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89 hot topic(s) found with the query "Sepsis"

Can Predictive AI Improve Early Detection of Sepsis and Other Conditions?
R Volker et al, JAMA, November 1, 2023 (Posted: Nov 01, 2023 1PM)

From the article: "AI and medicine intersect on a rapidly changing terrain where the possibilities are tremendous—tools that aid in the early detection of sepsis, for example, or help streamline transitions of care. AI is also ready for development in preventing pressure ulcers. In some areas of health care, AI may be ready for prime time, but in others, more research is needed to adapt these tools for real-world clinical use. "

Pregnant and Living with Sickle Cell Disease: A Push for Better Outcomes
NIH, September 2023 Brand (Posted: Sep 16, 2023 1PM)

From the website: " Experts say that medical advances in care and disease-modifying therapies have helped many people living with SCD survive well through their reproductive years. For parents-to-be, that means awareness is key. Individuals with SCD are at higher risk than the general population for preeclampsia, as Found discovered; but those with preeclampsia can go on to develop a condition called eclampsia, which can lead to seizures and even coma. People with SCD are also at higher risk for sepsis and blood clots. And there are risks for the fetus, such as lower-than-normal growth in the womb, preterm delivery, and stillbirth."

Comparison of pathogen detection consistency between metagenomic next-generation sequencing and blood culture in patients with suspected bloodstream infection.
Yuhua Zhou et al. Sci Rep 2023 6 (1) 9460 (Posted: Jun 11, 2023 8AM)

The application of metagenomic next-generation sequencing (mNGS) has gradually been carried out by clinical practitioner. However, few studies have compared it with blood cultures in patients suffering from suspected bloodstream infections. The purpose of this study was to compare the detection of pathogenic microorganisms by these two assays in patients with suspected bloodstream infection. We retrospectively studied patients with fever, chills, antibiotic use for more than 3 days, suspected bloodstream infection, and admission to the emergency department of Ruijin Hospital from January 2020 to June 2022.

Why is COVID life-threatening for some people? Genetics study offers clues Immune genes could play a part in the risk of needing intensive care when infected with SARS-CoV-2.
H Ledford, Nature, May 17, 2023 (Posted: May 17, 2023 4PM)

An analysis of DNA from more than 24,000 people who had COVID-19 and required treatment in intensive care has yielded more than a dozen new genetic links to the risk of developing extreme illness from the disease. The study has more than 2,000 authors, highlights the role of the immune system in fuelling the later stages of particularly severe COVID-19. The results could one day contribute to the development of therapies for COVID-19 — and potentially other diseases that cause acute respiratory distress or sepsis.

Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A mendelian randomisation study.
Fergus W Hamilton et al. PLoS medicine 2023 1 (1) e1004174 (Posted: Jan 31, 2023 8AM)

In a large, UK cohort (N = 485,825, including 11,643 with sepsis), genetic variation acting as a proxy (or natural experiment) for IL6R blockade was associated with a reduced odds of sepsis (odds ratio (OR) 0.80; 95% confidence interval (CI) 0.66 to 0.96) in MR analyses. Effects were consistent in secondary cohorts and when using differing definitions of sepsis, with effect sizes generally larger in more severe phenotypes.

Association between APOL1 risk variants and progression from infection to sepsis
L Jiang et al, MEDRXIV, January 28, 2023 (Posted: Jan 29, 2023 7AM)

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 (Posted: Dec 28, 2022 11AM)

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 (Posted: Dec 21, 2022 1PM)

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 (Posted: Dec 01, 2022 3PM)

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 (Posted: Nov 14, 2022 6AM)

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.

A Scoping Review of the Transcriptomic Perspective of Sepsis, a Move Towards Improved Precision Medicine?
A Rashid et al, MEDRXIV, November 2, 2022 (Posted: Nov 03, 2022 8AM)

Combining pathogen and host metagenomics for a better sepsis diagnostic.
Gant Vanya et al. Nature microbiology 2022 10 (11) 1713-1714 (Posted: Oct 29, 2022 11AM)

Sepsis is defined in the clinic as an assemblage of various failing physiology and laboratory markers of organ function triggered by infection. Efforts have been made to provide tighter definitions and criteria for sepsis to achieve better and more focused clinical care, research and epidemiology. A recent study shows that combining simultaneous host and pathogen metagenomic profiles in a cohort of hospitalized and critically ill patients allows for more accurate diagnosis of sepsis.

Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults.
Kalantar Katrina L et al. Nature microbiology 2022 10 (Posted: Oct 22, 2022 11AM)

We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. We suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.

A culture-free biphasic approach for sensitive and rapid detection of pathogens in dried whole-blood matrix.
Ganguli Anurup et al. Proceedings of the National Academy of Sciences of the United States of America 2022 9 (40) e2209607119 (Posted: Sep 27, 2022 7AM)

We report a culture-free “biphasic” approach to performing amplification reactions directly from whole blood. We dry the blood and create a physical nano scale fluidic network inside the dried blood matrix to allow for DNA amplification. We show single-molecule sensitivity for 3 bacteria and 1 fungal species from ~1 mL of blood in <2.5 h. We validated the assay with clinical samples and found complete agreement with the results of the clinical laboratory that used blood culture and PCR.

Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS
R Batra et al, MEDRXIV, August 13, 2022 (Posted: Aug 13, 2022 5PM)

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
R Adams et al, Nature Medicine, July 21, 2022 (Posted: Jul 22, 2022 8AM)

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing
KE Henry et al, Nature Medicine, July 21, 2022 (Posted: Jul 22, 2022 8AM)

We analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Patients with sepsis whose alert was confirmed by a provider within 3?h had a 1.85-h (95% CI 1.66–2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3?h after the alert or never addressed in the system.

Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system
KE Henry et al, NPJ Digital Medicine, July 21, 2022 (Posted: Jul 21, 2022 7AM)

Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians’ autonomy and support them across their entire workflow.

Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients
SC Brakenridge et al, JAMA Network Open, July 12, 2022 (Posted: Jul 12, 2022 0PM)

Can a whole-blood RNA transcriptomic metric (IMX) obtained in the first 12 hours after intensive care unit (ICU) admission accurately measure the presence of bacterial infection and risk for sepsis mortality? In this diagnostic and prognostic study including 200 patients with critical illness enrolled from a surgical ICU, the IMX transcriptomic metric was equivalent to or significantly better than the sequential organ failure assessment score and existing biomarkers (procalcitonin and interleukin 6 levels) for the diagnosis of acute infections and estimation of 30-day mortality.

Developing a shared sepsis data infrastructure: a systematic review and concept map to FHIR
B Brant et al, NPJ Digital Medicine April 4, 2022 (Posted: Apr 04, 2022 10AM)

The Sepsis on FHIR collaboration establishes a dynamic, federated, and interoperable system of sepsis data from 55 hospitals using 2 distinct inpatient electronic health record systems. Here we report on phase 1, a systematic review to identify clinical variables required to define sepsis and its subtypes to produce a concept mapping of elements onto Fast Healthcare Interoperability Resources (FHIR). Relevant papers described consensus sepsis definitions, provided criteria for sepsis, severe sepsis, septic shock, or detailed sepsis subtypes

Genetic Testing in Newborns Moves From Rare to Routine Application.
Pillers De-Ann M et al. JAMA pediatrics 2022 3 (Posted: Mar 22, 2022 0PM)

The development of the point-of-care test has only moved the goal post. The goal should not be limited to screening for the risk of developing hearing loss, but must be broadened to the identification of novel therapeutics to reduce harm. Importantly, diagnostic testing to identify more specifically those with neonatal sepsis is an important mandate. By having a targeted approach with reliable efficacy in sorting out who truly needs antibiotics, we will approach a safer world with fewer complications for newborns.

The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards
S Lauritsen et al, NPJ Digital Medicine, November 15, 2021 (Posted: Nov 15, 2021 6AM)

We introduce the basic concepts of framing, including prediction windows, observation windows, window shifts and event-triggers for a prediction that strongly affects the risk of clinician fatigue caused by false positives. Building on this, we apply four different framing structures to the same generic dataset, using a sepsis risk prediction model as an example, and evaluate how framing affects model performance and learning. Our results show that an apparently good model with strong evaluation results in both discrimination and calibration is not necessarily clinically usable.

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis
XQ Luo et al, Scientific Reports, October 12, 2021 (Posted: Oct 13, 2021 7AM)

The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing.

Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
SS Shashikumar et al, NPJ Digital Medicine, September 9, 2021 (Posted: Sep 10, 2021 7AM)

We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model.

External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.
Wong Andrew et al. JAMA internal medicine 2021 6 (Posted: Jun 25, 2021 6AM)

In this cohort study of 27?697 patients undergoing 38?455 hospitalizations, sepsis occurred in 7% of the hosptalizations. The Epic Sepsis Model predicted the onset of sepsis with an area under the curve of 0.63, which is substantially worse than the performance reported by its developer.

A hospital algorithm designed to predict a deadly condition misses most cases
N Wetsman, The Verge, June 22, 2021 (Posted: Jun 25, 2021 6AM)

SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care
S Gutmann et al, Nature Comms, June 7, 2021 (Posted: Jun 08, 2021 7AM)

We obtained blood samples (n?=?474) from hospitalized COVID-19 patients (n?=?123), non-COVID-19 ICU sepsis patients (n?=?25) and healthy controls (n?=?30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 RNAemia is comparable in performance to the best protein predictors.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.
Giacobbe Daniele Roberto et al. Frontiers in medicine 2021 8617486 (Posted: Mar 05, 2021 9AM)

We provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice.

Feasibility of Embedding a Scalable, Virtually Enabled Biorepository in the Electronic Health Record for Precision Medicine
KM De Merle et al, JAMA Network Open, February 21,2021 (Posted: Feb 23, 2021 6AM)

In this cohort study of 1027 patients with sepsis, a novel infrastructure, termed virtually enabled biorepository and electronic health record–embedded, scalable cohort for precision medicine (VESPRE) was developed. VESPRE appeared to demonstrate feasible digital screening, successful enrollment, biologic sampling, and lower costs compared with a traditional study design.

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values
Z Zhang et al, EBioMedicine, November 2020 (Posted: Nov 13, 2020 3PM)

Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study developed a parsimonious model for prognostic and predictive capability.

Individuals with Down syndrome hospitalized with COVID-19 have more severe disease
L Malle et al, Genetics in Medicine, October 18, 2020 (Posted: Oct 19, 2020 6AM)

In this retrospective study of 7246 patients hospitalized with COVID-19, we identified 12 patients with DS. Hospitalized individuals with DS are on average ten years younger than patients without DS. Patients with DS have more severe disease than controls, particularly an increased incidence of sepsis and mechanical ventilation.

Development, Validation, and Clinical Utility Assessment of a Prognostic Score for 1-Year Unplanned Rehospitalization or Death of Adult Sepsis Survivors
M Shankar-Hari et al, JAMA Network Open, September 14, 2020 (Posted: Sep 15, 2020 7AM)

In this cohort study of 94?748 patients rehospitalization or death in the first year after hospital discharge occurred for 51% of patients. The prognostic score uses 8 predictors: previous hospitalizations, age, socioeconomic status, preadmission dependence, number of comorbidities, admission type, site of infection, and admission blood hemoglobin level.

Sepsis Awareness Month: Why Sepsis Awareness Is More Important Than Ever
D Cardo, CDC Blog, September 11, 2020 Brand (Posted: Sep 13, 2020 10AM)

Cardiometabolic traits, sepsis and severe covid-19 with respiratory failure: a Mendelian randomization investigation
PJ Mark et al, MEDRXIV, June 20, 2020 (Posted: Jun 22, 2020 8AM)

This mendelian randomization study used the UK biobank to investigate the role of several traits with COVID-19 severity. The findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19. Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity,

New next-generation sequencing technique dramatically shortens diagnosis of sepsis
Eureka Alert, March 4, 2020 (Posted: Mar 06, 2020 8AM)

A new technique that uses real-time next-generation sequencing (NGS) to analyze tiny amounts of microbial cell-free DNA in the plasma of patients with sepsis, offering the possibility of accurate diagnosis of sepsis-causing agents within a few hours of drawing blood. Current diagnostic tests are neither fast nor specific.

An immune-cell signature of bacterial sepsis
M Reyes et al, Nature Medicine, February 17, 2020 (Posted: Feb 18, 2020 9AM)

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.
Fleuren Lucas M et al. Intensive care medicine 2020 Jan (Posted: Jan 29, 2020 7AM)

This systematic review shows that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although it presents alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results.

Development and Validation of a Predictive Model of the Risk of Pediatric Septic Shock Using Data Known at the Time of Hospital Arrival.
Scott Halden F et al. The Journal of pediatrics 2019 Nov (Posted: Nov 20, 2019 8AM)

This model estimated the risk of septic shock in children at hospital arrival earlier than existing models. It leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and has the potential to enhance clinical risk stratification in the critical moments before deterioration.

Clinical applications of artificial intelligence in sepsis: A narrative review.
Schinkel M et al. Computers in biology and medicine 2019 Oct 115103488 (Posted: Oct 23, 2019 9AM)

Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models.

A Genetic Approach to the Association Between PCSK9 and Sepsis.
Feng QiPing et al. JAMA network open 2019 Sep (9) e1911130 (Posted: Sep 12, 2019 7AM)

What is sepsis?
Know the Risks. Spot the Signs. Act Fast. CDC Information, 2019 Brand (Posted: Sep 03, 2019 10AM)

Enteric dysbiosis is associated with sepsis in patients
Z Liu et al, FASEB journal, August 28, 2019 (Posted: Aug 30, 2019 7AM)

Systematic analyses on clinical stool samples from patients with sepsis, including 16S rDNA sequencing, metabolomics, and metaproteomics analyses. The study found that the composition of gut microbiota was significantly disrupted in patients with sepsis compared with healthy individuals.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.
Giannini Heather M et al. Critical care medicine 2019 Aug (Posted: Aug 14, 2019 8AM)

Automating artificial intelligence for medical decision-making
R Matheson, MIT News, August 6, 2019 (Posted: Aug 07, 2019 7AM)

The field of predictive analytics holds increasing promise for helping clinicians diagnose and treat patients. Machine-learning models can be trained to find patterns in patient data to aid in sepsis care, design safer chemotherapy regimens, and predict a patient’s risk of having breast cancer or dying in the ICU, to name just a few examples.

Precision medicine in pediatric sepsis.
Atreya Mihir R et al. Current opinion in pediatrics 2019 06 (3) 322-327 (Posted: Jun 24, 2019 11AM)

New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.
Knaus William A et al. JAMA 2019 May (Posted: May 20, 2019 8AM)

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis.
Seymour Christopher W et al. JAMA 2019 May (Posted: May 20, 2019 8AM)

Precision medicine in pediatric sepsis.
Atreya Mihir R et al. Current opinion in pediatrics 2019 Mar (Posted: Mar 27, 2019 9AM)

Sepsis test could show results 'in minutes'
BBC News, February 19, 2019 (Posted: Feb 20, 2019 9AM)

Fatal Sepsis Associated with Bacterial Contamination of Platelets - Utah and California, August 2017.
Horth Roberta Z et al. MMWR. Morbidity and mortality weekly report 2018 Jun (25) 718-722 (Posted: Sep 01, 2018 6PM)

Maternal sepsis in the era of genomic medicine.
Kouskouti C et al. Archives of gynecology and obstetrics 2018 Jan (1) 49-60 (Posted: Feb 26, 2018 9AM)

A community approach to mortality prediction in sepsis via gene expression analysis.
Sweeney Timothy E et al. Nature communications 2018 Feb (1) 694 (Posted: Feb 19, 2018 6PM)

Metabolomics in Sepsis and Its Impact on Public Health.
Evangelatos Nikolaos et al. Public health genomics 2018 Jan (Posted: Feb 05, 2018 1PM)

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis.
Eckerle Michelle et al. Pharmacotherapy 2017 Sep (9) 1023-1032 (Posted: Oct 05, 2017 3PM)

Counting Sepsis, an Imprecise but Improving Science
KE Rudd et al, JAMA Sep 13, 2017 (Posted: Sep 13, 2017 1PM)

Get Ahead of Sepsis
Brand (Posted: Sep 01, 2017 5PM)

Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?
Langley Raymond J et al. Molecular diagnosis & therapy 2017 Jun (Posted: Sep 01, 2017 5PM)

A path to precision in the ICU.
Maslove David M et al. Critical care (London, England) 2017 04 (1) 79 (Posted: Sep 01, 2017 5PM)

Early Prediction of Sepsis Incidence in Critically Ill Patients Using Specific Genetic Polymorphisms.
David Vlad Laurentiu et al. Biochemical genetics 2017 Jun (3) 193-203 (Posted: Sep 01, 2017 5PM)

Sepsis is Life Changing for All Involved
Brand (Posted: Oct 03, 2016 10AM)

Genomics and pharmacogenomics of sepsis: so close and yet so far
JA Russell, Critical Care, Sept 2016 (Posted: Oct 02, 2016 9PM)

Genetics and the Evaluation of the Febrile Child
HB Bauchner, JAMA, August 23, 2016 (Posted: Aug 24, 2016 10AM)

Making Health Care Safer- Think sepsis. Time matters.
Brand (Posted: Aug 23, 2016 1PM)

Beyond Blood Culture and Gram Stain Analysis: A Review of Molecular Techniques for the Early Detection of Bacteremia in Surgical Patients.
Scerbo Michelle H et al. Surgical infections 2016 Jun (3) 294-302 (Posted: Jul 04, 2016 7AM)

Next-generation sequencing diagnostics of bacteremia in septic patients
S Grumaz et al, Genome Medicine, July 2016 (Posted: Jul 04, 2016 7AM)

New Definitions for Sepsis and Septic Shock Continuing Evolution but With Much Still to Be Done
E Abraham. JAMA,February 23, 2016 (Posted: Feb 23, 2016 9AM)

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
M SInger et al. JAMA, February 23, 2016 (Posted: Feb 23, 2016 9AM)

Developing a New Definition and Assessing New Clinical Criteria for Septic Shock For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Manu Shankar-Hari et al. JAMA, February 23, 2016 (Posted: Feb 22, 2016 3PM)

Personalized Medicine for Sepsis.
Pinheiro da Silva Fabiano et al. The American journal of the medical sciences 2015 Nov (5) 409-13 (Posted: Feb 10, 2016 10AM)

Elucidating the role of genomics in neonatal sepsis.
Srinivasan Lakshmi et al. Seminars in perinatology 2015 Dec (8) 611-6 (Posted: Feb 10, 2016 10AM)

Sepsis is the body’s overwhelming and life-threatening response to infection which can lead to tissue damage, organ failure, and death
Brand (Posted: Feb 10, 2016 10AM)

Common variants of NFE2L2 gene predisposes to acute respiratory distress syndrome in patients with severe sepsis.
Acosta-Herrera Marialbert et al. Crit Care 2015 256 (Posted: Sep 04, 2015 1PM)

Late-Onset Bloodstream Infection and Perturbed Maturation of the Gastrointestinal Microbiota in Premature Infants.
Shaw Alexander G et al. PLoS ONE 2015 (7) e0132923 (Posted: Sep 04, 2015 1PM)

The Need for EMS to be on the Lookout for Pediatric Sepsis
Brand (Posted: Sep 04, 2015 1PM)

The Power of Families in the Battle against Sepsis
Brand (Posted: Sep 04, 2015 1PM)

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set
TE Sweeney et al, Sci Trans Med, May 13, 2015 (Posted: May 14, 2015 10AM)

Association between interleukin-10 polymorphisms and sepsis: a meta-analysis.
Pan W et al. Epidemiol. Infect. 2015 Jan (2) 366-75 (Posted: May 14, 2015 8AM)

Finding a needle in the haystack: leveraging bioinformatics to identify a functional genetic risk factor for sepsis death.
Meyer Nuala J et al. Crit. Care Med. 2015 Jan (1) 242-3 (Posted: May 14, 2015 8AM)

Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study.
Rautanen Anna et al. Lancet Respir Med 2015 Jan (1) 53-60 (Posted: May 14, 2015 8AM)

An integrated transcriptome and expressed variant analysis of sepsis survival and death.
Tsalik Ephraim L et al. Genome Med 2014 (11) 111 (Posted: May 14, 2015 8AM)

Clinical associations of host genetic variations in the genes of cytokines in critically ill patients.
Belopolskaya O B et al. Clin. Exp. Immunol. 2015 Jan 23. (Posted: May 14, 2015 8AM)

Impact of virulence genes on sepsis severity and survival in Escherichia coli bacteremia.
Mora-Rillo Marta et al. Virulence 2015 (1) 93-100 (Posted: May 14, 2015 7AM)

Association between IL-6-174G/C polymorphism and the risk of sepsis and mortality: a systematic review and meta-analysis.
Gao Jun-wei et al. PLoS ONE 2015 (3) e0118843 (Posted: May 14, 2015 7AM)

An ADAM10 promoter polymorphism is a functional variant in severe sepsis patients and confers susceptibility to the development of sepsis.
Cui Lili et al. Crit Care 2015 (1) 73 (Posted: May 14, 2015 7AM)

Functional polymorphisms in CD86 gene are associated with susceptibility to pneumonia-induced sepsis.
Wang Chenfei et al. APMIS 2015 May (5) 433-8 (Posted: May 14, 2015 7AM)

Quicker sepsis diagnosis may be a step closer
A Yeager, Science News, May 14, 2015 (Posted: May 14, 2015 7AM)

Genomic analysis and clinical importance of Escherichia coli isolate from patients with sepsis.
Chakraborty Arindam et al. Indian J Pathol Microbiol 2015 Jan-Mar (1) 22-6 (Posted: Apr 24, 2015 10AM)

Prognostic markers of pediatric meningococcal sepsis.
Briassoulis George et al. Expert Rev Anti Infect Ther 2014 Sep (9) 1017-20 (Posted: Apr 21, 2015 3PM)

Disseminated Intravascular Coagulation
From NHLBI health topic site Brand (Posted: Jan 01, 2014 0AM)

Disseminated intravascular coagulation (ko-ag-u-LA-shun), or DIC, is a condition in which blood clots form throughout the body's small blood vessels. These blood clots can reduce or block blood flow through the blood vessels, which can damage the body's organs. In DIC, the increased clotting uses up platelets (PLATE-lets) and clotting factors in the blood. Platelets are blood cell fragments that stick together to seal small cuts and breaks on blood vessel walls and stop bleeding. Clotting factors are proteins needed for normal blood clotting. With fewer platelets and clotting factors in the blood, serious bleeding can occur. DIC can cause internal and external bleeding. Internal bleeding occurs inside the body. External bleeding occurs underneath or from the skin or mucosa. (The mucosa is the tissue that lines some organs and body cavities, such as your nose and mouth.) DIC can cause life-threatening bleeding. Overview To understand DIC, it helps to understand the body's normal blood clotting process. Your body has a system to control bleeding. When small cuts or breaks occur on blood vessel walls, your body activates clotting factors. These clotting factors, such as thrombin and fibrin, work with platelets to form blood clots. Blood clots seal the small cuts or breaks on the blood vessel walls. After bleeding stops and the vessels heal, your body breaks down and removes the clots. Some diseases and conditions can cause clotting factors to become overactive, leading to DIC. These diseases and conditions include: ?Sepsis (an infection in the bloodstream) ?Surgery and trauma ?Cancer ?Serious complications of pregnancy and childbirth Examples of less common causes of DIC are bites from poisonous snakes (such as rattlesnakes and other vipers), frostbite, and burns. The two types of DIC are acute and chronic. Acute DIC develops quickly (over hours or days) and must be treated right away. The condition begins with excessive blood clotting in the small blood vessels and quickly leads to serious bleeding. Chronic DIC develops slowly (over weeks or months). It lasts longer and usually isn't recognized as quickly as acute DIC. Chronic DIC causes excessive blood clotting, but it usually doesn't lead to bleeding. Cancer is the most common cause of chronic DIC. Treatment for DIC involves treating the clotting and bleeding problems and the underlying cause of the condition. People who have acute DIC may need blood transfusions, medicines, and other life-saving measures. People who have chronic DIC may need medicines to help prevent blood clots from forming in their small blood vessels. Outlook The outlook for DIC depends on its severity and underlying cause. Acute DIC can damage the body's organs and even cause death if it's not treated right away. Chronic DIC also can damage the body's organs. Researchers are looking for ways to prevent DIC or diagnose it early. They're also studying the use of various clotting proteins and medicines to treat the condition. Other Names ?Consumption coagulopathy ?Defibrination syndrome

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