Two-step interpretable modeling of ICU-AIs.

We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining the interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution time-dependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.

O-serotype distribution of Escherichia coli bloodstream infection isolates in critically ill patients in The Netherlands.

OBJECTIVES: Invasive infections by extra-intestinal pathogenic Escherichia coli (ExPEC) strains are increasing. We determined O-serogroups of E. coli isolates from ICU patients having bloodstream infections (BSI) and the potential coverage of a 10-valent O-polysaccharide conjugate vaccine currently in development for the prevention of invasive ExPEC disease. METHODS: We studied E. coli BSI among patients admitted to a tertiary ICU in the Netherlands between April 2011 and November 2016. O-serogroups were determined in vitro by agglutination and whole genome sequencing. RESULTS: Among 714 ICU patients having BSI, 70 (10%) had an E. coli BSI. Among 68 (97%) isolates serogrouped, the most common serogroups were O25 (n = 11; 16%), O8 (n = 5; 7%), O2 (n = 4; 6%), O6 (n = 4; 6%), and O15 (n = 4; 6%). The theoretical coverage of a 10-valent ExPEC vaccine was 54% (n = 37). CONCLUSIONS: A multi-valent ExPEC O-polysaccharide conjugate vaccine in development could potentially aid in the prevention of E. coli BSI in Dutch ICU patients.

Profile of the SeptiCyte™ LAB gene expression assay to diagnose infection in critically ill patients.

Sepsis is a severe and frequently occurring clinical syndrome, caused by the inflammatory response to infections. Recent studies on the human transcriptome during sepsis have yielded several gene-expression assays that might assist physicians during clinical assessment of patients suspected of sepsis. SeptiCyte™ LAB (Immunexpress, Seattle, WA) is the first gene expression assay that was cleared by the FDA in the United States to distinguish infectious from non-infectious causes of systemic inflammation in critically ill patients. The test consists of the simultaneous amplification of four RNA transcripts (CEACAM4, LAMP1, PLAC8, and PLA2G7) in whole blood using a quantitative real-time PCR reaction. This review provides an overview of the challenges in the diagnosis of sepsis, the development of gene expression signatures, and a detailed description of available clinical performance studies evaluating SeptiCyte™ LAB.