Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without clinical predictors. Methods: Blood samples from patients with acute TBI (all severities) were collected 6 months post injury using the Glasgow Outcome Scale Extended (GOSE) score, dichotomizing patients into: (i) those with favorable (GOSE≥5)/unfavorable outcome (GOSE ≤ 4) and complete (GOSE = 8)/incomplete (GOSE ≤ 7) recovery, and (ii) patients with mild TBI (mTBI) and patients with TBIs of all severities. Results: When sensitivity was set at 95–100%, the proteins' individual specificities remained low. H-FABP showed the best specificity (%) and sensitivity (100%) in predicting complete recovery in patients with mTBI. IL-10 had the best specificity (50%) and sensitivity (96%) in identifying patients with favorable outcome in patients with TBIs of all severities. When individual proteins were combined with clinical parameters, a model including H-FABP, NF-L, and ISS yielded a specificity of 56% and a sensitivity of 96% in predicting complete recovery in patients with mTBI. In predicting favorable outcome, a model consisting IL-10, age, and TBI severity reached a specificity of 80% and a sensitivity of 96% in patients with TBIs of all severities. Conclusion: Combining novel TBI biomarkers H-FABP and IL-10 with GFAP, NF-L and S100β and clinical parameters improves outcome prediction models in TBI.

Post-stroke infections occur in 20–36% of stroke patients and are associated with high morbidity and mortality rates. Early identification of patients at risk of developing an infection could improve care via an earlier treatment leading to a better outcome. We used proteomic tools in order to discover biomarkers able to stratify patients at risk of post-stroke infection. The post hoc analysis of a prospective cohort study including 40 ischemic stroke patients included 21 infected and 19 non-infected participants. A quantitative, isobaric labeling, proteomic strategy was applied to the plasma samples of 5 infected and 5 non-infected patients in order to highlight any significantly modulated proteins. A parallel reaction monitoring (PRM) assay was applied to 20 additional patients (10 infected and 10 non-infected) to verify discovery results. The most promising protein was pre-validated using an ELISA immunoassay on 40 patients and at different time points after stroke onset. Tandem mass analysis identified 266 proteins, of which only serum amyloid A (SAA1/2) was significantly (p = 0.007) regulated between the two groups of patients. This acute-phase protein appeared to be 2.2 times more abundant in infected patients than in non-infected ones. These results were verified and validated using PRM and ELISA immunoassays, which showed that infected patients had significantly higher concentrations of SAA1/2 than non-infected patients at hospital admission, but also at 1, 3, and 5 days after admission. The present study demonstrated that SAA1/2 is a promising predictor, at hospital admission, of stroke patients at risk of developing an infection. Further large, multicenter validation studies are needed to confirm these results. If confirmed, SAA1/2 concentrations could be used to identify the patients most at risk of post-stroke infections and therefore implement treatments more rapidly, thus reducing mortality.

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.

Background: Rapid identification of large vessel occlusion (LVO) is essential to optimize prehospital triage and timely access to mechanical thrombectomy, yet current clinical scales show limited diagnostic performance, particularly when high specificity is required to ensure patient safety. Methods: We performed a prospective analysis of 290 consecutive patients with suspected acute ischemic stroke presenting within 24 h of symptom onset ( n = 96 LVO), including 223 patients presenting within 6 h ( n = 71 LVO). Presence of LVO was determined by vascular neuroimaging (computed tomography angiography or magnetic resonance angiography) performed during routine clinical care. Circulating blood biomarkers and clinical variables were systematically evaluated to derive a multivariable panel for LVO detection. A data-driven modeling strategy (PanelomiX) was applied under predefined high-specificity constraints, with repeated stratified cross-validation used for panel ranking and robustness assessment. Diagnostic performance was compared against individual variables and previously reported biomarker–clinical combinations. Results: A multimodal panel combining NT-proBNP, D-dimer, mean blood pressure, and the National Institutes of Health Stroke Scale (NIHSS) score demonstrated superior discrimination compared with individual biomarkers, clinical variables alone or any other considered biomarker-clinical combination. At a specificity of 90%, the panel achieved a sensitivity of 59.8% in the 24 h cohort. Among patients presenting within 6 h of symptom onset, sensitivity reached 65.7% while maintaining a specificity of 90.3%. Cross-validated Youden index estimates decreased to 0.44 in both cohorts, confirming modest optimism bias consistent with the exploratory design of this single-cohort study. Conclusion: The integration of circulating biomarkers with clinical variables improved the high-specificity identification of LVO. This multimodal approach should be interpreted as a rule-in enrichment strategy to support early triage and optimized routing of patients with a high probability of LVO, rather than as a rule-out tool to exclude LVO in lower-risk patients.

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.