Geospatial Analysis for Prehospital Extracorporeal Cardiopulmonary Resuscitation in Houston, Texas.

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.

Prehospital Ultrasound: Nationwide Incidence from the NEMSIS Database.

OBJECTIVE: We sought to describe prehospital ultrasound (PHUS) use and trends in PHUS utilization over time using a national database. METHODS: Using the 2018 - 2021 National Emergency Medical Services Information System databases, we identified those EMS activations where PHUS was performed. We evaluated the association between year and number of PHUS exams performed using univariable and multivariable regression analysis. Analysis was performed on the overall group and various subgroups. RESULTS: In total, there were 148,709,000 EMS activations by 13,899 agencies over the 4 years. Of these, 3,291 unique activations (0.002%) involved PHUS, performed by 71 EMS agencies (0.5%). The annual rate of ultrasound evaluations per 1 million EMS activations significantly increased over the study period: 5.2 in 2018, 14.8 in 2019, 18.6 in 2020, and 38.9 in 2021 (p < 0.01). The number of agencies performing PHUS each year increased over the study period from 11 in 2018 to 54 in 2021 (p < 0.05). Each year after 2018 had an increased odds of PHUS use demonstrated with logistic regression (p < 0.01). PHUS was used in each US census region, and paramedics performed most of the PHUS exams (75.5%). We identified 1,060 out-of-hospital cardiac arrest, 820 trauma, and 427 respiratory PHUS cases. These three cohorts accounted for 70.1% of all PHUS cases. CONCLUSION: Prehospital ultrasound use in the United States increased significantly over the study period, but remains exceedingly rare. The performance of PHUS was recorded throughout the United States, with paramedics performing the majority of PHUS studies included in this database.

Impact of Receiving Hospital on Out-of-Hospital Cardiac Arrest Outcome: Racial and Ethnic Disparities in Texas.

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.