Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Project description:Delirium is common, leads to other adverse outcomes, and is costly. However, it often remains unrecognized in most clinical settings. The Confusion Assessment Method (CAM) is the most widely used diagnostic algorithm, and operationalizing its features would be a substantial advance for clinical care.To derive the 3D-CAM, a new 3-minute diagnostic assessment for CAM-defined delirium, and validate it against a clinical reference standard.Derivation and validation study.4 general medicine units in an academic medical center.201 inpatients aged 75 years or older.20 items that best operationalized the 4 CAM diagnostic features were identified to create the 3D-CAM. For prospective validation, 3D-CAM assessments were administered by trained research assistants. Clinicians independently did an extensive assessment, including patient and family interviews and medical record reviews. These data were considered by an expert panel to determine the presence or absence of delirium and dementia (reference standard). The 3D-CAM delirium diagnosis was compared with the reference standard in all patients and subgroups with and without dementia.The 201 participants in the prospective validation study had a mean age of 84 years, and 28% had dementia. The expert panel identified 21% with delirium, 88% of whom had hypoactive or normal psychomotor features. Median administration time for the 3D-CAM was 3 minutes (interquartile range, 2 to 5 minutes), sensitivity was 95% (95% CI, 84% to 99%), and specificity was 94% (CI, 90% to 97%). The 3D-CAM did well in patients with dementia (sensitivity, 96% [CI, 82% to 100%]; specificity, 86% [CI, 67% to 96%]) and without dementia (sensitivity, 93% [CI, 66% to 100%]; specificity, 96% [CI, 91% to 99%]).Limited to single-center, cross-sectional, and medical patients only.The 3D-CAM operationalizes the CAM algorithm using a 3-minute structured assessment with high sensitivity and specificity relative to a reference standard and could be an important tool for improving recognition of delirium.National Institute on Aging.

Project description:INTRODUCTION: Sepsis is a complex immunological response to infection characterized by early hyper-inflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on Systemic Inflammatory Response Syndrome (SIRS) differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. METHODS: This was a multi-centre, prospective clinical trial conducted across four tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n = 27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n = 38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n = 20). Each participant had minimally 5 ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. RESULTS: Based on preliminary microarray analyses comparing HC and sepsis groups, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. The area under the curve (AUC) receiver operator characteristics (ROC) curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 and 92%. CONCLUSIONS: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients.

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Diagnostic Test Score Validation With a Fallible Criterion.

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Diagnostic Test Score Validation With a Fallible Criterion.

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