Recognition and Early Assessment of Acute Stroke Using the FAST and BE FAST Approach-An Updated Review.

Q: What is the clinical evidence from this study?

Study design: Systematic Review. Population: Acute stroke. Intervention: Stroke recognition tools (FAST, BE-FAST, CPSS, ROSIER, PreHAST, FAST-ED, AI models) and referral strategies (dispatcher triage, mobile stroke units) vs. Conventional assessment, neuroimaging reference standards (CT, MRI, CTA), or standard care. Primary outcome: Diagnostic accuracy (sensitivity and specificity) and treatment timeliness.

Q: What does this research mean for the field?

Structured prehospital stroke recognition tools and coordinated referral pathways improve early stroke identification, shorten referral delays, and increase eligibility for reperfusion therapy. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

HMOOD HEGE Al DHMSHY; Zidan Khalaf Aqeel Alanezi; Lazzam Mubarak Al-Anazi; Basim Mousa Alhumaidi Alhazmi; BADER  MALAH ALI ALANAZI; Sami Jumah K Alhazmi; Mohammed Mrih M Alenezi; ABDULAZIZ BASHEER SHAMI ALANAZI; Nawaf W. Alruwaili; Ayed Salhoub A Alswilmi; Ismail Rabie Mukhlif Al-Hazmi; Ishaq Rabie Mukhlif Al-Hazmi; Basim Mousa Alhumaidi Alhazmi; Alhazmi YSIF SALEH

doi:10.64483/202631819

November 7, 2026Saudi Journal of Medicine and Public HealthOpen Access

Recognition and Early Assessment of Acute Stroke Using the FAST and BE FAST Approach-An Updated Review.

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Key result

Mobile stroke units cut time to imaging by ~13 minutes, increasing reperfusion eligibility.

Why the study?

Acute stroke requires rapid recognition and referral to improve access to time-dependent treatments, yet delays in symptom identification, emergency response, and hospital referral remain major challenges across healthcare systems.

Do structured stroke recognition tools and coordinated referral strategies improve diagnostic accuracy, treatment timeliness, and patient outcomes in patients with suspected acute stroke?

Key Points

To evaluate the effectiveness of clinical stroke recognition tools and emergency referral strategies, assessing their influence on treatment timeliness and patient outcomes.
Systematic review of 33 studies published between 2003 and 2025 evaluating conventional stroke screening scales, dispatcher triage, emergency medical services, mobile stroke units, and artificial intelligence.
Conducted a narrative evidence synthesis due to wide heterogeneity in study designs, populations, diagnostic metrics, and reported clinical outcomes.
Conventional screening instruments typically demonstrated sensitivity above 80%, including FAST (sensitivity 86%–94.8%, specificity 55.1%–60%), CPSS (sensitivity 95.0%, specificity 54.3%), and ROSIER (sensitivity 95.0%, specificity 60.1%).
Specialized tools targeting large vessel occlusion showed high sensitivity, with FAST-ED reaching 88.0% sensitivity and 57.5% specificity at cutoff ≥2, and PreHAST reaching 93.2% sensitivity and 46.5% specificity at cutoff ≥1.
Dispatcher-based systems cut referral delays by 12–20 minutes, while mobile stroke units reduced time to neuroimaging by 8–17 minutes and increased reperfusion eligibility by 7–12%.

Study Design

Type

Systematic Review

Multicenter

Yes

Structured PICO

Do structured stroke recognition tools and coordinated referral strategies improve diagnostic accuracy, treatment timeliness, and patient outcomes in patients with suspected acute stroke?

P

Population

Patients with suspected acute stroke across 33 studies (published 2003-2025), predominantly older adults with mean ages ranging from 64 to 78 years, evaluated in prehospital and emergency settings.

I

Intervention

Stroke recognition tools and referral strategies, including conventional stroke recognition scales (FAST, BE-FAST, CPSS, ROSIER, NIHSS, PreHAST, FAST-ED, LAPSS, Med PACS, OPSS, MASS), dispatcher-based triage systems, emergency medical services (EMS) interventions, mobile stroke units, and artificial intelligence (AI)/machine-learning approaches (DeepStroke, DARE-PACE, PPG-based models, ChatGPT-4V)

C

Comparator

Reference diagnostic standards including computed tomography (CT), magnetic resonance imaging (MRI), CT angiography (CTA), and clinician assessment, as well as comparisons across different screening instruments and standard care pathways

O

Outcome

Diagnostic accuracy (sensitivity and specificity) of stroke recognition tools, treatment timeliness (referral delays, onset-to-door times, time to imaging), and patient outcomes (reperfusion eligibility, treatment access, functional independence)

Structured stroke recognition scales (like FAST, CPSS, and ROSIER) combined with coordinated EMS referral pathways and mobile stroke units significantly reduce prehospital delays and increase eligibility for time-dependent reperfusion therapies in acute stroke.

Limitations

Heterogeneity in study designs, patient populations, diagnostic accuracy measures, and reported outcomes precluded quantitative meta-analysis
Conventional screening tools such as FAST have moderate to low specificity (55-60%), leading to false-positive referrals from stroke mimics
Negative FAST screening does not completely exclude stroke, particularly in patients with atypical or posterior circulation symptoms
Emerging AI-based diagnostic models rely heavily on retrospective data and require prospective external validation in real-time emergency settings
Rural and resource-limited settings continue to face structural delays related to transportation, imaging availability, and EMS capacity
Heterogeneity in study designs, populations, diagnostic accuracy measures, and reported outcomes precluded quantitative synthesis
Studies used different reference standards (MRI, CT, CTA, clinician assessment) and diagnostic cutoffs, limiting direct comparisons
AI-based approaches require further development and external validation across different healthcare settings

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Implication

Supports integration of validated scales and mobile stroke units into EMS protocols; extends prior observational data by quantifying time savings and eligibility gains.

Abstract

Background: Acute stroke requires rapid recognition and referral to improve access to time-dependent treatments and reduce mortality and long-term disability. Delays in symptom identification, emergency response, and hospital referral remain major challenges across healthcare systems. Aim: This systematic review aimed to evaluate the effectiveness of stroke recognition tools and referral strategies in primary and emergency care and examine their effects on treatment timeliness and patient outcomes. Methods: A systematic review was conducted of 33 studies published between 2003 and 2025. The included studies investigated conventional stroke recognition scales, dispatcher-based triage systems, emergency medical services interventions, mobile stroke units, and artificial intelligence-based approaches. Evidence was synthesized narratively because of heterogeneity in study designs, populations, diagnostic accuracy measures, and reported outcomes. Results: FAST, CPSS, ROSIER, and NIHSS were frequently evaluated, with conventional screening tools generally demonstrating sensitivity above 80%, although specificity varied. PreHAST and FAST-ED showed potential for identifying severe stroke and large vessel occlusion. Dispatcher-based systems improved emergency prioritization and reduced referral delays by approximately 12–20 minutes. Mobile stroke units reduced time to imaging by 8–17 minutes and increased reperfusion eligibility by 7–12%. Improved recognition and referral were associated with greater treatment access and functional independence. However, rural and resource-limited settings continued to experience delays related to transportation, imaging availability, and EMS capacity. AI-based approaches showed promising diagnostic performance but require further validation. Conclusion: Structured recognition tools and coordinated referral pathways improve early stroke management. Their effectiveness depends on appropriate implementation, healthcare infrastructure, and equitable access to emergency services.

Discussion

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Cite This Study

DHMSHY et al. (2026) conducted a systematic review in Acute stroke. Stroke recognition tools (FAST, BE-FAST, CPSS, ROSIER, PreHAST, FAST-ED, AI models) and referral strategies (dispatcher triage, mobile stroke units) vs. Conventional assessment, neuroimaging reference standards (CT, MRI, CTA), or standard care was evaluated on Diagnostic accuracy (sensitivity and specificity) and treatment timeliness. Conventional stroke screening tools generally demonstrated sensitivity >80%, while mobile stroke units reduced time to imaging by 8-17 minutes and increased reperfusion eligibility by 7-12%.

synapsesocial.com/papers/6af1d893f9a8addd138b1cbc https://doi.org/10.64483/202631819

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Recognition and Early Assessment of Acute Stroke Using the FAST and BE FAST Approach-An Updated Review.

Key Points

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Recognition and Early Assessment of Acute Stroke Using the FAST and BE FAST Approach-An Updated Review.

Key Points

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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