Diagnostic Accuracy and Usability of the ECG247 Smart Heart Sensor Compared to Conventional Holter Technology

Background. Heart rhythm disorders, especially atrial fibrillation (AF), are increasing global health challenges. Conventional diagnostic tools for assessment of rhythm disorders suffer from limited availability, limited test duration time, and usability challenges. There is also a need for out-of-hospital investigation of arrhythmias. Therefore, the Norwegian ECG247 Smart Heart Sensor has been developed to simplify the assessment of heart rhythm disorders. The current study aimed to evaluate the diagnostic accuracy and usability of the ECG247 Smart Heart Sensor compared to conventional Holter monitors. Methods. Parallel tests with ECG247 Smart Heart Sensor and a Holter monitor were performed in 151 consecutive patients referred for out-of-hospital long-term ECG recording at Sorlandet Hospital Arendal, Norway. All ECG data were automatically analysed by both systems and evaluated by hospital physicians. Participants were asked to complete a questionnaire scoring usability parameters after the test. Results. A total of 150 patients (62% men, age 54 (±17) years) completed the study. The ECG quality from both monitors was considered satisfactory for rhythm analysis in all patients. AF was identified in 9 (6%) patients during the period with parallel tests. The diagnostic accuracy for automatic AF detection was 95% (95% CI 91–98) for the ECG247 Smart Heart Sensor and 81% (95% CI 74–87) for the Holter system. The proportion of false-positive AF was 4% in tests analysed by the ECG247 algorithm and 16% in tests analysed by the Holter algorithm. Other arrhythmias were absent/rare. The system usability score was significantly better for ECG247 Smart Heart Sensor compared to traditional Holter technology (score 87.4 vs. 67.5, p < 0.001 ). Conclusions. The ECG247 Smart Heart Sensor showed at least comparable diagnostic accuracy for AF and improved usability compared to conventional Holter technology. ECG247 allows for prolonged monitoring and may improve detection of AF. This trial is registered with https://clinicaltrials.gov/ct2/show/NCT04700865.

Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score!

Purpose The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency. Methods Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration. Results Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data. Conclusion MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.

Algorithm 1016

In this article, we introduce the Python framework PyMGRIT, which implements the multigrid-reduction-in-time (MGRIT) algorithm for solving (non-)linear systems arising from the discretization of time-dependent problems. The MGRIT algorithm is a reduction-based iterative method that allows parallel-in-time simulations, i.e., calculating multiple time steps simultaneously in a simulation, using a time-grid hierarchy. The PyMGRIT framework includes many different variants of the MGRIT algorithm, ranging from different multigrid cycle types and relaxation schemes, various coarsening strategies, including time-only and space-time coarsening, and the ability to utilize different time integrators on different levels in the multigrid hierachy. The comprehensive documentation with tutorials and many examples and the fully documented code allow an easy start into the work with the package. The functionality of the code is ensured by automated serial and parallel tests using continuous integration. PyMGRIT supports serial runs suitable for prototyping and testing of new approaches, as well as parallel runs using the Message Passing Interface (MPI). In this manuscript, we describe the implementation of the MGRIT algorithm in PyMGRIT and present the usage from both a user and a developer point of view. Three examples illustrate different aspects of the package itself, especially running tests with pure time parallelism, as well as space-time parallelism through the coupling of PyMGRIT with PETSc or Firedrake.

Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score!

Purpose The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvements to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency. Methods Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration. Results Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data. Conclusion MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used.

Shaking Table Test on the Response of a Cross Interchange Metro Station under Harmonic Excitations Refers to a Single Two-Storey Metro Station

Interchange is essential in a metro network. Regarding the seismic performance, a series of large-scale shaking table tests were performed on an interchange station. The interchange station was composed of a two-story section rigidly connected to a perpendicular three-story section, leading to an abrupt change of stiffness in the conjunction area. Synthetic model soil (a mixture of sand and sawdust) and granular concrete with galvanized steel wires were used to model the soil–structure system. The seismic motion was input along the transversal direction of the two-story structure, including white noise and sinusoidal seismic excitations. Parallel tests of a single two-story station were correspondingly carried out as a contrast. Test data recorded by accelerometers and strain gauges are presented. The bending strains of the columns measured in the interchange station were found to be smaller than those in the single station. The concentration of the longitudinal strain was observed near the conjunction. Insights on the seismic response of the interchange station are provided.

Evaluation of Network-Level Data Collection Variability and its Influence on Pavement Evaluation Utilizing Random Forest Method

The use of pavement condition data to support maintenance and resurfacing strategies and justify budget needs becomes more crucial as more data-driven approaches are being used by the state highway agencies (SHAs). Therefore, it is important to understand and thus evaluate the influence of data variability on pavement management activities. However, owing to a huge amount of data collected annually, it is a challenge for SHAs to evaluate the influence of data collection variability on network-level pavement evaluation. In this paper, network-level parallel tests were employed to evaluate data collection variability. Based on the data sets from the parallel tests, classification models were constructed to identify the segments that were subject to inconsistent rating resulting from data collection variability. These models were then used to evaluate the influence of data variability on pavement evaluation. The results indicated that the variability of longitudinal cracks was influenced by longitudinal lane joints, lateral wandering, and lane measurement zones. The influence of data variability on condition evaluation for state routes was more significant than that for interstates. However, high variability of individual metrics may not necessarily lead to high variability of combined metrics.

Organizational-and-technological solutions to optimize the quality control of building materials

The paper presents the results of the analysis of foreign and Russian experience in the field of research and development of new methods for the accelerated determination of frost resistance of concrete, as well as a description of the experimental method for determining frost resistance by exposing the tested samples to extremely low temperatures. The development of the building materials market, the improvement of construction and installation work technologies require the use of modern high-tech technologies and means, including in the implementation of construction control at unique construction facilities. Currently used regulatory documents of the Russian Federation and standards of foreign countries imply large time costs and high labor intensity. Foreign and domestic scientific research describes other methods of testing using expensive equipment, a large amount of mathematical calculations and mathematical modeling. Reducing the time interval for testing will slightly change. The developed experimental method takes into account the requirements of the regulatory documentation in force on the territory of the Russian Federation, and implies cyclic freezing of samples saturated in a 5% aqueous solution of sodium chloride at extremely low temperatures achieved by using liquid nitrogen. Based on the results of parallel tests in accordance with the requirements of regulatory documents and in accordance with the developed experimental method, a positive convergence of the results was obtained. A significant decrease in the time of testing to determine the frost resistance of concrete was recorded, which allows concluding that it is advisable to use this experimental method, especially if it is necessary to quickly control the quality of concrete at unique construction facilities.

Rapid in situ determination of ClO2 in drinking water by improved solid DPD spectrophotometry

This research aims to realize the rapid detection of ClO2 content in drinking water by adopting improved solid DPD. This method is fast and convenient with low cost and less waste liquid. The results show that this method has good precision and sensitivity. The linear correlation coefficients of the cubic regression equation were all greater than 0.999. The detection limit of the method was 0.002mg/L ClO2. The relative standard deviations (RSD) of seven parallel tests were between 1.37% and 8.87%, and the relative errors were small. The recovery rate was 96.67~110%. The method could be used for the direct determination of water samples with a mass concentration of 0.02mg/L~2.00mg/L in drinking water after ClO2 disinfection.

Algae-Laden Fouling Control by Gravity-Driven Membrane Ultrafiltration with Aluminum Sulfate-Chitosan: The Property of Floc and Cake Layer

Gravity-driven membrane (GDM) ultrafiltration is a promising water treatment method due to its low energy consumption and low maintenance. However, the low stable permeability in algae-laden water treatment is currently limiting its wider application. With the ultimate goal of increasing permeability, the aim of this study was to evaluate the effect of a composite coagulant of aluminum sulfate-chitosan (AS-CS) on the GDM filtration performance. In parallel tests with a single AS coagulant and without pre-coagulation, the analysis of membrane fouling resistance and the membrane fouling mechanism were evaluated. The results indicated that the AS-CS/GDM system can alleviate 23.74% and 58.80% membrane fouling, respectively, compared with AS/GDM and the GDM system. The AS-CS/GDM system can effectively remove humic-like substances having a molecular weight (MW) of 3–100 kDa, resulting in removal of 98.32% of algae cells and removal of 66.25% of dissolved organic carbon; the AS-CS/GDM system thereby improved the concentration of attached biomass on the membrane surface with the stronger biodegradability of organic matters. The application of AS-CS pre-coagulation in the GDM process could enhance the proliferation of microorganisms and the removal of low molecular weight humic-like substances. Therefore, the AS-CS/GDM system is a potentially important approach for algae-laden water treatment.