Collaborative Interventions Reduce Time-to-Thrombolysis for Acute Ischemic Stroke in a Public Safety Net Hospital

Threlkeld, Zachary D.; Kozak, Benjamin M.; McCoy, David; Cole, Sara; Martin, Christine; Singh, Vineeta

doi:10.1016/j.jstrokecerebrovasdis.2017.03.004

Cited by 17 publications

(17 citation statements)

References 25 publications

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“…Stroke pharmacists were trained and equipped with a mobile clot box containing labetalol, tPA and other equipment to facilitate tPA preparation and administration in the CT suite, expediting timely reperfusion therapy delivery. 15 The results were similarly replicated in two retrospective cohort studies which found the involvement of an ED stroke pharmacist is independently associated with a DNT reduction of up to 23 minutes. 14,54 The retrospective nature of the studies and different times of ED presentation in the patient groups introduces chronology bias and may skew workflow benefits in favour of the pharmacy group.…”

Section: Imaging To Intravenous Thrombolysismentioning

confidence: 62%

“…Interventions prior to the administration of reperfusion therapy include ambulance prenotification, direct patient transportation to imaging, tele‐stroke consultation and point‐of‐care testing (POCT) 10‐13 . The use of stroke pharmacists, bedside preparation and administration of thrombolytic therapy has been proposed to streamline reperfusion therapy delivery 14,15 . The utility of direct‐to‐angiography transport and parallel, standardized workflow has similarly been indicated to streamline thrombectomy procedures 16 .…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13] The use of stroke pharmacists, bedside preparation and administration of thrombolytic therapy has been proposed to streamline reperfusion therapy delivery. 14,15 The utility of direct-to-angiography transport and parallel, standardized workflow has similarly been indicated to streamline thrombectomy procedures. 16 The aforementioned workflow interventions may be supplemented with broader systems-based approaches including physician education, training and feedback to champion successful and rapid reperfusion treatment delivery and improve patient outcomes.…”

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confidence: 99%

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In‐hospital acute stroke workflow in acute stroke – Systems‐based approaches

et al. 2020

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The therapeutic goal for acute ischaemic stroke (AIS) patients is to achieve timely restoration of cerebral blood flow to the ischaemic brain tissue at risk of infarction and reduce neurological complications. 1 Current literature supports the efficacy and use of intravenous thrombolysis (IVT) with tissue plasminogen activator (tPA) and endovascular thrombectomy (EVT) in the treatment of AIS, with EVT proven to be more efficacious in patients presenting with large vessel occlusion (LVO). 2-5 When tPA and EVT are administered within their therapeutic time window of ≤4.5 and ≤6 hours, respectively, both therapies improve both recovery and clinical outcome of stroke patients. 2-6 Results from the DAWN trial have proposed extending the therapeutic time window for EVT to 24 hours due to improved efficacy and similar safety profiles. 7 Time to treatment is arguably the most important determinant of the efficacy of reperfusion

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Section: Imaging To Intravenous Thrombolysismentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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In‐hospital acute stroke workflow in acute stroke – Systems‐based approaches

et al. 2020

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“…More importantly, diagnosis can be established through bedside examination by specialists because it is a qualitative measurement. If the symptom occurs outside a hospital, a substantial time delay can cause poor outcomes for acute stroke patients [ 3 - 5 ]. In addition, the objective and accurate neurological assessments are not possible by mere visual examination because the examiner cannot easily trace the movement using the conventional neurological examination when there are subtle weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study

Park¹,

Lee²,

Han³

et al. 2020

J Med Internet Res

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Background Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. Objective In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. Methods We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). Results The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. Conclusions The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

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“…The role of care delivery by safety-net hospitals has been examined in other neurologic disease states including thrombolysis for stroke and radiosurgery for metastatic brain cancer. [23, 24] However, impact of hospital safety-net burden on endovascular treatment of ruptured brain aneurysms remains unstudied. This investigation aims to determine the impact of hospital safety net status on time to endovascular treatment of ruptured brain aneurysms.…”

Section: Introductionmentioning

confidence: 99%