Risk assessment models for potential use in the emergency department have lower predictive ability in older patients compared to the middle-aged for short-term mortality - a retrospective cohort study

Table S1. Comparison of Baseline characteristics of the TRIAGE II study and TRIAGE III study. Patients above 40 years were included in the current study Table S2. Comparison of AUCs of individual predictors in discriminating short-term mortality of ED patients, grouped according to age: 40–69 years (middle-aged), and 70+ years (older). Figure S1. Area under the Curve (AUC) for Receiver operating characteristics for all-cause mortality within 7 days for acutely admitted patients. Comparison of patients aged 40-69 (Middle-aged, blue colour), and patients aged 70+ (Older, red colour). The graph presents four different approaches of risk assessment of patients acutely presenting at the emergency department. Two different triage algorithms; Adaptive Process Triage (ADAPT) and Copenhagen Triage Algorithm (CTA), a predictive model using four vital signs (heart rate, arterial oxygen saturation, respiratory rate and systolic blood pressure), and a predictive model using levels of seven routine biomarkers (albumin, creatinine, c-reactive protein, haemoglobin, leucocytes, potassium, sodium). (DOCX 241 kb

Connective tissue growth factor is increased in plasma of type 1 diabetic patients with nephropathy

OBJECTIVE - Connective tissue growth factor (CTGF) is strongly upregulated in Fibrotic disorders and has been hypothesized to play a role in the development and progression of diabetes complications. The aim of the present study was to investigate the possible association of plasma CTGF levels in type I diabetic patients with markers relevant to development of diabetes complications. RESEARCH DESIGN AND METHODS - Plasma CTGF levels (full-length and NH2-terminal fragments) were determined in 62 well-characterized patients with type 1 diabetes and in 21 healthy control subjects. Correlations of these plasma CTGF levels with markers of glycemic control, platelet activation, endothelial activation, nephropathy, and retinopathy were investigated. RESULTS - Elevated plasma NH2-terminal fragment of CTGF (CTGF-N) levels were detected in a subpopulation of type I diabetic patients and were associated with diabetic nephropathy. Stepwise regression analysis revealed contribution of atbuminuria, creatinine clearance, and duration of diabetes as predictors of plasma CTGF-N level. Elevation of plasma CTGF-N levels in patients with retinopathy was probably clue to renal comorbidity. CONCLUSIONS - Plasma CTGF-N levels are elevated in type 1 diabetic patients with nephropathy and appear to be correlated with proteinuria and creatinine clearance. Further studies will be needed to determine the relevance of plasma CTGF as a clinical marker and/or pathogenic factor in diabetic nephropath

Additional file 1: of Risk assessment models for potential use in the emergency department have lower predictive ability in older patients compared to the middle-aged for short-term mortality – a retrospective cohort study

Table S1. Comparison of Baseline characteristics of the TRIAGE II study and TRIAGE III study. Patients above 40 years were included in the current study Table S2. Comparison of AUCs of individual predictors in discriminating short-term mortality of ED patients, grouped according to age: 40–69 years (middle-aged), and 70+ years (older). Figure S1. Area under the Curve (AUC) for Receiver operating characteristics for all-cause mortality within 7 days for acutely admitted patients. Comparison of patients aged 40-69 (Middle-aged, blue colour), and patients aged 70+ (Older, red colour). The graph presents four different approaches of risk assessment of patients acutely presenting at the emergency department. Two different triage algorithms; Adaptive Process Triage (ADAPT) and Copenhagen Triage Algorithm (CTA), a predictive model using four vital signs (heart rate, arterial oxygen saturation, respiratory rate and systolic blood pressure), and a predictive model using levels of seven routine biomarkers (albumin, creatinine, c-reactive protein, haemoglobin, leucocytes, potassium, sodium). (DOCX 241 kb