Image_1_CD44+ and CD31+ extracellular vesicles (EVs) are significantly reduced in polytraumatized patients with hemorrhagic shock – evaluation of their diagnostic and prognostic potential.jpeg

BackgroundHemorrhagic shock (HS) is responsible for approximately 2 million deaths per year worldwide and is caused in 80% by polytrauma. These patients need a precise and quick diagnostic, which should be based on a combination of laboratory markers and radiological data. Extracellular vesicles (EVs) were described as potential new markers and mediators in trauma. The aim of the present study was to analyze, whether the surface epitopes of plasma-EVs reflect HS in polytraumatized patients and whether cell-specific EV subpopulations are useful diagnostic tools.Material and methodsPlasma samples from polytraumatized patients (ISS ≥16) with HS (n=10) and without (n=15), were collected at emergency room (ER) and 24h after trauma. Plasma-EVs were isolated via size exclusion chromatography and EV-concentrations were detected by Coomassie Plus (Bradford) Assay. The EVs subpopulations were investigated by a bead-based multiplex flow cytometry measurement of surface epitopes and were compared with healthy controls (n=10). To investigate the diagnostic and prognostic potential of EVs subpopulations, results were correlated with clinical outcome parameters documented in the electronical patients’ record.ResultsWe observed a significant reduction of the total amount of plasma EVs in polytrauma patients with HS, as compared to polytrauma patients without HS and healthy controls. We found significant reduction of CD42a+ and CD41b+ (platelet-derived) EVs in all polytrauma patients, as well as a reduction of CD29+ EVs compared to healthy volunteers (*pConclusionOur data reveal that polytrauma patients with a hemorrhagic shock are characterized by a reduction of CD44+ and CD31+ plasma-EVs. Both EV populations showed a moderate correlation with the need of erythrocyte transfusion, were associated with non-survival and the need for catecholamines.</p

Image_2_CD44+ and CD31+ extracellular vesicles (EVs) are significantly reduced in polytraumatized patients with hemorrhagic shock – evaluation of their diagnostic and prognostic potential.jpeg

BackgroundHemorrhagic shock (HS) is responsible for approximately 2 million deaths per year worldwide and is caused in 80% by polytrauma. These patients need a precise and quick diagnostic, which should be based on a combination of laboratory markers and radiological data. Extracellular vesicles (EVs) were described as potential new markers and mediators in trauma. The aim of the present study was to analyze, whether the surface epitopes of plasma-EVs reflect HS in polytraumatized patients and whether cell-specific EV subpopulations are useful diagnostic tools.Material and methodsPlasma samples from polytraumatized patients (ISS ≥16) with HS (n=10) and without (n=15), were collected at emergency room (ER) and 24h after trauma. Plasma-EVs were isolated via size exclusion chromatography and EV-concentrations were detected by Coomassie Plus (Bradford) Assay. The EVs subpopulations were investigated by a bead-based multiplex flow cytometry measurement of surface epitopes and were compared with healthy controls (n=10). To investigate the diagnostic and prognostic potential of EVs subpopulations, results were correlated with clinical outcome parameters documented in the electronical patients’ record.ResultsWe observed a significant reduction of the total amount of plasma EVs in polytrauma patients with HS, as compared to polytrauma patients without HS and healthy controls. We found significant reduction of CD42a+ and CD41b+ (platelet-derived) EVs in all polytrauma patients, as well as a reduction of CD29+ EVs compared to healthy volunteers (*pConclusionOur data reveal that polytrauma patients with a hemorrhagic shock are characterized by a reduction of CD44+ and CD31+ plasma-EVs. Both EV populations showed a moderate correlation with the need of erythrocyte transfusion, were associated with non-survival and the need for catecholamines.</p

Table_1_CD44+ and CD31+ extracellular vesicles (EVs) are significantly reduced in polytraumatized patients with hemorrhagic shock – evaluation of their diagnostic and prognostic potential.docx

BackgroundHemorrhagic shock (HS) is responsible for approximately 2 million deaths per year worldwide and is caused in 80% by polytrauma. These patients need a precise and quick diagnostic, which should be based on a combination of laboratory markers and radiological data. Extracellular vesicles (EVs) were described as potential new markers and mediators in trauma. The aim of the present study was to analyze, whether the surface epitopes of plasma-EVs reflect HS in polytraumatized patients and whether cell-specific EV subpopulations are useful diagnostic tools.Material and methodsPlasma samples from polytraumatized patients (ISS ≥16) with HS (n=10) and without (n=15), were collected at emergency room (ER) and 24h after trauma. Plasma-EVs were isolated via size exclusion chromatography and EV-concentrations were detected by Coomassie Plus (Bradford) Assay. The EVs subpopulations were investigated by a bead-based multiplex flow cytometry measurement of surface epitopes and were compared with healthy controls (n=10). To investigate the diagnostic and prognostic potential of EVs subpopulations, results were correlated with clinical outcome parameters documented in the electronical patients’ record.ResultsWe observed a significant reduction of the total amount of plasma EVs in polytrauma patients with HS, as compared to polytrauma patients without HS and healthy controls. We found significant reduction of CD42a+ and CD41b+ (platelet-derived) EVs in all polytrauma patients, as well as a reduction of CD29+ EVs compared to healthy volunteers (*pConclusionOur data reveal that polytrauma patients with a hemorrhagic shock are characterized by a reduction of CD44+ and CD31+ plasma-EVs. Both EV populations showed a moderate correlation with the need of erythrocyte transfusion, were associated with non-survival and the need for catecholamines.</p

Image_3_CD44+ and CD31+ extracellular vesicles (EVs) are significantly reduced in polytraumatized patients with hemorrhagic shock – evaluation of their diagnostic and prognostic potential.jpeg

BackgroundHemorrhagic shock (HS) is responsible for approximately 2 million deaths per year worldwide and is caused in 80% by polytrauma. These patients need a precise and quick diagnostic, which should be based on a combination of laboratory markers and radiological data. Extracellular vesicles (EVs) were described as potential new markers and mediators in trauma. The aim of the present study was to analyze, whether the surface epitopes of plasma-EVs reflect HS in polytraumatized patients and whether cell-specific EV subpopulations are useful diagnostic tools.Material and methodsPlasma samples from polytraumatized patients (ISS ≥16) with HS (n=10) and without (n=15), were collected at emergency room (ER) and 24h after trauma. Plasma-EVs were isolated via size exclusion chromatography and EV-concentrations were detected by Coomassie Plus (Bradford) Assay. The EVs subpopulations were investigated by a bead-based multiplex flow cytometry measurement of surface epitopes and were compared with healthy controls (n=10). To investigate the diagnostic and prognostic potential of EVs subpopulations, results were correlated with clinical outcome parameters documented in the electronical patients’ record.ResultsWe observed a significant reduction of the total amount of plasma EVs in polytrauma patients with HS, as compared to polytrauma patients without HS and healthy controls. We found significant reduction of CD42a+ and CD41b+ (platelet-derived) EVs in all polytrauma patients, as well as a reduction of CD29+ EVs compared to healthy volunteers (*pConclusionOur data reveal that polytrauma patients with a hemorrhagic shock are characterized by a reduction of CD44+ and CD31+ plasma-EVs. Both EV populations showed a moderate correlation with the need of erythrocyte transfusion, were associated with non-survival and the need for catecholamines.</p

Table_1_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.docx

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p

Image_3_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.tiff

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p

Table_3_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.docx

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p

Image_2_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.tiff

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p

Image_5_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.tiff

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p

Image_4_Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.tiff

PurposeEarlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.Methods317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.ResultsA correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.ConclusionThe machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.</p