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Prehospital emergency care

September 29, 2026 Ā· via Europe PMC

The cost-effectiveness and health equity of prehospital treatment strategies for out-of-hospital cardiac arrest patients: a systematic review.

Jang K, Morton RL, Dennis M, Nikolovski J, Zhan X, Ke Y, Burns B, Tran AD.

ReviewAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Most intensified prehospital OHCA strategies were cost-effective, but equity reporting was weak.

What was studied

Systematic review of 10 economic evaluations.

Key findings

  • Early defibrillation and ALS by trained EMTs were cost-effective
  • Prehospital ECPR was more cost-effective than in-hospital ECPR
  • TOR strategies were cost-effective; equity reporting limited

Clinical relevance

Informs EMS investment.

Who should care

Emergency Medicine

Limitations

Heterogeneous comparators.

Support cost-effective strategies and equity reporting.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Objectives

Out-of-hospital cardiac arrest (OHCA) remains a major public health challenge, associated with substantial mortality and significant costs for health care system. This systematic review aimed to (i) synthesise economic evaluations of prehospital treatment strategies for OHCA from ambulance attendance through to hospital discharge; (ii) assess the methodological quality and reporting of the included studies; and (iii) evaluate the reporting of health equity-relevant data.

Methods

We searched Medline, Embase, Scopus, EconLit, and CINAHL databases for records from database inception to 12 June 2026. The QHES and CHEERS checklists were used to assess the quality and reporting standards of included studies, and the review was reported according to the PRISMA checklist. The completeness of reporting health equity outcomes was reviewed using the PROGRESS (Place of residence, Race, Occupation, Gender, Religion, Education, Socioeconomic status and Social capital) - Plus framework.

Results

Ten economic evaluations met eligibility criteria. Most studies were cost-utility analyses (n = 8, 80%) and used Markov models (n = 6, 60%). Most evaluations were 'very good quality' ( ≄80%) (n = 6, 60%). Early defibrillation and advanced life support (ALS) delivered by trained emergency medical technicians were cost-effective compared with no intervention, emergency physician-led ALS, standard emergency medical services or paramedic care (ICER range from Ā£11,407 to USD $56,700 per QALY gained). Prehospital extracorporeal cardiopulmonary resuscitation (ECPR) was more cost-effective than in-hospital ECPR strategies (ICER range from AUD $27,323 to $44,000 per QALY gained). Termination-of-resuscitation (TOR) strategies were cost-effective compared with no TOR or other TOR strategies, including basic life support and ALS TOR, with estimates of $110,554 saved per QALY lost and USD $23,851 per QALY gained.Survival rate and neurological outcomes influenced cost-effectiveness results. Equity reporting was limited: almost all included studies addressed less than 40% of the PROGRESS-Plus equity criteria.

Conclusions

Most strategies that increased prehospital treatment intensity, avoided futile transport, or enabled earlier advanced care were cost-effective; however, the certainty of evidence, particularly for sub-groups, was limited by heterogeneous comparators and sparse equity reporting.

DOI

10.1080/10903127.2026.2741269

PMID

42809516

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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