Reeves, Mathew J.

Background: CT perfusion (CTP) imaging is an important tool for identifying candidates for mechanical thrombectomy (MT). This modality is increasingly available in regional facilities, but whether pre-transfer perfusion imaging studies may be relied upon for treatment decisions is unclear. We analyzed a cohort of stroke transfers to quantify changes in CTP imaging profiles over time. Methods: A cohort of confirmed ischemic stroke patients who received CTP before and after transfer to a large mid-western comprehensive stroke center (CSC) were identified. We compared perfusion mismatch (MM), core infarct volume (CIV), and favorability of imaging for mechanical thrombectomy (MT) candidacy (defined as the presence of LVO and MM/CIV ratio of >1.8) between studies. Regression models were used to examine predictors of CIV growth including hypoperfusion intensity ratio (HIR) and time between studies. Results: Over a 24-month period, 61 patients met inclusion criteria. Median age was 71 (IQR 60-80), 52% were female, median NIHSS was 14 (IQR 4-19), and most patients had occlusion of the internal carotid and/or middle cerebral arteries (77%). The median time from last known well (LKW) to initial CT was 148 minutes (IQR 71-620) and median time between imaging studies was 149 minutes (IQR 126-194). Median CIV growth was 0 mL (IQR 0-6) and 8% (5/61) had growth ≥25 mL. In linear regression, higher HIR predicted CIV growth (β=24.3, p=0.04), but time from LKW to presentation and time between imaging studies did not. Imaging studies taken at the regional and CSC sites demonstrated excellent agreement in classifying patients as candidates for MT (kappa 0.778 [95% CI: 0.611-0.944]). A favorable regional CTP profile was 93% sensitive for MT delivery at the CSC with a positive predictive value of 63%. Conclusion: In our cohort, CIV growth between facilities was minimal despite relatively long interfacility transport times and was predicted by the regional HIR. A favorable regional imaging profile demonstrated high agreement with the imaging result at the CSC and was strongly associated with MT treatment.

Background: Emergency medical services (EMS) routing of stroke patients is challenging in rural settings. To address this, the American Stroke Association (ASA) has developed a template severity-based EMS triage algorithm to direct EMS to either the nearest stroke-ready versus the nearest Comprehensive Stroke Center (CSC) hospital, but its real-world impact is uncertain. Methods: This is an interim analysis of a before-and-after quality improvement study examining the impact of implementation of a severity-based EMS stroke destination protocol by three medical control authorities (MCAs) covering 4 rural counties in western Michigan. We defined optimal destination according to the ASA algorithm, which recommends bypass for patients within 24 of last known well if large vessel occlusion suspected and bypass will not delay thrombolysis. Chi square tests were used to compare optimal destination selection before and after implementation among EMS suspected stroke cases originating from the target counties. We also compared the time from EMS scene arrival to ischemic stroke (IS) treatment (thrombolysis or endovascular therapy [EVT]) before and after implementation using Wilcoxon Rank Sum tests. Results: From November 2021 to May 2024, EMS transported 616 suspected stroke cases, 272 (44.2%) of which ultimately received a diagnosis of ischemic stroke (IS) or transient ischemic attack. The protocol was implemented by each MCA in random order at months 21, 23, and 29, resulting in 73 MCA-months before and 17 after implementation, excluding a one-month washout for each MCA. Optimal hospital destinations were selected for 310/499 (62.1%) patients prior to the protocol and 86/117 (73.5%) after (p=0.021). Thrombolytics were administered to 57 patients (48 before and 9 after) and 46 (42 before and 4 after) received EVT. Median times from first EMS contact to needle decreased from 98 (interquartile range [IQR]: 79-107) to 77 (IQR: 75-85) minutes (p=0.012); median times from EMS contact to groin puncture increased nonsignificantly from 165 (IQR: 132-216) to 196 (IQR: 191-228) minutes (p=0.110). Conclusions: Interim analysis of implementation of a rural EMS severity-based triage algorithm for suspected stroke cases resulted in improved destination selection and faster thrombolysis but did not improve EVT treatment times. This analysis will be repeated once follow-up is completed in 7 months.

Background: Large vessel occlusion (LVO) stroke patients are often transferred from regional hospitals to comprehensive stroke centers (CSC) for thrombectomy. The need for repeat imaging at CSCs prior to intervention is unclear. We compared regional hospital and CSC perfusion imaging results for interfacility transfers in a single health system. Methods: We analyzed a cohort of patients in western Michigan who received CT perfusion imaging before and after transfer to a CSC. Perfusion mismatch (MM), core infarct volume (CIV), and favorability of imaging for mechanical thrombectomy (MT) candidacy were compared between the regional and CSC studies. A favorable imaging profile was defined as the presence of LVO, MM volume >10 mL, and MM/CIV ratio of >1.2. Linear regression was used to examine predictors of infarct growth during transfer. Results: Over a 10-month period, 25 patients met inclusion criteria. The median age was 76 (IQR 66-81), 60% were male, median NIHSS was 11 (IQR 2-18), and most patients had occlusion of the internal carotid or middle cerebral arteries (72%). The median time from last known well to initial CT was 250 minutes (IQR 85-620). Regional median MM volume was 52 mL (IQR 8-97), CIV was 0 mL (IQR 0-13), and hypoperfusion intensity ratio (HIR) was 0.25 (IQR 0-0.34). The median time between CTs was 152 minutes (IQR 139-226). The median change in MM volume was -3 mL (IQR -27-3) and median CIV growth rate was 0 mL/hr (IQR 0-2.0). In a multivariable regression model, higher HIR (β=23.2, p=0.012) and minutes between imaging studies (β=0.10, p=0.021) were associated with CIV growth. Sixteen patients (64%) had favorable imaging profiles for MT at the regional hospital. Of these, 15 (93.8%) continued to have a favorable CSC imaging profiles and 9 (56.2%) underwent MT. Of the 9 patients without favorable regional imaging profiles, 1 (11.1%) had a favorable CSC imaging profile and 2 (22.2%) underwent MT. Conclusion: In our sample, regional and CSC perfusion imaging patterns were similar and patients infrequently crossed thresholds for MT candidacy between studies. Initial HIR and longer delays between were independently associated with infarct growth during transfers, however overall infarct growth was very small.

Background— We explored regional variation in 30-day ischemic stroke mortality and readmission rates and the extent to which regional differences in patients, hospitals, healthcare resources, and a quality of care composite care measure explain the observed variation. Methods and Results— This ecological analysis aggregated patient and hospital characteristics from the Get With The Guidelines–Stroke registry (2007–2011), healthcare resource data from the Dartmouth Atlas of Health Care (2006), and Medicare fee-for-service data on 30-day mortality and readmissions (2007–2011) to the hospital referral region (HRR) level. We used linear regression to estimate adjusted HRR-level 30-day outcomes, to identify HRR-level characteristics associated with 30-day outcomes, and to describe which characteristics explained variation in 30-day outcomes. The mean adjusted HRR-level 30-day mortality and readmission rates were 10.3% (SD=1.1%) and 13.1% (SD=1.1%), respectively; a modest, negative correlation ( r =−0.17; P =0.003) was found between one another. Demographics explained more variation in readmissions than mortality (25% versus 6%), but after accounting for demographics, comorbidities accounted for more variation in mortality compared with readmission rates (17% versus 7%). The combination of hospital characteristics and healthcare resources explained 11% and 16% of the variance in mortality and readmission rates, beyond patient characteristics. Most of the regional variation in mortality (65%) and readmission (50%) rates remained unexplained. Conclusions— Thirty-day mortality and readmission rates vary substantially across HRRs and exhibit an inverse relationship. While regional variation in 30-day outcomes were explained by patient and hospital factors differently, much of the regional variation in both outcomes remains unexplained.