0000000000577983
AUTHOR
I. Gracia
SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
Abstract Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in su…
Early outcomes and complications following cardiac surgery in patients testing positive for coronavirus disease 2019: An international cohort study
The outbreak of severe acute respiratory syndromecoronavirus-2, the cause of coronavirus disease 2019 (COVID-19) in December 2019 represented a global emergency accounting for more than 2.5 million deaths worldwide.1 It has had an unprecedented influence on cardiac surgery internationally, resulting in cautious delivery of surgery and restructuring of services.2 Understanding the influence of COVID-19 on patients after cardiac surgery is based on assumptions from other surgical specialties and single-center studies. The COVIDSurg Collaborative conducted a multicenter cohort study, including 1128 patients, across 235 hospitals, from 24 countries demonstrating perioperative COVID-19 infection…
Estimating feature discriminant power in decision tree classifiers
Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.