Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.
Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.
Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.
OBJECTIVES: To assess the effects of workplace ergonomic design or training interventions, or both, for the prevention of work-related upper limb and neck MSDs in adults.
SEARCH METHODS: We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, AMED, Web of Science (Science Citation Index), SPORTDiscus, Cochrane Occupational Safety and Health Review Group Database and Cochrane Bone, Joint and Muscle Trauma Group Specialised Register to July 2010, and Physiotherapy Evidence Database, US Centers for Disease Control and Prevention, the National Institute for Occupational Safety and Health database, and International Occupational Safety and Health Information Centre database to November 2010.
SELECTION CRITERIA: We included randomised controlled trials (RCTs) of ergonomic workplace interventions for preventing work-related upper limb and neck MSDs. We included only studies with a baseline prevalence of MSDs of the upper limb or neck, or both, of less than 25%.
DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed risk of bias. We included studies with relevant data that we judged to be sufficiently homogeneous regarding the intervention and outcome in the meta-analysis. We assessed the overall quality of the evidence for each comparison using the GRADE approach.
MAIN RESULTS: We included 13 RCTs (2397 workers). Eleven studies were conducted in an office environment and two in a healthcare setting. We judged one study to have a low risk of bias. The 13 studies evaluated effectiveness of ergonomic equipment, supplementary breaks or reduced work hours, ergonomic training, a combination of ergonomic training and equipment, and patient lifting interventions for preventing work-related MSDs of the upper limb and neck in adults.Overall, there was moderate-quality evidence that arm support with alternative mouse reduced the incidence of neck/shoulder disorders (risk ratio (RR) 0.52; 95% confidence interval (CI) 0.27 to 0.99) but not the incidence of right upper limb MSDs (RR 0.73; 95% CI 0.32 to 1.66); and low-quality evidence that this intervention reduced neck/shoulder discomfort (standardised mean difference (SMD) -0.41; 95% CI -0.69 to -0.12) and right upper limb discomfort (SMD -0.34; 95% CI -0.63 to -0.06).There was also moderate-quality evidence that the incidence of neck/shoulder and right upper limb disorders were not reduced when comparing alternative mouse and conventional mouse (neck/shoulder RR 0.62; 95% CI 0.19 to 2.00; right upper limb RR 0.91; 95% CI 0.48 to 1.72), arm support and no arm support with conventional mouse (neck/shoulder RR 0.67; 95% CI 0.36 to 1.24; right upper limb RR 1.09; 95% CI 0.51 to 2.29), and alternative mouse with arm support and conventional mouse with arm support (neck/shoulder RR 0.58; 95% CI 0.30 to 1.12; right upper limb RR 0.92; 95% CI 0.36 to 2.36).There was low-quality evidence that using an alternative mouse with arm support compared to conventional mouse with arm support reduced neck/shoulder discomfort (SMD -0.39; 95% CI -0.67 to -0.10). There was low- to very low-quality evidence that other interventions were not effective in reducing work-related upper limb and neck MSDs in adults.
AUTHORS' CONCLUSIONS: We found moderate-quality evidence to suggest that the use of arm support with alternative mouse may reduce the incidence of neck/shoulder MSDs, but not right upper limb MSDs. Moreover, we found moderate-quality evidence to suggest that the incidence of neck/shoulder and right upper limb MSDs is not reduced when comparing alternative and conventional mouse with and without arm support. However, given there were multiple comparisons made involving a number of interventions and outcomes, high-quality evidence is needed to determine the effectiveness of these interventions clearly. While we found very-low- to low-quality evidence to suggest that other ergonomic interventions do not prevent work-related MSDs of the upper limb and neck, this was limited by the paucity and heterogeneity of available studies. This review highlights the need for high-quality RCTs examining the prevention of MSDs of the upper limb and neck.
METHODOLOGY: This prospective study compared the performance of nine commonly used PEs, including the Harris-Benedict (H-B1919), Penn State, and TAH equations, with ML models (XGBoost, Random Forest Regressor [RFR], Support Vector Regression), and DL models (Convolutional Neural Networks [CNN]) in estimating REE in critically ill patients. A dataset of 300 IC measurements from an intensive care unit (ICU) was used, with REE measured by both IC and PEs. The ML/DL models were trained using a combination of static (i.e., age, height, body weight) and dynamic (i.e., minute ventilation, body temperature) variables. A five-fold cross validation was performed to assess the model prediction performance using the root mean square error (RMSE) metric.
RESULTS: Of the PEs analysed, H-B1919 yielded the lowest RMSE at 362 calories. However, the XGBoost and RFR models significantly outperformed all PEs, achieving RMSE values of 199 and 200 calories, respectively. The CNN model demonstrated the poorest performance among ML models, with an RMSE of 250 calories. The inclusion of additional categorical variables such as body mass index (BMI) and body temperature classes slightly reduced RMSE across ML and DL models. Despite data augmentation and imputation techniques, no significant improvements in model performance were observed.
CONCLUSION: ML models, particularly XGBoost and RFR, provide more accurate REE estimations than traditional PEs, highlighting their potential to better capture the complex, non-linear relationships between physiological variables and REE. These models offer a promising alternative for guiding nutritional therapy in clinical settings, though further validation on independent datasets and across diverse patient populations is warranted.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.