id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-349396-a6zyioc1	Tsurumi, Amy	Multi-biomarker Prediction Models for Multiple Infection Episodes Following Blunt Trauma	2020-10-07		.txt	text/plain	4596	218	38	Applying machine learning algorithms to genome-wide transcriptome data from 128 adult blunt trauma patients' (42 MIIE cases and 85 non-cases) leukocytes collected ≤48 hours of injury and ≥3 days before any infection, we constructed a 15-transcript and a 26-transcript multi-biomarker panel model with the least absolute shrinkage and selection operator (LASSO) and Elastic Net, respectively, which accurately predicted MIIE (AUROC [95% CI]: 0.90 [0.84-0.96] and 0.92 [0.86-0.96]), and significantly outperformed clinical models. In a previous study among burn trauma patients, we developed a blood transcriptomic multi-biomarker panel for predicting multiple independent infection episodes (MIIE) outcome during the course of recovery (Yan et al., 2015) . Our study shows that employing novel prognostic models based on early blood transcriptome profiling following severe trauma is an effective method for identifying patients who are particularly at high risk for MIIE and thus, hypersusceptible to infections.	./cache/cord-349396-a6zyioc1.txt	./txt/cord-349396-a6zyioc1.txt