Dataset Information

A Common Genetic Variant in TLR1 Enhances Human Neutrophil Priming and Impacts Length of Intensive Care Stay in Pediatric Sepsis.

ABSTRACT: Polymorphonuclear leukocytes (PMN) achieve an intermediate or primed state of activation following stimulation with certain agonists. Primed PMN have enhanced responsiveness to subsequent stimuli, which can be beneficial in eliminating microbes but may cause host tissue damage in certain disease contexts, including sepsis. As PMN priming by TLR4 agonists is well described, we hypothesized that ligation of TLR2/1 or TLR2/6 would prime PMN. Surprisingly, PMN from only a subset of donors were primed in response to the TLR2/1 agonist, Pam3CSK4, although PMN from all donors were primed by the TLR2/6 agonist, FSL-1. Priming responses included generation of intracellular and extracellular reactive oxygen species, MAPK phosphorylation, integrin activation, secondary granule exocytosis, and cytokine secretion. Genotyping studies revealed that PMN responsiveness to Pam3CSK4 was enhanced by a common single-nucleotide polymorphism (SNP) in TLR1 (rs5743618). Notably, PMN from donors with the SNP had higher surface levels of TLR1 and were demonstrated to have enhanced association of TLR1 with the endoplasmic reticulum chaperone gp96. We analyzed TLR1 genotypes in a pediatric sepsis database and found that patients with sepsis or septic shock who had a positive blood culture and were homozygous for the SNP associated with neutrophil priming had prolonged pediatric intensive care unit length of stay. We conclude that this TLR1 SNP leads to excessive PMN priming in response to cell stimulation. Based on our finding that septic children with this SNP had longer pediatric intensive care unit stays, we speculate that this SNP results in hyperinflammation in diseases such as sepsis.

SUBMITTER: Whitmore LC

PROVIDER: S-EPMC4724530 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Common Genetic Variant in TLR1 Enhances Human Neutrophil Priming and Impacts Length of Intensive Care Stay in Pediatric Sepsis.

Whitmore Laura C LC Hook Jessica S JS Philiph Amanda R AR Hilkin Brieanna M BM Bing Xinyu X Ahn Chul C Wong Hector R HR Ferguson Polly J PJ Moreland Jessica G JG

Journal of immunology (Baltimore, Md. : 1950) 20160104 3

Polymorphonuclear leukocytes (PMN) achieve an intermediate or primed state of activation following stimulation with certain agonists. Primed PMN have enhanced responsiveness to subsequent stimuli, which can be beneficial in eliminating microbes but may cause host tissue damage in certain disease contexts, including sepsis. As PMN priming by TLR4 agonists is well described, we hypothesized that ligation of TLR2/1 or TLR2/6 would prime PMN. Surprisingly, PMN from only a subset of donors were prime ...[more]

PMID: 26729809

Similar Datasets

Project description:IntroductionEffective planning of elective surgical procedures requiring postoperative intensive care is important in preventing cancellations and empty intensive care unit (ICU) beds. To improve planning, we constructed, validated and tested three models designed to predict length of stay (LOS) in the ICU in individual patients.MethodsRetrospective data were collected from 518 consecutive patients who underwent oesophagectomy with reconstruction for carcinoma between January 1997 and April 2005. Three multivariable linear regression models for LOS, namely preoperative, postoperative and intra-ICU, were constructed using these data. Internal validation was assessed using bootstrap sampling in order to obtain validated estimates of the explained variance (r2). To determine the potential gain of the best performing model in day-to-day clinical practice, prospective data from a second cohort of 65 consecutive patients undergoing oesophagectomy between May 2005 and April 2006 were used in the model, and the predictive performance of the model was compared with prediction based on mean LOS.ResultsThe intra-ICU model had an r2 of 45% after internal validation. Important prognostic variables for LOS included greater patient age, comorbidity, type of surgical approach, intraoperative respiratory minute volume and complications occurring within 72 hours in the ICU. The potential gain of the best model in day-to-day clinical practice was determined relative to mean LOS. Use of the model reduced the deficit number (underestimation) of ICU days by 65 and increased the excess number (overestimation) of ICU days by 23 for the cohort of 65 patients. A conservative analysis conducted in the second, prospective cohort of patients revealed that 7% more oesophagectomies could have been accommodated, and 15% of cancelled procedures could have been prevented.ConclusionPatient characteristics can be used to create models that will help in predicting LOS in the ICU. This will result in more efficient use of ICU beds and fewer cancellations.

Project description:To estimate the prevalence of chronic conditions among children admitted to U.S. pediatric intensive care units and to assess whether patients with complex chronic conditions experience pediatric intensive care unit mortality and prolonged length of stay risk beyond that predicted by commonly used severity-of-illness risk-adjustment models.Retrospective cohort analysis of 52,791 pediatric admissions to 54 U.S. pediatric intensive care units that participated in the Virtual Pediatric Intensive Care Unit Systems database in 2008.Hierarchical logistic regression models, clustered by pediatric intensive care unit site, for pediatric intensive care unit mortality and length of stay >15 days. Standardized mortality ratios adjusted for severity-of-illness score alone and with complex chronic conditions.Fifty-three percent of pediatric intensive care unit admissions had complex chronic conditions, and 18.5% had noncomplex chronic conditions. The prevalence of these conditions and their organ system subcategories varied considerably across sites. The majority of complex chronic condition subcategories were associated with significantly greater odds of pediatric intensive care unit mortality (odds ratios 1.25-2.9, all p values < .02) compared to having a noncomplex chronic condition or no chronic condition, after controlling for age, gender, trauma, and severity-of-illness. Only respiratory, gastrointestinal, and rheumatologic/orthopedic/psychiatric complex chronic conditions were not associated with increased odds of pediatric intensive care unit mortality. All subcategories were significantly associated with prolonged length of stay. All noncomplex chronic condition subcategories were either not associated or were negatively associated with pediatric intensive care unit mortality, and most were not associated with prolonged length of stay, compared to having no chronic conditions. Among this group of pediatric intensive care units, adding complex chronic conditions to risk-adjustment models led to greater model accuracy but did not substantially change unit-level standardized mortality ratios.Children with complex chronic conditions were at greater risk for pediatric intensive care unit mortality and prolonged length of stay than those with no chronic conditions, but the magnitude of risk varied across subcategories. Inclusion of complex chronic conditions into models of pediatric intensive care unit mortality improved model accuracy but had little impact on standardized mortality ratios.

Project description:BackgroundHealthcare management faces complex challenges in allocating hospital resources, and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to map approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS-P) and patients' populations and environments used to develop the models.MethodsUsing the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology, we performed a scoping review that identified 28 studies and analyzed them. The search was conducted on four databases (Science Direct, Scopus, Web of Science, and Medline). The identification of relevant studies was structured around three axes related to the research questions: (i) forecast models, (ii) hospital length-of-stay, and (iii) pediatric patients. Two authors carried out all stages to ensure the reliability of the review process. Articles that passed the initial screening had their data charted on a spreadsheet. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation.ResultsForecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and primarily used in emergency departments to model patients in specific situations.ConclusionsThe studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to LOS-P forecasting methods and a future research agenda.

Dataset Information

A Common Genetic Variant in TLR1 Enhances Human Neutrophil Priming and Impacts Length of Intensive Care Stay in Pediatric Sepsis.

Publications

A Common Genetic Variant in TLR1 Enhances Human Neutrophil Priming and Impacts Length of Intensive Care Stay in Pediatric Sepsis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure