Abstract only Objective: Improve the institution of NIH Stroke scale (NIHSS) and outcomes of Stroke codes in low English proficiency population (LEP) at a comprehensive stroke center. Background: LEP ...individuals are those with limited ability to read, write, speak, or understand English. The 2000 national census showed 47 million U.S. citizens/residents aged 5 years and older spoke a language other than English at home. This is projected to grow by 67 million by 2050. For patients from culturally and linguistically diverse backgrounds, language barriers contribute to poorer quality of care. Stroke is a leading cause of serious long-term disability and death and our aim is to ensure that time-sensitive interventions during a Stroke code are available to our LEP patients in the most efficient and fastest manner. Design/Methods: 1) Pre-intervention survey of providers 2) Creating a set of English words that are more internationally used to assess dysarthria 3) Interpreter Education (for select languages) regarding stroke, acuity of Stroke codes, NIHSS content, tPA and thrombectomy 4) Easy access for providers to the trained interpreters and use of the new Dysarthria words 5) Post-intervention survey of providers running Stroke code 6) Compare post-intervention door-to-needle and door-to-thrombectomy times in languages intervened to other languages in the same period of time. Results: Pre-intervention survey shows that 84.6% of the providers (n=26) deemed running LEP stroke codes in a time-efficient manner as difficult or very difficult. 50% found getting a telephonic interpreter to start the communication to be slow/very slow. 88.5% found the telephonic providers to be either somewhat helpful or not so helpful during the code. 92% of the providers found the words to test dysarthria on the NIHSS to be not helpful in LEP stroke patients. ConclusionS: It is apparent that LEP Stroke codes can be improved based on the above provider opinions. Therefore, we have set into motion a multi-pronged strategy by rethinking the contents of NIHSS, interpreter education and finally instituting an intervention based on the same. The study has started the final phase of having providers access the trained interpreters. The data of the latter will be collected in about 2 years’ time.
Abstract only Introduction: Atrial fibrillation (AF), a well-defined ischemic stroke (IS) risk factor whose prevalence increases with age, is associated with higher stroke severity. We aimed to ...evaluate stroke severity and hospital mortality in a nationally representative sample of AF-related IS patients. Methods: We utilized data from the National (Nationwide) Inpatient Sample databases from 2015 - 2018 using ICD-10 diagnostic codes to identify individuals with IS and comorbid AF. The NIHSS was used to characterize stroke severity in a subset of cases after 10/1/2016. Nonparametric statistics and logistic regression analyses were conducted to evaluate associations between AF and hospital death. Results: Of the 382,758 IS cases, 99,566 (26%) had comorbid AF. AF increased linearly with age, reaching at 47% of all hospitalized IS patients 85+ years of age or older (Figure). Higher age, male sex, white race, obesity, and higher median income were associated with comorbid AF, whereas diabetes, hypertension, tobacco use, and hyperlipidemia were associated with reduced odds of comorbid AF. While 5.8% of all IS patients died during hospitalization, mortality was increased nearly two-fold in those with AF (9.0% vs. 4.6%, p<.001). Among in-hospital deaths from IS, comorbid AF increased with age, present in 59% of those 85+ years of age or older (Figure). NIHSS, reported in 21% of patients, was higher in AF patients (mean NIHSS 6 vs. 9, p<.001). High NIHSS was the strongest independent predictor of hospital death. Conclusion: The burden of AF in a nationally representative sample of hospitalized IS patients is substantial, present in nearly 50% of the 85+ age group. AF-related IS is more severe and more likely to be fatal. As our population ages, the prevalence of AF will only increase. Understanding the severity and fatality of AF-related IS will have profound implications for health systems and may better facilitate anticipatory guidance and AF treatment.
Purpose of Review
Antiplatelet therapy remains the standard of care in secondary stroke prevention for non-cardioembolic ischemic stroke and transient ischemic attack. We aim to examine the use of ...antiplatelet agents in secondary prevention through highlighting relevant clinical trials and meta-analyses as well as providing commentary regarding our practice.
Recent Findings
In the POINT and CHANCE trials, dual antiplatelet therapy reduced recurrent stroke compared to aspirin monotherapy. Sub-analyses of these trials suggest that genetic polymorphisms could play a role in diminishing the effectiveness of clopidogrel. Similarly, THALES demonstrated better outcomes with ticagrelor-aspirin combination therapy over aspirin monotherapy.
Summary
Combination antiplatelet therapy with aspirin and the P2Y12 inhibitors, clopidogrel and ticagrelor, reduced stroke recurrence in those presenting with mild ischemic stroke or high risk TIA. Genetic polymorphisms may play a role in determining the appropriate regimen. Questions remain regarding the optimal duration of combination antiplatelet therapy for various stroke etiologies.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The evolution of artificial intelligence (AI) systems in the field of anaesthesiology owes to notable advancements in data processing, databases, algorithmic programs, and computation power. Over the ...past decades, its accelerated progression has enhanced safety in anaesthesia by improving the efficiency of equipment, perioperative risk assessments, monitoring, and drug administration systems. AI in the field of anaesthesia aims to improve patient safety, optimise resources, and improve the quality of anaesthesia management in all phases of perioperative care. The use of AI is likely to impact difficult airway management and patient safety considerably. AI has been explored to predict difficult intubation to outperform conventional airway examinations by integrating subjective factors, such as facial appearance, speech features, habitus, and other poorly known features. This narrative review delves into the status of AI in airway management, the most recent developments in this field, and its future clinical applications.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
