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Geospatial Analysis for Prehospital Extracorporeal Cardiopulmonary Resuscitation in Houston, Texas.
Huebinger, Ryan; Hunyadi, Jocelyn V; Zhang, Kehe; Shekhar, Aditya C; Bauer, Cici X; Bakunas, Carrie; Waller-Delarosa, John; Schulz, Kevin; Persse, David; Witkov, Richard.
Afiliação
  • Huebinger R; Department of Emergency Medicine, University of New Mexico, Albuquerque, NM.
  • Hunyadi JV; Texas Emergency Medicine Research Center, McGovern Medical School, Houston, TX.
  • Zhang K; Department of Biostatistics and Data Science, University of Texas Health Science Center in Houston, School of Public Health, Houston, TX.
  • Shekhar AC; Center of Spatial-Temporal Modeling of Applications in Population Sciences, University of Texas Health Science Center in Houston, School of Public Health, Houston, TX.
  • Bauer CX; Department of Biostatistics and Data Science, University of Texas Health Science Center in Houston, School of Public Health, Houston, TX.
  • Bakunas C; Icahn School of Medicine at Mount Sinai, New York City, NY.
  • Waller-Delarosa J; Department of Biostatistics and Data Science, University of Texas Health Science Center in Houston, School of Public Health, Houston, TX.
  • Schulz K; Center of Spatial-Temporal Modeling of Applications in Population Sciences, University of Texas Health Science Center in Houston, School of Public Health, Houston, TX.
  • Persse D; Department of Emergency Medicine, University of Texas Health Science Center in Houston, Houston, TX.
  • Witkov R; Texas Emergency Medicine Research Center, McGovern Medical School, Houston, TX.
Prehosp Emerg Care ; : 1-9, 2024 Aug 27.
Article em En | MEDLINE | ID: mdl-39190864
ABSTRACT

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 2013-2021. 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 500 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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article