Admission Levels of Total Tau and β-Amyloid Isoforms 1–40 and 1–42 in Predicting the Outcome of Mild Traumatic Brain Injury

Background: The purpose of this study was to investigate if admission levels of total tau (T-tau) and β-amyloid isoforms 1-40 (Aβ40) and 1-42 (Aβ42) could predict clinical outcome in patients with mild traumatic brain injury (mTBI). Methods: A total of 105 patients with mTBI [Glasgow Coma Scale (GCS) ≥ 13] recruited in Turku University Hospital, Turku, Finland were included in this study. Blood samples were drawn within 24 h of admission for analysis of plasma T-tau, Aβ40, and Aβ42. Patients were divided into computed tomography (CT)-positive and CT-negative groups. The outcome was assessed 6–12 months after the injury using the Extended Glasgow Outcome Scale (GOSE). Outcomes were defined as complete (GOSE 8) or incomplete (GOSE < 8) recovery. The Rivermead Post Concussion Symptoms Questionnaire (RPCSQ) was also used to assess mTBI-related symptoms. Predictive values of the biomarkers were analyzed independently, in panels and together with clinical parameters. Results: The admission levels of plasma T-tau, Aβ40, and Aβ42 were not significantly different between patients with complete and incomplete recovery. The levels of T-tau, Aβ40, and Aβ42 could poorly predict complete recovery, with areas under the receiver operating characteristic curve 0.56, 0.52, and 0.54, respectively. For the whole cohort, there was a significant negative correlation between the levels of T-tau and ordinal GOSE score (Spearman ρ = −0.231, p = 0.018). In a multivariate logistic regression model including age, GCS, duration of posttraumatic amnesia, Injury Severity Score (ISS), time from injury to sampling, and CT findings, none of the biomarkers could predict complete recovery independently or together with the other two biomarkers. Plasma levels of T-tau, Aβ40, and Aβ42 did not significantly differ between the outcome groups either within the CT-positive or CT-negative subgroups. Levels of Aβ40 and Aβ42 did not significantly correlate with outcome, but in the CT-positive subgroup, the levels of T-tau significantly correlated with ordinal GOSE score (Spearman ρ = −0.288, p = 0.035). The levels of T-tau, Aβ40, and Aβ42 were not correlated with the RPCSQ scores. Conclusions: The early levels of T-tau are correlated with the outcome in patients with mTBI, but none of the biomarkers either alone or in any combinations could predict complete recovery in patients with mTBI

Admission Levels of Total Tau and β-Amyloid Isoforms 1–40 and 1–42 in Predicting the Outcome of Mild Traumatic Brain Injury

Background: The purpose of this study was to investigate if admission levels of total tau (T-tau) and beta-amyloid isoforms 1-40 (A beta 40) and 1-42 (A beta 42) could predict clinical outcome in patients with mild traumatic brain injury (mTBI).Methods: A total of 105 patients with mTBI [Glasgow Coma Scale (GCS) >= 13] recruited in Turku University Hospital, Turku, Finland were included in this study. Blood samples were drawn within 24 h of admission for analysis of plasma T-tau, A beta 40, and A beta 42. Patients were divided into computed tomography (CT)-positive and CT-negative groups. The outcome was assessed 6-12 months after the injury using the Extended Glasgow Outcome Scale (GOSE). Outcomes were defined as complete (GOSE 8) or incomplete (GOSE Results: The admission levels of plasma T-tau, A beta 40, and A beta 42 were not significantly different between patients with complete and incomplete recovery. The levels of T-tau, A beta 40, and A beta 42 could poorly predict complete recovery, with areas under the receiver operating characteristic curve 0.56, 0.52, and 0.54, respectively. For the whole cohort, there was a significant negative correlation between the levels of T-tau and ordinal GOSE score (Spearman rho = -0.231, p = 0.018). In a multivariate logistic regression model including age, GCS, duration of posttraumatic amnesia, Injury Severity Score (ISS), time from injury to sampling, and CT findings, none of the biomarkers could predict complete recovery independently or together with the other two biomarkers. Plasma levels of T-tau, A beta 40, and A beta 42 did not significantly differ between the outcome groups either within the CT-positive or CT-negative subgroups. Levels of A beta 40 and A beta 42 did not significantly correlate with outcome, but in the CT-positive subgroup, the levels of T-tau significantly correlated with ordinal GOSE score (Spearman rho = -0.288, p = 0.035). The levels of T-tau, A beta 40, and A beta 42 were not correlated with the RPCSQ scores.Conclusions: The early levels of T-tau are correlated with the outcome in patients with mTBI, but none of the biomarkers either alone or in any combinations could predict complete recovery in patients with mTBI.</div

Biomarkers for prediction of cerebrovascular diseases associated complications and outcome

Un accident cerebrovasculaire est une urgence médicale responsable d'une forte proportion de décès (5,9 millions par an). Les infections et plus concrètement la pneumonie, sont la complication la plus fréquente, en produisant environ 30% de décès. Même si leur diagnostic précoce est nécessaire pour commencer avec l'antibiothérapie dans le plus court délai, celle-ci est une tâche difficile pour les médecins. Par conséquent, afin d'appliquer de nouvelles stratégies de traitement, l'objective de cette étude était de découvrir et de valider des biomarqueurs d'infection capables de prédire les patients qui développeront une infection pendant leur hospitalisation. En utilisant des approches de protéomique quantitative et en incluant des données de la littérature, nous avons mis en évidence deux potentiels biomarqueurs : la neopterin et la SAA. Ces molecules sont un excellent outil pour commencer avec l'antibiothérapie plus précocement, conduisant à une meilleure prise en charge des patients et à une amélioration des pronostiques.Stroke is a medical emergency responsible of a high number of deaths (5.9 million annually). Post-stroke infections, more concretely pneumonia, are the most common post-stroke complication, producing around 30% of the mortality. Even if their early diagnosis is necessary to start with antibiotherapy in the shortest delay, this is a challenging task for the doctors. Consequently, in order to apply new treatment strategies as soon as possible, the aim of this study was to discover and validate infection biomarkers able to predict the patients that will develop an infection during hospitalization. By using quantitative proteomic approaches and including data of the literature, we highlighted two potential promising risk stratificator markers: neopterin and Serum Amyloid A (SAA). This work has demonstrated that both of them are excellent tools to start antibiotherapy in an earlier stage, leading to a better management of the patients and to an improvement of their associated outcomes

A Panel Comprising Serum Amyloid A, White Blood Cells and Nihss for the Triage of Patients at Low Risk of Post-Stroke Infection

Accurate and early prediction of poststroke infections is important to improve antibiotic therapy guidance and/or to avoid unnecessary antibiotic treatment. We hypothesized that the combination of blood biomarkers with clinical parameters could help to optimize risk stratification during hospitalization. In this prospective observational study, blood samples of 283 ischemic stroke patients were collected at hospital admission within 72 h from symptom onset. Among the 283 included patients, 60 developed an infection during the first five days of hospitalization. Performance predictions of blood biomarkers (Serum Amyloid-A (SAA), C-reactive protein, procalcitonin (CRP), white blood cells (WBC), creatinine) and clinical parameters (National Institutes of Health Stroke Scale (NIHSS), age, temperature) for the detection of poststroke infection were evaluated individually using receiver operating characteristics curves. Three machine learning techniques were used for creating panels: Associative Rules Mining, Decision Trees and an internal iterative-threshold based method called PanelomiX. The PanelomiX algorithm showed stable performance when applied to two representative subgroups obtained as splits of the main subgroup. The panel including SAA, WBC and NIHSS had a sensitivity of 97% and a specificity of 45% to identify patients who did not develop an infection. Therefore, it could be used at hospital admission to avoid unnecessary antibiotic (AB) treatment in around half of the patients, and consequently, to reduce AB resistance

Measuring Serum Amyloid A for Infection Prediction in Aneurysmal Subarachnoid Hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high rates of mortality and morbidity. Nosocomial infections, such as pneumonia or urinary tract infections, are among the main causes of worsening outcomes and death. The aim of this study was to discover a biomarker to predict infection in aSAH patients. For this purpose, the plasma of infected and noninfected patients was compared using quantitative mass spectrometry. The most interesting differentially expressed proteins were selected for validation by immunoassays on plasma samples taken from patients (n = 81) over 10 days of hospitalization. Predictive performances were established using Mann-Whitney U tests and receiver operating characteristic curves. Quantitative proteomics identified 17 significantly regulated proteins. Of these, levels of serum amyloid A (SAA) were significantly higher in infected patients (p < 0.007). ELISA confirmed that the concentrations were significantly higher (p < 0.002) already at hospital admission in patients who subsequently developed an infection during their hospitalization, (AUC of 76%) for a cutoff value of 90.9 μg/mL. Our data suggested that measuring SAA could be an efficient means of detecting patients susceptible of developing an infection during hospitalization after an aSAH. Its predictive capacity could lead to earlier antibiotherapy, improved patient management, and potentially better long-term outcomes

SAA (Serum Amyloid A): A Novel Predictor of Stroke-Associated Infections

BACKGROUND AND PURPOSE The aim of this study was to evaluate and independently validate SAA (serum amyloid A)-a recently discovered blood biomarker-to predict poststroke infections. METHODS The derivation cohort (A) was composed of 283 acute ischemic stroke patients and the independent validation cohort (B), of 367 patients. The primary outcome measure was any stroke-associated infection, defined by the criteria of the US Centers for Disease Control and Prevention, occurring during hospitalization. To determine the association of SAA levels on admission with the development of infections, logistic regression models were calculated. The discriminatory ability of SAA was assessed, by calculating the area under the receiver operating characteristic curve. RESULTS After adjusting for all predictors that were significantly associated with any infection in the univariate analysis, SAA remained an independent predictor in study A (adjusted odds ratio, 1.44 [95% CI, 1.16-1.79]; P=0.001) and in study B (adjusted odds ratio, 1.52 [1.05-2.22]; P=0.028). Adding SAA to the best regression model without the biomarker, the discriminatory accuracy improved from 0.76 (0.69-0.83) to 0.79 (0.72-0.86; P<0.001; likelihood ratio test) in study A. These results were externally validated in study B with an improvement in the area under the receiver operating characteristic curve, from 0.75 (0.70-0.81) to 0.76 (0.71-0.82; P<0.038). CONCLUSIONS Among patients with ischemic stroke, blood SAA measured on admission is a novel independent predictor of infection after stroke. SAA improved the discrimination between patients who developed an infection compared with those who did not in both derivation and validation cohorts. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00390962

Blood biomarkers to differentiate ischemic and hemorrhagic strokes

Objective: To validate a panel of blood biomarkers to differentiate between ischemic stroke (IS) and intracerebral hemorrhage (ICH) in patients with suspected stroke. Methods: Patients with suspected stroke admitted within 4.5 hours after onset were enrolled. Blood samples were collected at hospital admission. Glial fibrillary acid protein (GFAP), retinol binding protein 4 (RBP-4), N-terminal proB-type natriuretic peptide (NT-proBNP), and endostatin were measured by immunoassays. Cutoff points were obtained for 100% specificity for IS. A high-sensitivity assay to measure GFAP and rapid point-of-care tests (POCTs) to measure RBP-4 and NT-proBNP were used in subsets of patients. Biomarker panels were evaluated in another cohort of 62 stroke mimics. Results: A total of 189 patients (154 IS and 35 ICH) were enrolled. Patients with IS had higher RBP-4, NT-proBNP, and endostatin and lower GFAP levels than patients with ICH. The best biomarker combination for the identification of IS was RBP-4+NT-proBNP, which was able to identify 29.7% of patients with IS with 100% specificity. In the subset of patients for whom GFAP was measured with the high-sensitivity assay, RBP-4, NT-proBNP, and GFAP identified 51.5% of patients with IS with 100% specificity. When stroke mimics were included, specificities were reduced to 98.4 and 96.8%, respectively. POCTs of RBP-4 and NT-proBNP showed results similar results to those of conventional ELISAs. Conclusions: A biomarker panel including RBP-4, NT-proBNP, and GFAP provided moderate but potentially useful sensitivity rates at 100% specificity for IS diagnosis. If confirmed in future studies, this strategy might allow prehospital treatment in selected patients. Classification of evidence: This study provides Class I evidence that a biomarker panel including RBP-4, NT-proBNP, and GFAP distinguishes IS from ICH with moderate accuracy.</p

Role of Clinical Characteristics and Biomarkers at Admission to Predict One-Year Mortality in Elderly Patients with Pneumonia

Background: A hospitalization for community-acquired pneumonia results in a decrease in long-term survival in elderly patients. We assessed biomarkers at admission to predict one-year mortality in a cohort of elderly patients with pneumonia.Methods: A prospective observational study included patients >65 years hospitalized with pneumonia. Assessment of PSI, CURB-65, and biomarkers (C-reactive protein (CRP), procalcitonin (PCT), NT-pro-B-type natriuretic peptide (NT-proBNP), interleukin (IL)-6 and -8, tumor necrosis factor alpha (TNF-α), serum amyloid A (SAA), neopterin (NP), myeloperoxidase (MPO), anti-apolipoprotein A-1 IgG (anti-apoA-1), and anti-phosphorylcholine IgM (anti-PC IgM)) was used to calculate prognostic values for one-year mortality using ROC curve analyses. Post hoc optimal cutoffs with corresponding sensitivity (SE) and specificity (SP) were determined using the Youden index.Results: A total of 133 patients were included (median age 83 years [IQR: 78-89]). Age, dementia, BMI, NT-proBNP (AUROC 0.65 (95% CI: 0.55-0.77)), and IL-8 (AUROC 0.66 (95% CI: 0.56-0.75)) were significantly associated with mortality, with NT-proBNP (HR 1.01 (95% CI 1.00-1.02) and BMI (HR 0.92 (95% CI 0.85-1.000) being independent of age, gender, comorbidities, and PSI with Cox regression. At the cutoff value of 2200 ng/L, NT-proBNP had 67% sensitivity and 70% specificity. PSI and CURB-65 were not associated with mortality.Conclusions: NT-proBNP levels upon admission and BMI displayed the highest prognostic accuracy for one-year mortality and may help clinicians to identify patients with poor long-term prognosis.</p

Kinetics of inflammatory biomarkers to predict one-year mortality in older patients hospitalized for pneumonia: a multivariable analysis

Objectives: Long-term mortality is increased in older patients with pneumonia. We aimed to test whether residual inflammation is predictive of one-year mortality after pneumonia. Methods: Inflammation biomarkers (C-reactive protein [CRP], interleukin [IL]-6 and IL-8, tumor necrosis factor-α, serum amyloid A, neopterin, myeloperoxidase, anti-apolipoprotein A-1, and anti-phosphorylcholine IgM) were measured at admission and discharge in older patients hospitalized for pneumonia in a prospective study. Univariate and multivariate analyses were conducted using absolute level at discharge and relative and absolute differences between admission and discharge for all biomarkers, along with usual prognostic factors. Results: In the 133 included patients (median age, 83 years [interquartile range: 78-89]), one-year mortality was 26%. In univariate analysis, the relative difference of CRP levels had the highest area under the receiver operating characteristic curve (0.70; 95% confidence interval [CI] 0.60-0.80). A decrease of CRP levels of more than 67% between admission and discharge had 68% sensitivity and 68% specificity to predict survival. In multivariate analysis, lower body mass index (hazard ratio=0.87 [CI 95% 0.79-0.96], P-value=0.01), higher IL-8 (hazard ratio=1.02 [CI 95% 1.00-1.04], P-value=0.02), and higher CRP (1.01 [95% CI 1.00-1.02], P=0.01) at discharge were independently associated with mortality. Conclusion: Higher IL-8 and CRP levels at discharge were independently associated with one-year mortality. The relative CRP difference during hospitalization was the best individual biomarker for predicting one-year mortality.</p

Accuracy of C-reactive protein, procalcitonin, serum amyloid A and neopterin for low-dose CT-scan confirmed pneumonia in elderly patients: A prospective cohort study

The diagnosis of pneumonia based on semiology and chest X-rays is frequently inaccurate, particularly in elderly patients. Older (C-reactive protein (CRP); procalcitonin (PCT)) or newer (Serum amyloid A (SAA); neopterin (NP)) biomarkers may increase the accuracy of pneumonia diagnosis, but data are scarce and conflicting. We assessed the accuracy of CRP, PCT, SAA, NP and the ratios CRP/NP and SAA/NP in a prospective observational cohort of elderly patients with suspected pneumonia