Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia

Matthew Miller; Louisa Jorm; Chris Partyka; Brian Burns; Karel Habig; Carissa Oh; Sam Immens; Neil Ballard; Blanca Gallego

doi:10.1016/j.injury.2024.111570

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia

Injury. 2024 Apr 17:111570. doi: 10.1016/j.injury.2024.111570. Online ahead of print.

Authors

Matthew Miller¹, Louisa Jorm², Chris Partyka³, Brian Burns⁴, Karel Habig⁵, Carissa Oh⁶, Sam Immens⁵, Neil Ballard⁷, Blanca Gallego⁸

Affiliations

¹ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Anesthesia, St George Hospital, Kogarah, NSW 2217 Australia; Centre for Big Data Research in Health at UNSW Sydney, Kensington, NSW 2052, Australia. Electronic address: m.r.miller@unsw.edu.au.
² Foundation Director of the Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia.
³ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, Royal North Shore Hospital, St Leonards, NSW 2065, Australia.
⁴ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Royal North Shore Hospital, St Leonards, NSW 2065, Australia; Faculty of Medicine & Health, University of Sydney, Camperdown, NSW 2050, Australia.
⁵ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia.
⁶ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, St George Hospital, Kogarah, NSW 2217 Australia.
⁷ Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Paediatric Emergency Medicine, Sydney Children's Hospital, Randwick, NSW 2031, Australia; Department of Emergency Medicine, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia.
⁸ Clinical analytics and machine learning unit, Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia.

PMID: 38664086
DOI: 10.1016/j.injury.2024.111570

Abstract

Background: Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records.

Methods: We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %).

Conclusion: The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Keywords: Ambulances; Cohort studies; Datasets; Electronic; Emergency medical services; Health records; International classification of diseases; Outcome assessment (health care); Patient care; Prehospital care; Trauma.