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• Serum biomarkers such as GFAP, UCH-L1 predict neurological outcomes after spontaneous subarachnoid hemorrhage (sSAH). • sSAH blood volume and serum biomarker show a robust correlation. • A structured blood sampling of serum biomarkers with automated immunoassay of chemiluminescence creates a framework for understanding neurological failure. • Threshold levels of GFAP and UCH-L1 describe predictive parameters for clinical outcomes. • The use of serum biomarkers is crucial in neurocritical care to prevent patients' clinical deterioration. Spontaneous non-traumatic subarachnoid hemorrhage (sSAH) is a severe brain vascular accident. Glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) can be theoretically assayed to predict a patient's progression, picturing different aspects of clinical failure after sSAH. The study aims to: a) explore the correlation between sSAH blood volume and biomarkers variation; b) evaluate if these can be predictive of the neurogenic response after sSAH and be prognostic of patient outcome; c) establish eventual threshold levels of biomarkers to define patients' clinical outcome. Blood volumetry at CT scan upon admission, GFAP and UCH-L1 were collected at 24 h, at 72 h, and after 7 days from hemorrhage. Trends and cut-off serum sampling were determined. Clinical outcome was assessed with mRS scale at 14 days. A strong correlation between GFAP and UCH-L1 and blood diffusion volume in all explored serum intervals related to unfavorable outcome. GFAP and UCH-L1 were very early predictors of unfavorable outcomes at 24 h from sSAH (p = 0.002 and 0.011 respectively). Threshold levels of UCH-L1 apparently revealed a very early, early and late predictor of unfavorable outcomes. GFAP and UCH-L1 represent a potential tool for prompt monitoring and customization of therapies in neurosurgical patients. [ABSTRACT FROM AUTHOR]

Objectives: The objectives were to ascertain the prevalence of renal impairment among patients with a takotsubo cardiomyopathy (TTC) episode and whether clinical outcomes are related to renal function.Methods: A total of 108 consecutive subjects with TTC were enrolled in a multicenter registry and followed for a mean period of 429 days. Renal function was evaluated during hospitalization in terms of acute kidney injury/failure and estimated glomerular filtration rate (eGFR). Incidence of death, rehospitalization, and recurrence of TTC during follow-up was recorded.Results: Raised creatinine levels can be found during hospitalizations for TTC episodes (analysis of variance P<.001). Incidence of acute kidney injury was 10%; that of acute kidney failure was 1%. Admission eGFR levels were proportional to the duration of hospitalization (r = -0.28, P<.01). Estimated GFR nadir values were related to adverse events at follow-up (log-rank P<.001). The hazard ratio of adverse events at follow-up in subjects with severe renal impairment (nadir eGFR <30 mL/[min 1.73 m(2)]) vs those with eGFR >60 mL/(min 1.73 m(2)) was 1.817 (95% confidence interval, 1.097-3.009; P<.05).Conclusions: Raised creatinine levels and impaired renal function may be found in patients with TTC. Lower eGFR values during hospitalization are associated with longer hospitalizations and higher rates of adverse events at follow-up. Renal function during a TTC episode should be carefully evaluated. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is common and may have severe consequences. Continuous long-term electrocardiogram (ECG) is widely used for AF screening. Recently, commercial ECG analysis software was launched, which automatically detects AF in long-term ECGs. It has been claimed that such tools offer reliable AF screening and save time for ECG analysis. However, this has not been investigated in a real-life patient cohort. Objective: To investigate the performance of automatic software-based screening for AF in long-term ECGs. Methods: Two independent physicians manually screened 22,601 hours of continuous long-term ECGs from 150 patients for AF. Presence, number, and duration of AF episodes were registered. Subsequently, the recordings were screened for AF by an established ECG analysis software (Pathfinder SL), and its performance was validated against the thorough manual analysis (gold standard). Results: Sensitivity and specificity for AF detection was 98.5% (95% confidence interval 91.72%–99.96%) and 80.21% (95% confidence interval 70.83%–87.64%), respectively. Software-based AF detection was inferior to manual analysis by physicians (P < .0001). Median AF duration was underestimated (19.4 hours vs 22.1 hours; P < .001) and median number of AF episodes was overestimated (32 episodes vs 2 episodes; P < .001) by the software. In comparison to extensive quantitative manual ECG analysis, software-based analysis saved time (2 minutes vs 19 minutes; P < .001). Conclusion: Owing to its high sensitivity and ability to save time, software-based ECG analysis may be used as a screening tool for AF. An additional manual confirmatory analysis may be required to reduce the number of false-positive findings. [Copyright &y& Elsevier]