id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-353200-5csewb1k	Jehi, Lara	Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19	2020-08-11		.txt	text/plain	4344	226	40	OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. Hospitalization risk prediction and outcomes in COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0237419 August 11, 2020 2 / 15 ethical restrictions by the Cleveland clinic regulatory bodies including the institutional review Board and legal counsel. We also develop and validate a statistical model that can assist with individualized prediction of hospitalization risk for a patient with COVID-19.	./cache/cord-353200-5csewb1k.txt	./txt/cord-353200-5csewb1k.txt