Patient Care Sitter Reduction and Fall Safety Improvement

The financial constraints imposed upon operational budgets by the frequent use of patient care sitters is well known among hospital leadership. Despite the high labor costs associated with direct and continuous observation, this intervention is routinely deployed by frontline care teams in an effort to preserve patients from harm, particularly from accidental falls. This reality creates an opportunity where significant budget savings can be achieved by supplanting the use of patient care sitters with more effective fall prevention strategies. This quality improvement (QI) project implemented a non-psychiatric sitter reduction and fall prevention initiative in two high volume adult acute care units. Through a collaborative process involving frontline staff, clinical subject matter experts, leadership stakeholders, and medical equipment vendor support, this project implemented a three-fold quality improvement effort including education, policy enhancement, and patient safety supply evaluation. This multi-tier engagement included a 60-day clinical evaluation of the program elements where sitter utilization, fall events, and falls with injury were compared to the organization’s historical performance. The project produced a 46% reduction in sitter utilization within the two trial units. Though fall outcomes were unaffected by this QI project, the initiative produced results commensurate with contemporary evidence that utilization of patient care sitters can be effectively reduced without risk to patient safety

Left and right ventricle assessment with Cardiac CT: validation study vs. Cardiac MR

Objectives To compare Magnetic Resonance (MR) and Computed Tomography (CT) for the assessment of left (LV) and right (RV) ventricular functional parameters. Methods Seventy nine patients underwent both Cardiac CT and Cardiac MR. Images were acquired using short axis (SAX) reconstructions for CT and 2D cine b-SSFP (balanced- steady state free precession) SAX sequence for MR, and evaluated using dedicated software. Results CT and MR images showed good agreement: LV EF (Ejection Fraction) (52±14% for CT vs. 52±14% for MR; r0 0.73; p>0.05); RV EF (47±12% for CT vs. 47±12% for MR; r00.74; p>0.05); LV EDV (End Diastolic Volume) (74± 21 ml/m 2 for CT vs. 76±25 ml/m 2 for MR; r00.59; p>0.05); RV EDV (84±25 ml/m 2 for CT vs. 80±23 ml/m 2 for MR; r0 0.58; p>0.05); LV ESV (End Systolic Volume)(37±19 ml/m 2 for CT vs. 38±23 ml/m 2 for MR; r00.76; p>0.05); RV ESV (46±21 ml/m 2 for CT vs. 43±18 ml/m 2 for MR; r00.70; p>0.05). Intra- and inter-observer variability were good, and the performance of CT was maintained for different EF subgroups. Conclusions Cardiac CT provides accurate and reproducible LVand RV volume parameters compared with MR, and can be considered as a reliable alternative for patients who are not suitable to undergo MR. Key Points • Cardiac-CT is able to provide Left and Right Ventricular function. • Cardiac-CT is accurate as MR for LV and RV volume assessment. • Cardiac-CT can provide accurate evaluation of coronary arteries and LV and RV function

Machine learning based prediction models in male reproductive health: Development of a proof-of-concept model for Klinefelter Syndrome in azoospermic patients

Background Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Objective Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Materials and methods Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Results Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on . Discussion Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Conclusions Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians

Investigating the thermal effect of channel heatsink using MWCNTs nanofluids

The utilisation of Multi-walled carbon nanotubes (MWCNTs) nanofluids is considered to be a highly efficient approach in the field of thermal engineering, specifically for the purpose of cooling electronic processors. The usage of a microchannel along with an electronic chip for liquid cooling of electronics presents a compelling substitute to the conventional bulky aluminium heat sinks. A minichannel heat sink employing MWCNTs nanofluid as a coolant is further enhanced in thermal and hydraulic performance. In order to analyze the performance of the minichannel heat sinks, a conjugate heat transfer model has been solved using the commercial software ANSYS-CFD. Theoretically, it showed that the presence of MWCNTs reduced thermal resistance and increased the thermal conductivity of liquid cooling system. The results reveal a maximum enhancement of in average heat transfer coefficient ( h ) for minichannel heat sink using MWCNTs as a coolant at volume 40%, 46%, and 52% concentrations of 0.25%, 0.5% and 0.75%. The performance evaluation shows that the overall performance of the minichannel heat sink using MWCNTs cooled minichannel heat sink at 0.75% volume concentration is roughly enhanced more as compared to water

Computational depth of anesthesia via multiple vital signs based on artificial neural networks

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.This research is financially supported by the Ministry of Science and Technology (MOST) of Taiwan. This research is also supported by the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is also sponsored by MOST (MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

A thermodynamic analysis of forced convection through porous media using pore scale modeling

The flow thorough porous media is analyzed from a thermodynamic perspective, with a particular focus on the entropy generation inside the porous media, using a pore scale modeling approach. A single representative elementary volume was utilized to reduce the CPU time. Periodic boundary conditions were employed for the vertical boundaries, by re-injecting the velocity and temperature profiles from the outlet to the inlet and iterating. The entropy generation was determined for both circular and square cross-sectional configurations, and the effects of different Reynolds numbers, assuming Darcy and Forchheimer regimes, were also taken into account. Three porosities were evaluated and discussed for each cross-sectional configuration, and streamlines, isothermal lines and the local entropy generation rate contours were determined and compared. The local entropy generation rate contours indicated that the highest entropy generation regions were close to the inlet for low Reynolds flows and near the central cylinder for high Reynolds flows. Increasing Reynolds number from 100 to 200 reveals disturbances in the dimensionless volume averaged entropy generation rate trend that may be due to a change in the fluid flow regime. According to Bejan number evaluation for both cross-section configurations, it is demonstrated that is mainly provoked by the heat transfer irreversibility. A performance evaluation criterion parameter was calculated for different case-studies. By this parameter, conditions for obtaining the least entropy generation and the highest Nusselt number could be achieved simultaneously. Indeed, this parameter utilizes both the first and the second laws of thermodynamics to present the best case-study. According to the performance evaluation criterion, it is indicated that the square cross-section configuration with o=0.64 exhibits better thermal performance for low Reynolds number flows. A comparison between the equal porosity cases for two different cross-sectional configurations indicated that the square cross-section demonstrated a higher performance evaluation criterion than the circular cross-section, for a variety of different Reynolds numbers

Performance evaluation considering iterations per phase and SA temperature in WMN-SA system

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we consider Simulated Annealing Algorithm (SA) temperature and Iteration per phase for the router node placement problem in WMNs. We want to find the optimal distribution of router nodes in order to provide the best network connectivity and provide the best coverage in a set of Normal distributed clients. From simulation results, we found how to optimize both the size of Giant Component and number of covered mesh clients. When the number of iterations per phase is big, the performance is better in WMN-SA System. From for SA temperature, when SA temperature is 0 and 1, the performance is almost same. When SA temperature is 2 and 3 or more, the performance decrease because there are many kick ups.Peer ReviewedPostprint (published version