Background: Rapid reperfusion with mechanical thrombectomy (MT) for large vessel occlusion leads to an improvement functional independence. Prolonged transfers from non-interventional to interventional hospitals are a major cause of delay in reperfusion times. The use of the 911 EMS system has accelerated myocardial infarction transfers and potentially could accelerate stroke transfers, but their effect on stroke transfer times have not been well delineated. Methods: In our 14- hospital system, transfers from non-interventional facilities to intrasystem or intersystem interventional capable facilities typically utilize a centralized group of nurses who arrange for contracted ambulance pickup (CAP). However, in San Bernardino County (CA), 911 EMS continuation of care (COC) is an alternative method available for transferring MT patients. Under the COC process, the sending hospital calls 911 and the nearest EMS ambulance responds to transport the patient to the thrombectomy center. We compared Door In, Door Out (DIDO) times between the COC vs CAP protocol between January 1, 2021 and January 30, 2022. Demographic and clinical variables were compared using t-test for continuous variables and chi-square for categorical variables. Results: Among 112 patients, 26 (23%) were transferred by COC and 86 (77%) by CAP. COC and CAP patients did not significantly differ in age ( Mdn 71 {IQR:22} vs Mdn 72 {IQR:20.5}, p = 0.81), sex (46% vs 36% female, p = 0.92), or presenting NIHSS ( Mdn 14 {IQR:12} vs Mdn 13 {IQR:14}, p = 0.62). Rates of thrombolytic started at the sending hospital were 54% (14/26) in COC patients and 48% (41/86) in ETAP patients (p = 0.58). DIDO time was faster in the COC group, ( Mdn 58 minutes {IQR 44-73} vs Mdn 94 minutes {IQR 79-142}, p<0.01). In contrast, there was a significant difference in DIDO time between intrasystem transfers by COC ( Mdn 69.5 min. {IQR: 48.5-115.5} and transfers out of system by COC ( Mdn 66 min.{IQR: 53-88}, p=0.20), suggesting that there was no special advantage for intrasystem transfers over intersystem transfers. Conclusion: Using the 911 EMS system for interfacility transport of patients eligible for MT reduced DIDO time at the sending hospital by over 40 minutes, which may contribute to improved functional outcome.
Introduction: The effect of tenecteplase (TNK) use on reducing interfacility transfer times and ambulance-based metrics for patients with a large vessel occlusion (LVO) eligible for endovascular thrombectomy (EVT) is not well studied. We analyzed how the administration of IV-TNK vs IV-tPA affected the Door In Door Out time (DIDO) as well as two novel ambulance-based metrics in patients arriving to the emergency department (ED) with an LVO who were transferred from 13 primary stroke centers to undergo EVT. Methods: Data were retrospectively abstracted for patients with an LVO from 09/2020 to 04/2022. Median DIDO times were calculated for patients who received no thrombolysis, tPA, and TNK. Subgroup analyses were performed for two novel time metrics, Door to Ambulance Arrival (DAA) and Ambulance Arrival to Ambulance Departure (AAAD), for patients that received IV thrombolysis. Statistical analysis was performed using the Wilcoxan rank sum and chi square tests. Results: There were 191 patients with AIS and an LVO. 130 received no IV thrombolysis and 62 received IV thrombolysis (32 tPA vs 29 TNK). There were no significant differences in baseline demographics between the thrombolysis and non-thrombolysis groups. Our sample was diverse in ethnicity with 60.7% being non-white. DIDO (TNK: 73 min {IQR 59-107} vs tPA: 106 min {IQR 88-137}, p = 0.01), DAA (TNK: 52.5 min {IQR 38-84} vs tPA: 77 min {IQR 61-105}, p = 0.03), and AAAD (TNK: 16.5 mins {IQR 10-22} vs tPA: 22 mins {IQR 18-40}, p = 0.01) were all faster for TNK cases compared to tPA cases. 58.5% of the TNK cases had a DIDO of ≤ 90 minutes vs 34.4% of tPA cases. Conclusions: Switching from IV-tPA to IV-TNK improved the DIDO by 33 minutes. This improvement was driven by two novel DIDO metrics: Door to Ambulance Arrival Time (DAA) and Ambulance Arrival to Ambulance Departure Time (AAAD) likely because the absence of a tPA infusion (drip & ship) allowed the use of non-critical care ambulances, which are more widely available, to transport patients.
Background: Door-In-Door-Out (DIDO) times are crucial to help improve outcomes for patients presenting to Primary Stroke Centers (PSCs) needing urgent endovascular therapy. However, DIDO is often a difficult measure to track and improve due to relatively low volumes at a single stroke program. Healthcare networks hold a significant advantage through standardization of processes which then allow for aggregate data collection. Methods: We designed a system that went live 1/2021 using PowerBI™ and Microsoft Suite™ products to monitor DIDO across 13 PSCs. Data queries from existing centralized stroke data were modeled together with internal transport team data into one report resulting in an interactive chart used to track DIDO and its related performance measures including: door to transfer center contact time (DTCC), door to ambulance arrival time (DAA), ambulance arrival to ambulance departure Time (AAAD) and reasons for delayed transfer. Data was analyzed using Wilcoxon Rank Sum test. Results: Metrics were assessed pre (1/1/2021-3/31/2021) and post (4/1/2021-12/31/2021) implementation of a dashboard guided process improvement. There were 32 transfers pre vs. 86 post-implementation. Median DIDO improved: (Pre: 120 min {IQR:82-146} vs. post: 86 min {IQR: 62-108} p=0.004). Other sub-metrics of DIDO all improved: Median DTCC (Pre: 55 min {IQR:32-76} vs. post: 38 min {IQR: 22-53} p=0.032); median DAA (Pre: 89 min {IQR:68-126} vs. post: 67 min {IQR: 43-84} p=0.041). There was a trend to improvement for the median AAAD: (Pre: 24 min {IQR:12-36} vs. post: 19 min {IQR: 10-24} p=0.054). Conclusion: Relatively low volume but high-risk events like DIDO transfers can be improved by leveraging the use of modern data display and analysis software solutions that process the collective data aggregate generated from standardized workflows across a health care network.
Background: Delays in pediatric stroke recognition and treatment are significant factors contributing to the high rate of morbidity and mortality. There is a lack of national standards for pediatric stroke quality measures. We created a dashboard to abstract pediatric stroke data, including patient volume and established quality measures, from our organization’s 14 hospitals. Methods: The regional pediatric stroke dashboard was created using the organization’s existing adult stroke dashboard as a template. The monitoring system was designed using PowerBI™ and Microsoft Suite™. Dashboard elements were modified to capture pediatric stroke demographics and quality indicators identified by the region’s multidisciplinary pediatric stroke workgroup. Data were abstracted using the organization’s electronic medical record system. Results: The dashboard provides a regionwide, visual representation of this organization’s pediatric stroke data. It is an automated and systematic graphic report describing pediatric stroke volumes and quality metrics. Between 1/2021-6/2022, there were 39 stroke cases: 15% subarachnoid hemorrhage(6/39), 56% intracerebral hemorrhage (22/39), 28% ischemic (11/39). Nine percent of ischemic strokes received a thrombolytic (1/11); 27% had thrombectomy (3/11); 54% were discharged to home (21/39) with no mortalities. The median age was 7.5 years and 66.7% were female. Future dashboard elements will include times for door-to-initial physician evaluation, door-to-Stroke Team notification, door-to-CT or MRI interpretation and door-to-lab results. Conclusion: The Pediatric Stroke Dashboard is an important component of the organization’s regional Pediatric Stroke Program. Leveraging this technology to track and trend the patient volumes and performance measures allows for continuous assessment and identification of opportunities for improvement to achieve pediatric stroke care excellence.
Background: The 2018 WAKE-UP Trial established the safety and efficacy of administering IV-tPA in patients who presented within 4.5 hours of symptom discovery based on specific MRI sequences. Implementation of the study results remains limited at Primary Stroke Centers (PSCs). We assessed the feasibility of a Comprehensive Stroke Center (CSC) MRI based unwitnessed thrombolysis protocol at 13 PSCs. Methods: A committee consisting of vascular neurologists, neuroradiologists and MRI technologists developed the protocol. The protocol was utilized at the CSC for one year, and subsequently expanded to the PSCs. A specific "WAKE-UP MRI”" order was created which consisted of only DWI, GRE and FLAIR sequences. MRI technologists were trained to ensure that the "WAKE-UP MRI” would take priority over other patients; eg: if there was no one in MRI, it would be held for the stroke patient, and if MRI was occupied, the stroke patient would be next. The acute stroke care was managed by an integrated telestroke system at the PSCs. All telestroke providers were educated on the protocol. Standard stroke time metrics were collected and analyzed over 2 years. Data from the CSC was compared with its 13 PSC spokes. A student’s t-test was used for data analysis. Results: A total of 190 patients who arrived with 4.5 hours of symptom discovery were screened; 62 at CSC and 128 at PSCs. Forty underwent emergent MRI at CSC and 29 at PSCs. Median door to MRI time was longer for patients presenting to PSC compared to CSC (78 vs 53.5 mins; p = 0.0002). Twenty-one vs 9 patients received tPA at CSC and PSC hospitals respectively. Median door to needle (DTN) times were longer for patients at PSC vs CSC (106 vs 67 mins; p = .018). The most common reasons for not administering IV-tPA was a negative MRI (40% at PSCs vs 47% at CSC) and a lack of DWI and FLAIR mismatch (40% at PSCs vs 42% at CSC). Conclusion: Use of emergent MRI in unwitnessed stroke for thrombolysis is feasible at PSCs. There remain barriers to implementation, with PSCs showing more delayed access to MRI and thrombolysis compared to CSC.
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