Alteplase administration for acute ischemic stroke (AIS) in ER - a 5-year review

ER visits for AIS grown in last 10 years by 25% Ongoing effort by AHA/ASA to improve access to care with early stroke recognition and awareness Time is brain; rate of thrombolysis with alteplase (ALT) should increase with better EMS systems, awareness, and educatio

Impact of a multidisciplinary group on management of patent foramen ovale in cryptogenic stroke and outcome measures: A retrospective study

Patent foramen ovale (PFO) is a common cardiac abnormality present in roughly 25% of the adult population. Although typically benign, patients with a PFO account for 50% of the population of cryptogenic stroke, an ischemic stroke with an unknown cause. Guidelines suggest closure in patients with a cryptogenic stroke found to have a PFO; however, it is unclear whether medical treatment should be given to all patients, regardless of PFO closure, and data is limited on the treatment outcomes. This retrospective chart analysis of 80 patients with a cryptogenic stroke found to have a PFO will investigate the criteria a multidisciplinary team used to determine whether patients should undergo PFO closure, receive medical therapy, or receive both. Additionally, it will investigate stroke recurrence, the percentage of patients with postprocedural complications, and the percentage of patients where the cause of the stroke was determined later. There is currently limited data on treatment outcomes and stroke recurrence in this cohort of patients, so this study will provide valuable knowledge that will help clinicians make more informed decisions on how to treat patients with cryptogenic stroke found to have a PFO

Early Follow-Up Phone Calls to Reduce 30-Day Readmissions For Stroke Patients Discharged to Home

Patients admitted to the acute stroke unit with minor neurologic deficits are frequently discharged directly to home rather than to a rehabilitation center. Data from our tertiary care comprehensive stroke center has shown that in a 7-month period, 37% of patients admitted to the stroke unit were discharged home versus discharged to rehab or other location. Our average 30-day readmission rate for home discharges is 5.14%. More than 30% of these readmitted patients had been discharged on a Thursday or Friday on their index admission. When discharged home, patients typically are tasked with several responsibilities including but not limited to medication management, organizing follow-up appointments, monitoring blood pressure, and coordinating home services. In addition to recovering mentally and physically from stroke, these tasks can lead to additional burden particularly on weekends when access to care may be limited. We hypothesize that those who are discharged home on a Thursday or Friday are at higher risk for readmission and predict that scripted phone calls to these patients over the weekend could result in reduction in readmissions

Readmission Risk Assessment Tool for Stroke Patients

Introduction: Strokes are one of the leading causes of morbidity and mortality in the world and its cost of management has vastly increased; an effective prediction tool that utilizes artificial intelligence to lower the rate of stroke-related readmissions has the potential to lower healthcare costs and increase the quality of provider care. We hypothesize that machine learning techniques are superior to traditional statistics when determining the likelihood of 30-day readmission for Jefferson’s stroke patients. Methods: Jefferson’s existing data on stroke patients were cleaned, aggregated, and prepared to be split into train and test sets. Using the train sets, machine learning (ML) models such as Random Forest, Support Vector Machines, and Neural Networks were trained to assess the risk of readmission. Each model’s accuracy and precision were captured in the form of confusion matrices, AUCs, and more to reveal the most superior ML method in assessing this risk. These results were then compared to the readmission risk determined by traditional statistics. Results: After training the ML models, the test sets were inputted to determine how accurately they could predict a stroke patient’s chance of readmission with new data. Traditional statistics (in the form of logistic regression) showed an accuracy of 84%. The ML methods utilized resulted in the following accuracies: Random Forest at 95.50%, SVM at 94.79%, and Neural Networks at 95.40%. Discussion: This study not only demonstrates that machine learning methods are superior to traditional statistics in regard to determining the 30-day readmission risks for Jefferson stroke patients, but it also shows that the Random Forest model is the most accurate in doing so. The potential implications of this tool are large; its use can be seen at both the patient and the hospital levels by improving costs for the patient and the hospital as well as improving stroke education and care

Improving Resident Confidence and Efficiency During Stroke Alerts Through Simulation Training

Objectives Teach incoming neurology residents how to respond efficiently and appropriately to stroke alerts Improve the confidence level of residents during stroke alertshttps://jdc.jefferson.edu/patientsafetyposters/1084/thumbnail.jp

Improving Medical and Endovascular Management for Acute Ischemic Stroke Through Multidisciplinary Education and Simulation

Primary goals: Reduce door to treatment times (both DTN and DTP) to meet and exceed existing guidelines metrics. Educate residents about acute stroke management, including national guidelines and new institutional protocols to improve efficiency during stroke alerts.https://jdc.jefferson.edu/patientsafetyposters/1097/thumbnail.jp

The Prevalence and Risk Factors of Acute Myocardial Infarction (AMI) After Acute Ischemic Stroke (AIS) in the United States

Objectives: To determine the prevalence and risk factors for, and the association with in-hospital mortality of, AMI after AIS, and to study the effect of intravenous recombinant tissue plasminogen activator (r-tPA) in this setting. We hypothesized that AMI would be associated with lower survival rate at hospital discharge but that intravenous r-tPA would be associated with lower risk of AMI

Pennsylvania comprehensive stroke center collaborative: Statement on the recently updated IV rt-PA prescriber information for acute ischemic stroke.

OBJECTIVE: Recently, the FDA guidelines regarding the eligibility of patients with acute ischemic stroke to receive IV rt-PA have been modified and are not in complete accord with the latest AHA/ASA guidelines. The resultant differences may result in discrepancies in patient selection for intravenous thrombolysis. METHODS: Several comprehensive stroke centers in the state of Pennsylvania have undertaken a collaborative effort to clarify and unify our own recommendations regarding how to reconcile these different guidelines. RESULTS: Seizure at onset of stroke, small previous strokes that are subacute or chronic, multilobar infarct involving more than one third of the middle cerebral artery territory on CT scan, hypoglycemia, minor or rapidly improving symptoms should not be considered as contraindications for intravenous thrombolysis. It is recommended to follow the AHA/ASA guidelines regarding blood pressure management and bleeding diathesis. Patients receiving factor Xa inhibitors and direct thrombin inhibitors within the preceding 48h should be excluded from receiving IV rt-PA. CT angiography is effective in identifying candidates for endovascular therapy. Consultation with and/or transfer to a comprehensive stroke center should be an option where indicated. Patients should receive IV rt-PA up to 4.5h after the onset of stroke. CONCLUSIONS: The process of identifying patients who will benefit the most from IV rt-PA is still evolving. Considering the rapidity with which patients need to be evaluated and treated, it remains imperative that systems of care adopt protocols to quickly gather the necessary data and have access to expert consultation as necessary to facilitate best practices