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Competing Interests: Disclosure: I.S.d.A. has research-related financial interests in Abiomed. The other authors have no conflicts of interest to report.

Objectives: Extracorporeal cardiopulmonary resuscitation (eCPR) is a promising treatment that could improve survival for refractory out-of-hospital (OHCA) patients. Healthcare systems may choose to start eCPR in the prehospital setting to optimize time to eCPR initiation and decrease low-flow time. We used geospatial modeling to evaluate different eCPR catchment strategies for a forthcoming prehospital eCPR program in Houston, Texas., Methods: We studied OHCAs treated by the Houston Fire Department from 2013 to 2021. We included OHCA patients aged 18-65 years old with an initial shockable rhythm that did not have prehospital return of spontaneous circulation (ROSC). Based on the geolocation that each OHCA occurred, we used geospatial modeling to identify eCPR candidates using four mapping strategies based on distance/drive time from the eCPR center: 1) 15-minute drive time, 20-minute drive time, 10-mile drive distance, and 15-mile drive distance., Results: Of 18,501 OHCAs during the study period, 881 met the eCPR inclusion criteria. Compared to non-eCPR candidates, eCPR candidates were younger (median age 52.3 years vs 62.7 years, p  < 0.01) and had a higher proportion of males (76.6% v 59.8%, p  < 0.01). Of eCPR candidate OHCAs, OHCAs occurred more frequently during the weekdays and the daytime, with 5:00 PM being the most common time. Using geospatial modeling and based on drive time, 219 OHCAs (24.9% of 881) were within a 15-minute drive, and 454 (51.5%) were within a 20-minute drive. Using drive distance, 383 eCPR candidates (43.5%) were within 10 miles, and 703 (79.8%) were within 15 miles., Conclusions: Using geospatial modeling, we demonstrated a process to estimate potential eCPR patient volumes for a geographic region. Geospatial modeling represents a viable strategy for healthcare systems to delineate eCPR catchment areas.

Background Factors associated with out-of-hospital cardiac arrest (OHCA) outcome disparities remain poorly understood. We evaluated the role of receiving hospital on OHCA outcome disparities. Methods and Results We studied people with OHCA who survived to hospital admission from TX-CARES (Texas Cardiac Arrest Registry to Enhance Survival), 2014 to 2021. Using census data, we stratified OHCAs into majority (>50%) strata: non-Hispanic White race and ethnicity, non-Hispanic Black race and ethnicity, and Hispanic or Latino ethnicity. We stratified hospitals into performance quartiles based on the primary outcome, survival with good neurologic outcome. We evaluated the association between race and ethnicity and care at higher-performance hospitals. We compared 3 models evaluating the association between race and ethnicity and outcome: (1) ignoring hospital, (2) adjusting for hospital as a random intercept, and (3) adjusting for hospital performance quartile. We adjusted models for possible confounders. We included 10 434 OHCAs. Hospital performance quartile outcome rates ranged from 11.3% (fourth) to 37.1% (first). Compared with OHCAs in neighborhoods of majority White race, those in neighborhoods of majority Black race (odds ratio [OR], 0.1 [95% CI, 0.1-0.1]) and Hispanic or Latino ethnicity (OR, 0.2 [95% CI, 0.2-0.2]) were less likely to be cared for at higher-performing hospitals. Compared with White neighborhoods (30.1%) and ignoring hospital, outcomes were worse in Black neighborhoods (15.4%; adjusted OR [aOR], 0.5 [95% CI, 0.4-0.5]) and Hispanic or Latino neighborhoods (19.2%; aOR, 0.6 [95% CI, 0.5-0.7]). Adjusting for hospital as a random intercept, outcomes improved for Black neighborhoods (aOR, 0.9 [95% CI, 0.7-1.05]) and Hispanic or Latino neighborhoods (aOR, 0.9 [95% CI, 0.8-0.99]). Adjusting for hospital performance quartile, outcomes improved for Black neighborhoods (aOR, 0.8 [95% CI, 0.7-1.01]) and Hispanic or Latino neighborhoods (aOR, 0.9 [95% CI, 0.8-0.996]). Conclusions In Black and Hispanic or Latino communities, OHCAs were less likely to be cared for at higher-performing hospitals, and adjusting for receiving hospital improved OHCA outcome disparities.

Introduction: Post-arrest care is essential to the chain of survival after out-of-hospital cardiac arrest (OHCA). Sparse literature evaluates disparities in post-arrest care. We sought to measure post-arrest care disparities using a statewide OHCA registry., Methods: We evaluated 2014-2020 data in the Texas Cardiac Arrest Registry to Enhance Survival (TX-CARES) and included adult OHCAs surviving to hospital admission. We stratified subjects by race/ethnicity. Outcomes were targeted temperature management (TTM), percutaneous intervention (PCI), early withdrawal of life-sustaining therapies (WLST), survival to discharge, and survival with cerebral performance category (CPC) of 1-2 (considered favorable). We used both multivariable and mixed-effects, logistic regression models to evaluate the association between race/ethnicity and outcomes, adjusting for confounders. We modeled receiving hospital as a random intercept for the mixed-models analysis., Results: We included 8,363 OHCAs; 3,916 White, 2,251 Black, 2,196 Hispanic/Latino. On multivariable analysis, Black patients had a lower PCI (aOR 0.4, 95% CI 0.3-0.5) and survival with good CPC (aOR 0.6, 95% CI 0.6-0.7). Hispanic/Latino patients had lower TTM (aOR 0.8, 95% CI 0.7-0.9), PCI (aOR 0.6, 95% CI 0.5-0.8), survival (aOR 0.8, 95% CI 0.7-0.9), and survival with good CPC (aOR 0.7, 95% CI 0.6-0.7). However, after adjusting for clustering by receiving hospital, most of the post-arrest care relationships were negated, and Black patients actually had a higher rate of TTM (aOR 1.2, 95% CI 1.1-1.3)., Conclusions: Minority OHCA victims experienced disparities in post-arrest care and outcomes. However, adjusting for receiving hospital random-effect largely diminished these findings. Inter-hospital, post-arrest care disparities may exist., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: Large variation exists for out-of-hospital-cardiac-arrest (OHCA) prehospital care, but less is known about variations in post-arrest care. We sought to evaluate variation in post-arrest care in Texas as well as factors associated with higher performing hospitals., Methods: We analyzed data in Texas Cardiac Arrest Registry to Enhance Survival (TX-CARES), including all adult, non-traumatic OHCAs from 1/1/2014 through 12/31/ 2020 that survived to hospital admission. We first evaluated variability in provisions of post-arrest care and outcomes. We then stratified hospitals into quartiles based on their rate of survival and evaluated the association between improving quartiles and care. Lastly, we evaluated for outliers in post-arrest care and outcomes using a mixed-effect regression model., Results: We analyzed 7,842 OHCAs admitted to 146 hospitals. We identified large variations in post-arrest care, including targeted temperature management (TTM) (IQR 7.0-51.1%), left heart catheterization (LHC) (IQ 0-25%), and percutaneous coronary intervention (PCI) (IQR 0-10.3%). Higher performing hospital quartiles were associated with higher rates of TTM (aOR 1.42, 95% CI 1.36-1.49), LHC (aOR 2.07, 95% CI 1.92-2.23), and PCI (aOR 2.02, 95% CI 1.81-2.25); but lower rates of bystander CPR (aOR 0.90, 95% CI 0.87-0.94). We identified numerous performance outlier hospitals; 39 for TTM, 34 for PCI, 9 for survival to discharge, and 24 for survival with good neurologic function., Conclusions: Post-arrest care varied widely across Texas hospitals. Hospitals with higher rates of survival to discharge had increased rates of TTM, LHC, and PCI but not bystander CPR., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)