developed control system.

Long-Term Examination of Water Chemistry Changes Following Treatment of Cyanobacterial Bloom with Coagulants and Minerals.

The abundant growth in cyanobacterial blooms poses severe ecological threats with a high risk to aquatic organisms and global public health. Control of cyanobacterial blooms involves spraying cyanobacteria removal materials, including coagulants. However, little is known about the fate of the coagulated-cyanobacteria-laden water. Here, we examined long-term changes in water quality following treatment with various coagulants and minerals for cyanobacterial removal when the coagulated cyanobacterial cells were not removed from the water. An experiment in a controlled water system tested the effects of six different compounds, one conventional coagulant, two natural inorganic coagulants, and three minerals. All tested coagulants and minerals exhibited >75% of cyanobacterial removal efficiency. However, compared to the control, higher concentrations of nitrogen were observed from some samples treated during the experimental period. After 20 months, the final total phosphorus concentration of the raw water increased 20-fold compared to the initial concentration to 11.82 mg/L, indicating significant nutrient release over time. Moreover, we observed that the decomposition of sedimented cyanobacterial cells caused the release of intracellular contents into the supernatant, increasing phosphorous concentration over time. Therefore, cyanobacterial cells should be removed from water after treatment to prevent eutrophication and maintain water quality.

An Adaptive Prescribed Performance Tracking Motion Control Methodology for Robotic Manipulators with Global Finite-Time Stability.

In this paper, the problem of an APPTMC for manipulators is investigated. During the robot's operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Afterward, a series of transformed errors is used to convert "constrained" systems into equivalent "unconstrained" ones, to facilitate control design. The modified PPFs ensure position tracking errors are managed in a pre-designed performance domain. Especially, the SSE boundaries will be symmetrical to zero, so when the transformed error is zero, the tracking error will be as well. Secondly, a modified NISMS based on the transformed errors allows for determining the highest acceptable range of the tracking errors in the steady-state, finite-time convergence index, and singularity elimination. Thirdly, a fixed-time USOSMO is proposed to directly estimate the lumped uncertainty. Fourthly, an ASTwCL is applied to deal with observer output errors and chattering. Finally, an observer-based-control solution is synthesized from the above techniques to achieve PCP in the sense of finite-time Lyapunov stability. In addition, the precision, robustness, as well as harmful chattering reduction of the proposed APPTMC are improved significantly. The Lyapunov theory is used to analyze the stability of closed-loop systems. Throughout simulations, the proposed PPTMC has been shown to perform well and be effective.

Seven Days of Bismuth-Based Quadruple Therapy Is as Effective for the First-Line Treatment of Clarithromycin-Resistant Confirmed Helicobacter pylori Infection as 14 Days of Bismuth-Based Quadruple Therapy.

BACKGROUND/AIMS: Point mutations in the 23S ribosomal RNA gene have been associated with Helicobacter pylori (H. pylori) clarithromycin resistance and bismuth-based quadruple therapy (BQT) is one of the options for the treatment of clarithromycin-resistant H. pylori. Current H. pylori treatment guidelines recommend BQT for 10-14 days. This study aims to compare the eradication extents according to 7-day and 14-day BQT treatment for treatment-naïve clarithromycin-resistant confirmed H. pylori infection. METHODS: We retrospectively investigated treatment-naïve H. pylori infection cases from March 2019 to December 2020, where patients were treated with BQT. Clarithromycin resistance was identified with a dual-priming oligonucleotide-based multiplex polymerase chain reaction method. We reviewed a total of 126 cases. Fifty-three subjects were treated with a 7-day BQT regimen (7-day group), and 73 subjects were treated with a 14-day BQT regimen (14-day group). We evaluated the total eradication extent of the BQT and compared the eradication extents of the two study groups. RESULTS: Total eradication extent of H. pylori was 83.3% (105/126). The eradication extents of the two groups were as follows: 7-day group (81.1% (43/53)), 14-day group (84.9% (62/73), p = 0.572) by intention-to-treat analysis; 7-day group (95.6% (43/45)), 14-day group (92.5% (62/67), p = 0.518) by per-protocol analysis. The moderate or severe adverse event extents during the eradication were 30.2% (16/53) in the 7-day group and 19.2% (14/73) in the 14-day group (p = 0.152). CONCLUSIONS: The 7-day BQT regimen was as effective as the 14-day BQT regimen in the eradication of treatment-naïve clarithromycin-resistant H. pylori infection.

Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.

BACKGROUND: Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited. OBJECTIVE: We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery. METHODS: EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment. RESULTS: GBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results. CONCLUSIONS: Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators.

Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error transformation to guarantee a prescribed performance within a finite time. Following the definitions mentioned, a modified integral nonlinear sliding-mode hyperplane is constructed from the transformed errors. By using the designed nonlinear sliding-mode surface and the super-twisting reaching control law, an advanced approach to the prescribed performance control was formed for the trajectory tracking control of uncertain robotic manipulators. The proposed controller exhibits improved properties, including estimated convergence speed and a predefined upper and lower limit for maximum overshoot during transient responses. Furthermore, the maximum allowable size of the control errors at the steady-state can be predefined and these errors will inevitably converge to zero within a finite time, while the proposed controller can provide a smooth control torque without the loss of its robustness. It is shown that the proposed control system is globally stable and convergent over a finite time. A comprehensive analysis of the effectiveness of the proposed control algorithm was already conducted via the simulation of an industrial robot manipulator.

A Novel Active Fault-Tolerant Tracking Control for Robot Manipulators with Finite-Time Stability.

Many terminal sliding mode controllers (TSMCs) have been suggested to obtain exact tracking control of robotic manipulators in finite time. The ordinary method is based on TSMCs that secure trajectory tracking under the assumptions such as the known robot dynamic model and the determined upper boundary of uncertain components. Despite tracking errors that tend to zero in finite time, the weakness of TSMCs is chattering, slow convergence speed, and the need for the exact robot dynamic model. Few studies are handling the weakness of TSMCs by using the combination between TSMCs and finite-time observers. In this paper, we present a novel finite-time fault tolerance control (FTC) method for robotic manipulators. A finite-time fault detection observer (FTFDO) is proposed to estimate all uncertainties, external disturbances, and faults accurately and on time. From the estimated information of FTFDO, a novel finite-time FTC method is developed based on a new finite-time terminal sliding surface and a new finite-time reaching control law. Thanks to this approach, the proposed FTC method provides a fast convergence speed for both observation error and control error in finite time. The operation of the robot system is guaranteed with expected performance even in case of faults, including high tracking accuracy, small chattering behavior in control input signals, and fast transient response with the variation of disturbances, uncertainties, or faults. The stability and finite-time convergence of the proposed control system are verified that they are strictly guaranteed by Lyapunov theory and finite-time control theory. The simulation performance for a FARA robotic manipulator proves the proposed control theory's correctness and effectiveness.

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

BACKGROUND: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management. OBJECTIVE: First, this study aims to develop a machine learning (ML)-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. METHODS: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. RESULTS: We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. CONCLUSIONS: In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

A Robust Observer-Based Control Strategy for n-DOF Uncertain Robot Manipulators with Fixed-Time Stability.

In this paper, a robust observer-based control strategy for n-DOF uncertain robot manipulators with fixed-time stability was developed. The novel fixed-time nonsingular sliding mode surface enables control errors to converge to the equilibrium point quickly within fixed time without singularity. The development of the novel fixed-time disturbance observer based on a uniform robust exact differentiator also allows uncertain terms and exterior disturbances to be proactively addressed. The designed observer can accurately approximate uncertain terms within a fixed time and contribute to significant chattering reduction in the traditional sliding mode control. A robust observer-based control strategy was formulated, according to a combination of the fixed-time nonsingular terminal sliding mode control method and the designed observer, to yield global fixed time stability for n-DOF uncertain robot manipulators. The proposed controller proved definitively that it was able to obtain global stabilization in fixed time. The approximation capability of the proposed observer, the convergence of the proposed sliding surface, and the effectiveness of the proposed control strategy in fixed time were fully confirmed by simulation performance on an industrial robot manipulator.

Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts.

OBJECTIVE: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. MATERIALS AND METHODS: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. RESULTS: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). CONCLUSION: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

Deep learning segmentation of major vessels in X-ray coronary angiography.

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

The Challenge With Clinical Radiology Electives: Student and Faculty Perspectives Identify Areas for Improvement.

PURPOSE: To determine medical students' and radiologists' attitude toward radiology electives at a distributed medical school and identify specific areas for improvement. METHODS: During a single academic year, both students and faculty preceptors were surveyed anonymously following a senior radiology elective. The survey was based on an established theoretical framework for studying the educational environment which takes into account domains: (1) goal orientation, (2) organization/regulation, and (3) relationships. Mann-Whitney tests were performed to determine if there was any difference between the overall satisfaction of students and preceptors, responses from the different elective sites and students' ratings of the domains. Statistical significance was set at P < .05. Thematic analysis was performed on the narrative comments to identify specific challenges. RESULTS: The response rate was 82.0% for students (95/116) and 19.5% (31/159) for radiologists. There was no difference in responses based on elective site. Overall, the elective was viewed positively by both groups however students rated their experience as significantly better than their preceptors (P = .0012). Students viewed the relationships domain more positively than both the other two (goal orientation, P = .0001; organization/regulation, P = .0038). Thematic analysis identified that the student challenges were lack of autonomy, structured teaching, and preceptor continuity and the preceptor challenges were ambiguous learning objectives/expectations and insufficient resources. CONCLUSIONS: The radiology elective challenges identified in this study provide educators with specific areas to target when updating radiology electives. A better elective experience may improve students' radiology knowledge and attitude towards the specialty as well as radiologists' interest in teaching.

Randomized Trial Evaluating Percutaneous Coronary Intervention for the Treatment of Chronic Total Occlusion.

BACKGROUND: Procedural results for percutaneous coronary intervention (PCI) in coronary vessels with chronic total occlusion (CTO) have improved in recent years, and PCI strategies have moved toward more complete revascularization with more liberal use of CTO-PCI. However, evidence evaluating CTO-PCI is limited to observational studies and small clinical trials. METHODS: In this open-label, multicenter, randomized, noninferiority trial, PCI-eligible patients were assigned to receive either 1 of 2 strategies: PCI or no PCI for the qualifying de novo CTO lesion with the option for PCI of obstructive non-CTO lesions at the discretion of the operator. The primary end point was a composite of death, myocardial infarction, stroke, or any revascularization. Health-related quality of life was assessed at baseline and at 1, 6, 12, 24, and 36 months. Because of slow recruitment, the trial was stopped before completion of the 1284 planned enrollments. RESULTS: Between March 2010 and September 2016, 834 patients were randomly assigned to the CTO-PCI (n=417) or no CTO-PCI (n=398) strategy. Among the patients assigned to the no CTO-PCI strategy, 78 (19.6%) crossed over to receive staged CTO-PCI within 3 days of randomization. The overall CTO-PCI success rate was 90.6%. Serious nonfatal complications associated with CTO-PCI occurred in 3 patients (1 stroke, 1 cardiac tamponade, and 1 patient with recurrent episodes of ventricular tachyarrhythmia induced by intracoronary thrombus). Approximately half of the patients in each group underwent PCI for an average of 1.3 non-CTO lesions, resulting in a comparable residual SYNTAX score (Synergy Between PCI With TAXUS and Cardiac Surgery; 3.7±5.4 versus 4.0±5.9, P=0.42) confined to non-CTO vessels. During a median follow-up of 4.0 years (interquartile range, 2.4 to 5.1 years), there was no significant difference between the CTO-PCI and the no CTO-PCI strategies in the incidence of the primary end point (22.3% versus 22.4%, hazard ratio, 1.03; 95% CI, 0.77 to 1.37; P=0.86). Both CTO-PCI and no CTO-PCI strategy were associated with significant improvements but without between-group differences in disease-specific health status that was sustained through 36 months. CONCLUSIONS: CTO-PCI was feasible with high success rates. There was no difference in the incidence of major adverse cardiovascular events with CTO-PCI versus no CTO-PCI, but the study was limited by low power for clinical end points and high crossover rates between groups. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01078051.

Effect of different doses of gadolinium contrast agent on clinical outcomes in MS.

OBJECTIVE: The objective of this paper is to evaluate potential dose-dependent adverse effects of gadolinium-based contrast agents (GBCAs) on MS progression. METHODS: Outcomes from a cohort of 612 secondary progressive MS (SPMS) patients, enrolled in a two-year, placebo-controlled (negative) trial assessing the efficacy of MBP8298, were acquired. Patients received one to four (infrequent cohort; IFR) or 5-11 (frequent cohort; FR) GBCA injections between week 4 and week 104. The primary outcome was the change in Expanded Disability Status Scale (EDSS) and time to confirmed EDSS progression. Secondary outcomes included the changes in the Multiple Sclerosis Functional Composite (MSFC), Timed 25-Foot Walk (T25FW), 9-Hole-Peg Test (9HPT), and Paced Auditory Serial Addition Test (PASAT) from baseline to week 104. RESULTS: The 512 IFR and 100 FR participants showed no differences in baseline demographics or disease history. The mean change from baseline to week 104 in EDSS was +0.21 (IFR) and +0.13 (FR); MSFC -0.38 (IFR) and -0.14 (FR); T25FW +1.28 (IFR) and +0.55 (FR); 9HPT -0.06 (IFR) and -0.08 (FR); and PASAT +0.22 (IFR) and +0.20 (FR). The FR to IFR progression hazard ratio equaled 0.68 (p = 0.09). There were no significant differences in any of the outcomes between the two cohorts. CONCLUSION: There were no differences in the disability progression measures between the two cohorts, indicating that gadolinium does not result in greater clinical worsening in SPMS after a two-year period.

Virtual monoenergetic reconstruction of contrast-enhanced CT scans of the abdomen and pelvis at 40 keV improves the detection of peritoneal metastatic deposits.

PURPOSE: To evaluate the role of virtual monoenergetic imaging (VMI) in the detection of peritoneal metastatic disease in contrast-enhanced computed tomography (CT) of the abdomen and pelvis and to compare this technique to the conventional 120 kV mixed dataset. MATERIALS AND METHODS: Institutional review board approval was obtained with no informed consent required for this retrospective analysis. 43 consecutive patients with histopathologically confirmed peritoneal disease were scanned using a standard protocol on a 128-section dual-source, dual-energy CT system (100/140 keV). Scans were retrospectively reconstructed at VMI energy levels from 40-110 keV in 10 keV increments and were analyzed both quantitatively and qualitatively. CNR values for peritoneal metastatic deposits were recorded using region of interest (ROI) analysis at each energy level for all VMI datasets. Subjective analysis was performed by two independent fellowship-trained readers with combined experience of greater than 15 years. Qualitative parameters included diagnostic acceptability, subjective noise, and contrast resolution and confidence. RESULTS: The contrast-to-noise ratios (CNRs) for peritoneal metastatic deposits at the different VMI energy levels were compared using a one-way ANOVA with Tukey Post Test, and the optimal CNR was observed at 40 keV (p < 0.0001). Qualitative parameters were compared using a Paired T Test. Subjective noise, diagnostic acceptability, and contrast resolution was significantly better on the conventional images, but readers reported increased confidence on VMI at 40 keV (p < 0.001). CONCLUSION: VMI reconstruction of contrast-enhanced dual-energy CT scans of the abdomen and pelvis at 40 keV maximizes the conspicuity of metastatic peritoneal deposits and improves radiologists' diagnostic confidence compared with conventional CT images. We recommend using virtual monoenergetic datasets at 40 keV as a tool for improving the detection of these lesions in routine clinical practice.

Virtual monoenergetic reconstruction of contrast-enhanced dual energy CT at 70keV maximizes mural enhancement in acute small bowel obstruction.

PURPOSE: In patients with small bowel obstruction (SBO), it is challenging to detect early ischemia. The purpose of this study is to evaluate the quantitative and qualitative benefits of virtual monoenergetic image (VMI) reconstruction in the assessment of small bowel mural enhancement on dual source dual energy computed tomography (CT) scans of the abdomen. MATERIALS AND METHODS: Institutional review board approval was obtained, for this retrospective analysis. 72 consecutive patients with acute SBO were scanned using a second generation 128-slice dual source, CT system. Images were reconstructed at VMI energy levels from 40 to 110keV in 10keV increments and were analysed quantitatively and qualitatively. Contrast to noise ratios (CNR) and signal to noise ratios (SNR) for mural enhancement were recorded for all VMI datasets and compared to conventional polychromatic images (PCI) at 120kVp. Subjective analysis of mural enhancement on VMI and PCI was performed by 3 blinded readers. RESULTS: Optimal CNR values for small intestinal mural enhancement were observed at 70keV. Qualitative assessment revealed that there was no statistical difference in diagnostic accuracy between VMI and PCI. All readers reported improved confidence when assessing the contrast enhancement on the 70keV VMI dataset and in our series, 2 additional cases of ischemia were identified on this reconstruction. CONCLUSION: Contrast-enhanced dual source dual energy CT with VMI reconstruction at 70keV maximizes the CNR of small bowel mural enhancement and increases the overall diagnostic confidence in assessing mural enhancement in patients with SBO.