Angus B. Reed

TL;DR: In this paper, a systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020.

TL;DR: A range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.

TL;DR: In this paper, the authors developed and validated prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases, using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration.

TL;DR: In this article, the authors developed a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting to identify new patient-centric variables that can be incorporated into CVD risk assessments.

TL;DR: In this article, the authors developed a random forest classification model using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess risk of mortality with disease deterioration.