Dataset Information

Robust health-score based survival prediction for a neonatal mouse model of polymicrobial sepsis.

ABSTRACT: Infectious disease and sepsis represent a serious problem for all, but especially in early life. Much of the increase in morbidity and mortality due to infection in early life is presumed to relate to fundamental differences between neonatal and adult immunity. Mechanistic insight into the way newborns' immune systems handle infectious threats is lacking; as a result, there has only been limited success in providing effective immunomodulatory interventions to reduce infectious mortality. Given the complexity of the host-pathogen interactions, neonatal mouse models can offer potential avenues providing valuable data. However, the small size of neonatal mice hampers the ability to collect biological samples without sacrificing the animals. Further, the lack of a standardized metric to quantify newborn mouse health increases reliance on correlative biomarkers without a known relationship to 'clinical' outcome. To address this bottleneck, we developed a system that allows assessment of neonatal mouse health in a readily standardized and quantifiable manner. The resulting health scores require no special equipment or sample collection and can be assigned in less than 20 seconds. Importantly, the health scores are highly predictive of survival. A classifier built on our health score revealed a positive relationship between reduced bacterial load and survival, demonstrating how this scoring system can be used to bridge the gap between assumed relevance of biomarkers and the clinical outcome of interest. Adoption of this scoring system will not only provide a robust metric to assess health of newborn mice but will also allow for objective, prospective studies of infectious disease and possible interventions in early life.

SUBMITTER: Brook B

PROVIDER: S-EPMC6590826 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Robust health-score based survival prediction for a neonatal mouse model of polymicrobial sepsis.

Brook Byron B Harbeson Danny D Amenyogbe Nelly N Ben-Othman Rym R Kollmann Tobias R TR Aniba Radhouane R

PloS one 20190624 6

Infectious disease and sepsis represent a serious problem for all, but especially in early life. Much of the increase in morbidity and mortality due to infection in early life is presumed to relate to fundamental differences between neonatal and adult immunity. Mechanistic insight into the way newborns' immune systems handle infectious threats is lacking; as a result, there has only been limited success in providing effective immunomodulatory interventions to reduce infectious mortality. Given t ...[more]

PMID: 31233529

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Project description:BackgroundAn imbalance between overwhelming inflammation and lymphocyte apoptosis is the main cause of high mortality in patients with sepsis. Baicalin, the main active ingredient of the Scutellaria root, exerts anti-inflammatory, anti-apoptotic, and even antibacterial properties in inflammatory and infectious diseases. However, the therapeutic effect of baicalin on polymicrobial sepsis remains unknown.Methodology/principal findingsPolymicrobial sepsis was induced by cecal ligation and puncture (CLP) in C57BL/6 mice. Mice were infused with baicalin intraperitoneally at 1 h, 6 h and 12 h after CLP. Survival rates were assessed over the subsequent 8 days. Bacterial burdens in blood and peritoneal cavity were calculated to assess the bacterial clearance. Neutrophil count in peritoneal lavage fluid was also calculated. Injuries to the lung and liver were detected by hematoxylin and eosin staining. Levels of cytokines, including tumor necrosis factor (TNF)-alpha, interleukin (IL)-6, IL-10 and IL-17, in blood and peritoneum were measured by enzyme-linked immunosorbent assay. Adaptive immune function was assessed by apoptosis of lymphocytes in the thymus and counts of different cell types in the spleen. Baicalin significantly enhanced bacterial clearance and improved survival of septic mice. The number of neutrophils in peritoneal lavage fluid was reduced by baicalin. Less neutrophil infiltration of the lung and liver in baicalin-treated mice was associated with attenuated injuries to these organs. Baicalin significantly reduced the levels of proinflammatory cytokines but increased the level of anti-inflammatory cytokine in blood and peritoneum. Apoptosis of CD3(+) T cell was inhibited in the thymus. The numbers of CD4(+), CD8(+) T lymphocytes and dendritic cells (DCs) were higher, while the number of CD4(+)CD25(+) regulatory T cells was lower in the baicalin group compared with the CLP group.Conclusions/significanceBaicalin improves survival of mice with polymicrobial sepsis, and this may be attributed to its antibacterial property as well as its anti-inflammatory and anti-apoptotic effects.

Project description:ObjectiveAdministrative data are used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score.DesignRetrospective cohort study using 2012 administrative data from five U.S. states. Three cohorts of patients with severe sepsis were created: 1) International Classification of Diseases, 9th Revision, Clinical Modification codes for severe sepsis/septic shock, 2) Martin approach, and 3) Angus approach. The model was developed and internally validated in International Classification of Diseases, 9th Revision, Clinical Modification, cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score.SettingAcute care, nonfederal hospitals in New York, Maryland, Florida, Michigan, and Washington.SubjectsPatients in one of three severe sepsis cohorts: 1) explicitly coded (n = 108,448), 2) Martin cohort (n = 139,094), and 3) Angus cohort (n = 523,637) INTERVENTIONS: None.Measurements and main resultsMaximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit and C-statistics, respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. Goodness-of-fit demonstrated p value of more than 0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort), suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile.ConclusionsOur sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data.

Dataset Information

Robust health-score based survival prediction for a neonatal mouse model of polymicrobial sepsis.

Publications

Robust health-score based survival prediction for a neonatal mouse model of polymicrobial sepsis.

Similar Datasets

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