Assessing predictive accuracy for outcomes of ventilator-associated events in an international cohort: the EUVAE study.

Sergio Ramírez-EstradaLeonel LagunesYolanda Peña-LópezAmir Vahedian-AzimiSaad NseirKostoula ArvanitiAliye BastugIzarne TotorikaNefise OztoprakLilla BouadmaDespoina KoulentiJordi Rellonull null

Published in: Intensive care medicine (2018)

Respiratory infections (mainly VAT) were the most common complication. VAE algorithms only identified events with surrogates of severe oxygenation deterioration. As a consequence, IVAC definitions missed one fourth of the episodes of VAP and three fourths of the episodes of VAT. Identifying VAT (often missed by IVAC-plus criteria) is important, as VAP and VAT have different impacts on mortality.

Keyphrases

machine learning
cardiovascular events
deep learning
early onset
cardiovascular disease
blood flow
adipose tissue
weight loss
respiratory tract