International consensus on lung function testing during COVID-19 pandemic and beyond

COVID-19 has negatively affected the delivery of respiratory diagnostic services across the world due to the potential risk of disease transmission during lung function testing. Community prevalence, reoccurrence of COVID-19 surges, and the emergence of different variants of the SARS-CoV-2 virus have impeded attempts to restore services. Finding consensus on how to deliver safe lung function services for both patients attending and for staff performing the tests are of paramount importance.This international statement presents the consensus opinion of 23 experts in the field of lung function and respiratory physiology balanced with evidence from the reviewed literature. It describes a robust roadmap for restoration and continuity of lung function testing services during the COVID-19 pandemic and beyond.Important strategies presented in this consensus statement relate to the patient journey when attending for lung function tests. We discuss appointment preparation, operational and environmental issues, testing room requirements including mitigation strategies for transmission risk, requirement for improved ventilation, maintaining physical distance, and use of personal protection equipment. We also provide consensus opinion on precautions relating to specific tests, filters, management of special patient groups, and alternative options to testing in hospitals.The pandemic has highlighted how vulnerable lung function services are and forces us to re-think how long term mitigation strategies can protect our services during this and any possible future pandemic. This statement aspires to address the safety concerns that exist and provide strategies to make lung function tests and the testing environment safer when tests are required.

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care. Lay summary Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion. Graphical abstract

Antithrombotic therapy in the elderly and senile age: the consensus opinion of experts of the Russian Association of Gerontologists and Geriatricians and the National Society of Preventive Cardiology

В данном документе обсуждаются особенности АТТ у лиц пожилого и старческого возраста в различных клинических ситуациях.