Background: The long-term follow-up of clinical outcomes in patients admitted with acute stroke can identify relevant clinical data in the prevention of stroke recurrence as well as measure the quality of life of such patients. Follow-up after discharge in hospitals without stroke clinics can be a challenge. Therefore we created in our hospital an outcomes measurement nuclei characterized as a data collection center, with the main objective of periodically measuring clinical outcomes and quality of life of patients after hospital discharge.This sector works together with the different clinical specialties in providing information with a focus on outcome indicators, using questionnaires to estimate the parameters of evaluation of health states.Our objective was to describe data obtained from this data collection center evaluating post-discharge quality of life of patients treated in our stroke center 30, 90, 180 days and 01 years after the diagnosis. Methods: The study was conducted from January 2012 to March 2016, at a tertiary, general, private hospital in São Paulo, Brazil. Phone calls using the EuroQol instrument (EQ-5D) to measure quality of life were performed. The modified Rankin scale and a structured questionnaire to identify stroke recurrence, readmissions and medication failures were also applied. Results: We conducted 2184 telephone calls and obtained 1727 (79%) successful contacts. The mean EQ-5D at 30 days was: 0.732 +/-0.558; at 90 days: 0.722 +/- 0.358; at 180 Days: 0.781 +/- 0.326; and at 12 months 0.766 +/-0.349. During the follow-up, 31 patients (2%) died. The main reasons for censuring patients were unsuccessful contact after 3 attempts (51%); outdated registration data (3%) and refusals (9%). Conclusion: In conclusion, monitoring of standardized clinical outcomes after stroke is possible even in private non academic hospitals, allowing the acquisition of quality of care indicators and patient centered outcomes.

Our aims were to describe temporal trends of the performances measures for ischemic stroke and to evaluate factors influencing stroke quality performances indicators. Methods: We evaluated consecutive patients discharged with ischemic stroke at Hospital Israelita Albert Einstein from January 2009 to December 2013. Clinical characteristics and ten predefined performance measures selected by the Get with the Guidelines (GWTG) stroke program as targets for stroke quality improvement were evaluated. Results: We evaluated 551 patients, the median age was 77.0 years old [64.0-84.0], 58.4% were man and the median time from symptom onset to hospital admission was 345.0 minutes [104.5-1417.5]. The median length of stay was 8.0 days [4.0-14.0], 17.1% received thrombolytic treatment. A good outcome at discharge (modified Rankin scale <3) was observed in 67.6% of the patients, 7.9% of the patients had died. The quality indicators that were different between years were: use of antithrombotic therapy at discharge, cholesterol-lowering therapy, smoking cessation counseling and stroke education. In the univariate analysis being discharged in a Joint Commission visit year (OR 1.84 [1.29-2.61], p<0.01), female sex (OR 0.68 CI [0.48-0.96], p=0.03), dyslipidemia (OR 1.95 [1.33-2.88], p<0.01) and receiving thrombolytic treatment (OR 1.81 [1.08-3.02], CI p=0.02) were found to be associated with a perfect care index of 85% or higher. After multivariate adjustment, only thrombolytic treatment OR 2.06 CI 95%[1.21-3.51] p<0.01, dyslipidemia OR 2.03 [1.36-3.02] p<0.01 and discharge in a Joint Commission’s visit year OR 1.8 CI 95%[1.29-2.65] p<0.01 remained in the model as predictors of higher perfect care index. Conclusion: Clinical characteristics like dyslipidemia and being treated with thrombolysis and being admitted in a Joint Commission visit year seems to influence adhesion to quality performance indicators in our Stroke Center. A more continuous evaluation of Stroke Centers by Joint Commission instead of pre specified evaluation visits seems to be justified.

Introduction: Distal hyperintense vessels (DHV) detected by FLAIR imaging are not uncommon in patients with acute ischemic stroke. The presence of DHV and its predictors has been scarcely evaluated in patients with transient ischemic attack (TIA), being associated to the presence of large vessel occlusion in such patients. We assessed the hypothesis that DHV are frequent in patients with TIA and do correlate with relevant clinical and neuroimaging characteristics. Methods: We evaluated a database of consecutive patients admitted with TIA from February 2009 to June 2012 who had undergone magnetic resonance imaging within 30 h of symptoms onset and intracranial and extracranial vascular imaging. We analyzed the relationship between DHV, clinical presentation, risk factors, neuroimaging characteristics and large artery stenosis or occlusion. DHV signals were defined on FLAIR images as focal, linear or serpentine, hyperintense signals relative to gray matter. Two neuroradiologists blinded to clinical information reached consensus regarding the presence of DHV. Results: Seventy-two TIA patients were enrolled. The median time from symptoms onset to MRI was 8:39 h [4:21, 14:13]. DHV signals on FLAIR images were present in 12 (16.7 %) patients. The overall agreement between examiners was good (k 0.67). Patients with DHV had more atrial fibrillation (AF) than those without (41.7% versus 21.7%, p=0.05) and a trend towards more congestive heart failure (CHF) (8.3% versus 1.7%, p=0.2) and diabetes (41.7% versus 21.7%, p=0.1). There were no differences in the frequency of intracranial or cervical arterial stenosis, cerebral microbleeds and white matter abnormalities in patients with and without DHV. In a multivariate logistic regression analysis, only AF had a trend to be a predictor of DHV (OR=4.24, p=0.1). The statistical model to predict DHV including AF, diabetes, and CHF had a moderate fit in terms of discrimination (c statistic=0.62) Conclusion: DHV signals on FLAIR images occur in patients with TIA and might correlate with clinical variables like AF and not only with large vessel occlusion as previously described. The presence of DVH in patients with TIA and AF might be a surrogate marker for a previous large vessel occlusion spontaneously recanalized.

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Background: Medical complications are potential barriers to optimal recovery after acute ischemic stroke. Hypotension and shock can be particularly deleterious to the recovery of the penumbral tissue. The frequency of shock in patients admitted with acute stroke has not been systematically evaluated. We assessed the hypothesis that shock is not uncommon after ischemic stroke and is related to clinical prognosis. Methods: We evaluated a prospectively collected database of consecutive patients admitted to a tertiary hospital with acute ischemic stroke from January 2010 to December 2015. Shock was defined as persisting hypotension requiring vasopressors to maintain mean arterial pressure ≥65 mmHg. Results: A total of 1145 patients (mean age 72 +/-16 years, 56% males) were evaluated. Shock was diagnosed in 15 patients (1.3%). These patients were similar in age, gender, blood pressure at hospital admission, and previous history of coronary artery disease to patients without shock. Sepsis was the most common single etiology of the shock (93%). Septic source was pulmonary in 60%, abdominal in 13%, undetermined in 20% and blood borne in 6.6%. Septic shock was diagnosed at a median of 14 days [8,20] after hospital admission. Patients who developed shock had a higher National Institutes of Health Stroke Scale (NIHSS) at admission (12 [8,20] vs 3 [1,10], p<0.01), a higher frequency of diabetes (60% vs 30%, p=0.01), urinary tract infection (27% vs 4.8%, p<0.01), pneumonia (47% versus 4.5%, p<0.01), acute renal (33% vs 0.7%, p<0.01) and respiratory (53% vs 1.8%, p<0.01) failures. Length of hospital stay (39 [29,156] vs 8 days [4, 15], p<0.01) and ICU stay (19[7, 27.5] vs 0 days [0,3], p<0.01) were higher in patients with shock. Shock was associated with a hospital mortality of 80% vs 9% in patients without shock (p<0.01). In multivariate logistic regression analysis, only pneumonia (OR: 16.9, [4.1-69.4],p<0.01) and NIHSS 1.07 [1.01-1.15, p=0.04] at admission were predictors of developing shock during hospital admission. Conclusion: In conclusion, shock was an infrequent but severe complication in patients with stroke and was associated with a very high mortality. Sepsis was the most important etiology.