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Last Posted: Jan 29, 2023
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Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021
X Cui et al, J Per Med, December 27, 2022

Research related to artificial intelligence in CCM has been increasing over the years. The USA published the most articles and had the top 10 active affiliations. The top ten active journals are bioinformatics journals and are in JCR Q1. Prediction, diagnosis, and treatment strategy exploration of sepsis, pneumonia, and acute kidney injury were the most focused topics. Electronic health records (EHRs) were the most widely used data and the “-omics” data should be integrated further.

A Culture of [Blood] Cultures Why hasn't rapid sequencing for serious infections and sepsis become standard of care?
E Topol, Ground Truths, December 17, 2022

Today when a patient presents with possible sepsis we draw multiple blood cultures and wait a few days before the results come back with a possible pathogen and readout for antibiotics that may be useful. The patient is bombarded with “empiric, broad spectrum antibiotics” to cover all the bacteria that are thought to be potentially causal, with implicit acknowledgement that viruses and other pathogens (fungi, parasites) won’t be covered by the antibiotic cocktail.

Sepsis as a Challenge for Personalized Medicine
R Zahorek et al, J Per Med, December 1, 2022

The remarkable progress in clinical medicine in the field of Sepsis can be attributed to basic research, genomics and proteomics, together with a better understanding of the immunopathology, biology and epidemiology of sepsis syndrome. The aim of this Special Issue is to provide research evidence and potential uses for personalized medicine in Sepsis, highlighting eight papers focused on research achievements in animal and human studies.

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.
Cano-Gamez Eddie et al. Science translational medicine 2022 11 (669) eabq4433

Predictors of severe infection could help physicians manage clinical care. Cano-Gamez et al. present an RNA-seq–based gene expression signature derived from patients with sepsis that generally captured patient prognosis with high sensitivity. Biologically, this signature corresponded to immune dysfunction. A machine learning framework based on the gene signature correctly stratified pediatric and adult patients with bacterial or viral sepsis, as well as patients with infection who did not meet sepsis criteria, including H1N1 influenza and COVID-19.

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.