The emerging role of red blood cells in cytokine signalling and modulating immune cells

Karsten, Elisabeth; Herbert, Ben R.

doi:10.1016/j.blre.2019.100644

Cited by 27 publications

(26 citation statements)

References 125 publications

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“…Another independent risk factor identified in this study was The relationship between allogeneic blood transfusion and postoperative pneumonia can be partially explained by changes in immune function (33,34). Longer storage of transfused blood products was also reported to be associated with an increased risk of postoperative pneumonia as well as poorer outcomes, which may be explained by the reduced oxygen-carrying capacity and transfusion-related inflammatory reactions (35)(36)(37)(38).…”

Section: Discussionmentioning

confidence: 67%

Risk factors for postoperative pneumonia after cardiac surgery: a prediction model

Wang¹,

Huang²,

Wang³

et al. 2021

J Thorac Dis

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Background: Postoperative pneumonia is the main infectious complication following cardiac surgery and is associated with significant increases in morbidity, mortality and health care costs. The aim of this study was to identify potential risk factors related to the occurrence of postoperative pneumonia in adult patients undergoing cardiac surgery and to develop a predictive system. Methods: Adult patients who underwent open heart surgery in our institution between 2016 and 2019 were enrolled in this study. Preoperative and intraoperative variables were collected and analyzed. A multivariate prediction model for evaluating the risk of postoperative pneumonia was established using logistic regression analysis via forward stepwise selection, and points were assigned to significant risk factors based on their regression coefficient values.Results: Postoperative pneumonia occurred in 530 of the 5,323 patients (9.96%). Prolonged stays in the postoperative intensive care unit (ICU) and hospital, as well as higher mortality (25.66% versus 0.65%), were observed in patients with postoperative pneumonia. Multivariate analysis identified 13 independent risk factors including patient demographics, comorbidities, cardiac function, cardiopulmonary bypass (CPB) duration, and blood transfusion. The prediction model showed good discrimination (C-statistic: 0.80) and was well calibrated (Hosmer-Lemeshow χ 2 =7.907, P value =0.443). A 32-point risk score was generated, and then three risk intervals were defined. Conclusions:We derived and validated a prediction model for postoperative pneumonia after cardiac surgery incorporating 13 easily discernible risk factors. The scoring system may be helpful for individualized risk estimations and clinical decision-making.

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Section: Discussionmentioning

confidence: 67%

Risk factors for postoperative pneumonia after cardiac surgery: a prediction model

Wang¹,

Huang²,

Wang³

et al. 2021

J Thorac Dis

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“…Another study conducted in patients undergoing coronary artery bypass grafting reported that limited blood transfusion could reduce the risk of POP, mortality, and health care costs [23]. The association between POP and blood transfusion can be partially explained by changes of immune function [24,25]. The length of blood storage might also affect the occurrence of POP [26].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Risk Prediction Model for Postoperative Pneumonia in Adult Patients Undergoing Stanford Type a Acute Aortic Dissection Surgery: A Case Control Study

Wang¹,

Huang²,

Wang³

et al. 2021

Preprint

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BackgroundPneumonia is a common complication after Stanford type A acute aortic dissection surgery (AADS) and contributes significantly to morbidity, mortality, and length of stay. The purpose of this study was to identify independent risk factors associated with pneumonia after AADS and to develop and validate a risk prediction model.MethodsAdults undergoing AADS between 2016 and 2019 were identified in a single-institution database. Patients were randomly divided into training and validation sets at a ratio of 2:1. Preoperative and intraoperative variables were included for analysis. A multivariate logistic regression model was constructed using significant variables from univariate analysis in the training set. A nomogram was constructed for clinical utility and the model was validated in an independent dataset.ResultsPostoperative pneumonia developed in 170 of 492 patients (34.6%). In the training set, multivariate analysis identified seven independent predictors for pneumonia after AADS including age, smoking history, chronic obstructive pulmonary disease, renal insufficiency, leucocytosis, low platelet count, and intraoperative transfusion of red blood cells. The model demonstrated good calibration (Hosmer-Lemeshow χ2 = 3.31, P = 0.91) and discrimination (C-index = 0.77) in the training set. The model was also well calibrated (Hosmer-Lemeshow χ2 = 5.73, P = 0.68) and showed reliable discriminatory ability (C-index = 0.78) in the validation set. By visual inspection, the calibrations were good in both the training and validation sets.ConclusionWe developed and validated a risk prediction model for pneumonia after AADS. The model may have clinical utility in individualized risk evaluation and perioperative management.Clinical Trial Registry NumberChiCTR1900028127.

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“…In addition to chemokines, interferons, interleukins and the hormone erythropoietin are classified as cytokines. From the initial discovery of chemokine binding to Duffy, many more cytokines have been shown to be associated with the erythrocyte [8]. A considerable step forward was taken in a study where almost 50 different cytokines were identified in erythrocyte preparations [9].…”

Section: The Erythrocyte In Immunologymentioning

confidence: 99%

“…Most of the cytokines have been localized extracellularly on the erythrocyte possibly bound to transmembrane receptors or associated to the glycocalyx. Macrophage migration inhibitory factor 1 (MIF1), D-dopachrome tautomerase (DDT) and interleukin-33 were found to have an intracellular localization [8,12] (Figure 1). Interleukin-33 is a nuclear localized interleukin belonging to the interleukin-1 family [12].…”

Section: The Erythrocyte In Immunologymentioning

confidence: 99%

“…In these encounters erythrocytes are screened for healthiness and may be repaired by the macrophages [120], for instance by removal of the inclusion bodies containing damaged hemoglobin, called Heinz bodies. Erythrocytes are the main source of blood MIF1 (macrophage migration inhibitory factor 1), although the physiological significance of this remains to be established [8]. At last, the senescent or eryptotic erythrocyte is phagocytosed by spleen or liver macrophages.…”

Section: Senescence Aging Eryptosis and Hemolysis Of The Erythrocytementioning

confidence: 99%

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Erythrocytes as Biomarkers of Virus and Bacteria in View of Metal Ion Homeostasis

Johansson¹,

Falk²

2021

Erythrocyte - A Peripheral Biomarker for Infection and Inflammation

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The erythrocyte contributes to the immune system in several ways. It sequesters interferons, interleukins or chemokines and by binding nucleic acid. It binds virus and bacteria and may deliver bacteria to macrophages for phagocytosis. It may also kill bacteria directly with oxygen. For proper function of the erythrocyte, homeostasis of reactive oxygen species, selenium, metal ions and trace elements is important. Erythrocytes display morphological and metabolic changes in diseases like sepsis, and in several genetic diseases. Patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), giving rise to the coronavirus disease 2019 (Covid-19), show many erythrocyte changes as compared to healthy controls. The erythrocyte responds to hemolysins by purinergic signaling leading to hemolysis or phosphatidylserine exposure on the plasma membrane. Phosphatidylserine marks erythrocytes for clearance by spleen macrophages. Regulated erythrocyte cell death, also called eryptosis, can be induced by oxidative stress, pathogen infection, and certain diseases like sepsis. Erythrocytes may, in the future, contribute more to diagnosis based on research and diagnostic technological development.

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The emerging role of red blood cells in cytokine signalling and modulating immune cells

Cited by 27 publications

References 125 publications

Risk factors for postoperative pneumonia after cardiac surgery: a prediction model

Risk factors for postoperative pneumonia after cardiac surgery: a prediction model

Development and Validation of a Risk Prediction Model for Postoperative Pneumonia in Adult Patients Undergoing Stanford Type a Acute Aortic Dissection Surgery: A Case Control Study

Erythrocytes as Biomarkers of Virus and Bacteria in View of Metal Ion Homeostasis

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