Validation of submaximal prediction equations for the 1 repetition maximum bench press test on a group of collegiate football players

The purpose of the study was to determine the accuracy of 11 prediction equations in estimating the 1 repetition maximum (1RM) bench press from repetitions completed by collegiate football players (N = 69) using 225 lb. The demographic variables race, age, height, weight, fat-free weight, and percent body fat were measured to determine whether these variables increased the accuracy of the prediction equations; race was the most frequently selected variable in the regression analyses. The validity of the prediction equations was dependent upon the number of repetitions performed, i.e., validity was higher when fewer repetitions were completed. Explained variability of 1RM was slightly higher for all 11 equations when demographic variables were included. A new prediction equation was also developed using the number of repetitions performed and the demographic variables height and fat-free weight

Validation of Submaximal Prediction Equations for the 1 Repetition Maximum Bench Press Test on a Group of Collegiate Football Players

The purpose of the study was to determine the accuracy of 11 prediction equations in estimating the 1 repetition maximum (1RM) bench press from repetitions completed by collegiate football players (N = 69) using 225 lb. The demographic variables race, age, height, weight, fat-free weight, and percent body fat were measured to determine whether these variables increased the accuracy of the prediction equations; race was the most frequently selected variable in the regression analyses. The validity of the prediction equations was dependent upon the number of repetitions performed, i.e., validity was higher when fewer repetitions were completed. Explained variability of 1RM was slightly higher for all 11 equations when demographic variables were included. A new prediction equation was also developed using the number of repetitions performed and the demographic variables height and fat-free weight

SCAI/HRS Expert Consensus Statement on Transcatheter Left Atrial Appendage Closure

Exclusion of the left atrial appendage to reduce thromboembolic risk related to atrial fibrillation was first performed surgically in 1949. Over the past 2 decades, the field of transcatheter endovascular left atrial appendage closure (LAAC) has rapidly expanded, with a myriad of devices approved or in clinical development. The number of LAAC procedures performed in the United States and worldwide has increased exponentially since the Food and Drug Administration approval of the WATCHMAN (Boston Scientific) device in 2015. The Society for Cardiovascular Angiography & Interventions (SCAI) has previously published statements in 2015 and 2016 providing societal overview of the technology and institutional and operator requirements for LAAC. Since then, results from several important clinical trials and registries have been published, technical expertise and clinical practice have matured over time, and the device and imaging technologies have evolved. Therefore, SCAI prioritized the development of an updated consensus statement to provide recommendations on contemporary, evidence-based best practices for transcatheter LAAC focusing on endovascular devices

A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry

BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the TERAVOLT registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure and a tree-based model to screen and optimize a broad panel of demographics, clinical COVID-19 and cancer characteristics. RESULTS: As of April 15, 2021, 1491 consecutive evaluable patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened approximately 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection then identified seven major determinants of death ECOG-PS (OR 2.47 1.87-3.26), neutrophil count (OR 2.46 1.76-3.44), serum procalcitonin (OR 2.37 1.64-3.43), development of pneumonia (OR 1.95 1.48-2.58), c-reactive protein (CRP) (OR 1.90 1.43-2.51), tumor stage at COVID-19 diagnosis (OR 1.97 1.46-2.66) and age (OR 1.71 1.29-2.26). The ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 0.75 - 0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90% and the tree-based model recognized ECOG-PS, neutrophil count and CRP as the major determinants of prognosis. CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated the strongest association with poor outcome from COVID-19. With our analysis we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19