Monocytic HLA-DR expression kinetics in septic shock patients with different pathogens, sites of infection and adverse outcomes

At present, the neuroinfections in children are a socially significant problem, as they can lead to disability and death.Aim. To reveal the patterns of clinical manifestations of neuroinfections in the children’s central nervous system.Materials and methods. We investigated 91 cases of neuroinfections in children. The children underwent treatment in the Regional Infectious Diseases Hospital (Chita) between 2007 and 2014. Among 91 cases, 32 patients had viral neuroinfections and 59 had bacterial infections.Results. The young boys have bacterial neuroinfections more often. Headaches were found in 73.6 % of children; more often in children with viral neuroinfections – in 87.5 %, and less frequent in children with bacterial neuroinfections – in 66.1 %, p ˂ 0.01 The disease often starts with fever and vomiting. The neck stiffness and the Kernig symptom were often found in patients with bacterial neuroinfections persisting for 5 ± 1.7 days and 4 ± 1.9 days correspondingly, in children with viral neuroinfections – for 3 ± 1.4 and 3 ± 1.2 days, p ˂ 0.05. Pneumococcal etiology of the disease underlies the most severe and protracted cases in the course of neuroinfections. Pneumococcus causes the most severe and protracted diseases of the nervous system. Most often the bacterial neuroinfections cause cerebral edema and septic shock.Conclusions. Neuroinfections have typical clinical signs that need to be properly interpreted and evaluated by physicians to reduce adverse outcomes.

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The Early Expression of HLA-DR and CD64 Myeloid Markers Is Specifically Compartmentalized in the Blood and Lungs of Patients with Septic Shock

Mediators of Inflammation ◽

10.1155/2016/3074902 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Tomasz Skirecki ◽

Małgorzata Mikaszewska-Sokolewicz ◽

Grażyna Hoser ◽

Urszula Zielińska-Borkowska

Keyword(s):

Septic Shock ◽

Peripheral Blood ◽

Prognostic Value ◽

Lavage Fluid ◽

Blood Monocytes ◽

Activation Markers ◽

Circulating Cells ◽

Neutrophil Cd64 ◽

Hla Dr ◽

The Status

Identification of reliable biomarkers is key to guide targeted therapies in septic patients. Expression monitoring of monocyte HLA-DR and neutrophil CD64 could fulfill the above need. However, it is unknown whether their expression on circulating cells reflects the status of tissue resident cells. We compared expressions of HLA-DR and CD64 markers in the circulation and airways of septic shock patients and evaluated their outcome prognostic value. The expression of CD64 on neutrophils and HLA-DR on monocytes was analyzed in the peripheral blood and mini-bronchoalveolar lavage fluid cells by flow cytometry. Twenty-seven patients with septic shock were enrolled into the study. The fluorescence intensity of HLA-DR on circulating monocytes was 3.5-fold lower than on the pulmonary monocytes (p=0.01). The expression of CD64 on circulating and airway neutrophils was similar (p=0.47). Only the expression of CD64 on circulating neutrophils was higher in nonsurvivors versus survivors (2.8-fold;p=0.031). Pulmonary monocytes display a higher level of HLA-DR activation compared to peripheral blood monocytes but the expression of neutrophil CD64 is similar on lung and circulating cells. Death in septic patients was effectively predicted by neutrophil CD64 but not monocytic HLA-DR. Prognostic value of cellular activation markers in septic shock appears to strongly depend on their level of compartmentalization.

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Effects of stress-dose hydrocortisone therapy in septic shock (part III): monocyte HLA-DR expression and blood interferon-? concentration. Preliminary results of a double blinded, randomized, placebo-controlled cross-over study

Critical Care ◽

10.1186/cc480 ◽

1999 ◽

Vol 3 (Suppl 1) ◽

pp. P106

Author(s):

T Böhnke ◽

C Schulz ◽

D Keh ◽

M Pettersson ◽

S Weber-Carstens ◽

...

Keyword(s):

Septic Shock ◽

Preliminary Results ◽

Hla Dr ◽

Effects Of Stress ◽

Double Blinded ◽

Stress Dose

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Decreased expression of HLA-DR antigen-associated invariant chain mRNA predicts mortality after septic shock

Critical Care ◽

10.1186/cc11698 ◽

2012 ◽

Vol 16 (S3) ◽

Author(s):

M-A Cazalis ◽

L Cavé ◽

J Demaret ◽

V Barbalat ◽

E Cerrato ◽

...

Keyword(s):

Septic Shock ◽

Invariant Chain ◽

Hla Dr

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Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

10.21203/rs.3.rs-73620/v1 ◽

2020 ◽

Author(s):

Chin-Chuan Hsu ◽

Yuan Kao ◽

Chien-Chin Hsu ◽

Chia-Jung Chen ◽

Shu-Lien Hsu ◽

...

Keyword(s):

Artificial Intelligence ◽

Septic Shock ◽

Logistic Regression ◽

Prediction Model ◽

Real Time ◽

Adverse Outcomes ◽

Support Vector ◽

Icu Admission ◽

All Cause Mortality ◽

The Impact

Abstract Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

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