Adam L. Booth

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: A machine learning model is developed using five serum chemistry laboratory parameters from 398 patients for the prediction of death up to 48 h prior to patient expiration, and the resulting support vector machine model achieved 91% sensitivity and 91% specificity for predicting patient expiration status on held-out testing data.

TL;DR: This guide is intended to be a resource for pathologists and pathologists-in-training who wish to leverage technology to continue collaboration, teaching, and education in this era without a precedent or guidebook.