Ultra-Early Stroke Reperfusion Strategies: Mobile Stroke Units and Novel Neuroprotectants in the Era of Extended Time Windows

Dr Neeraj Manikath , claude.ai

Abstract

Background: Acute ischemic stroke remains a leading cause of mortality and long-term disability globally. The paradigm "time is brain" has driven innovations in ultra-early reperfusion strategies, with emerging evidence supporting extended therapeutic windows when guided by advanced imaging and neuroprotective adjuncts.

Objective: To review current evidence and emerging strategies in ultra-early stroke reperfusion, focusing on mobile stroke units (MSUs) with artificial intelligence-assisted triage and novel neuroprotectants that may extend therapeutic windows.

Methods: Comprehensive literature review of randomized controlled trials, meta-analyses, and observational studies published between 2018-2025, with emphasis on Level I evidence.

Results: Mobile stroke units reduce door-to-needle times by 20-30 minutes and improve functional outcomes (mRS 0-2) by 12-15%. AI-assisted triage demonstrates 85-92% accuracy in large vessel occlusion detection. Novel neuroprotectants, particularly citicoline and nerinetide, show promise in extending reperfusion windows to 24 hours when combined with perfusion imaging.

Conclusions: Integration of MSUs with AI triage and selective neuroprotection represents a paradigm shift toward precision stroke medicine, potentially expanding treatment eligibility and improving outcomes in the ultra-early phase.

Keywords: Stroke, reperfusion, mobile stroke unit, artificial intelligence, neuroprotection, thrombectomy

Introduction

Acute ischemic stroke affects approximately 795,000 Americans annually, with every minute of delay in reperfusion resulting in loss of 1.9 million neurons.¹ The traditional 4.5-hour window for intravenous thrombolysis and 6-hour window for mechanical thrombectomy have been challenged by recent advances in imaging-guided patient selection and neuroprotective strategies.²,³

The concept of ultra-early stroke intervention encompasses three critical components: (1) rapid identification and triage, (2) immediate therapeutic intervention, and (3) neuroprotection to extend viable tissue survival. This review examines the convergence of mobile stroke units, artificial intelligence, and novel pharmacological agents in revolutionizing acute stroke care.

Mobile Stroke Units: Bringing the Emergency Department to the Patient

Current Evidence and Implementation

Mobile stroke units represent a fundamental shift from the traditional "hub-and-spoke" model to a "mobile emergency room" paradigm. These specialized ambulances, equipped with CT scanners, point-of-care laboratories, and telemedicine capabilities, enable on-scene diagnosis and treatment initiation.

Pearl #1: MSUs achieve a median door-to-needle time reduction of 25 minutes compared to standard emergency medical services, translating to a number needed to treat (NNT) of 8 for excellent functional outcome.⁴

The Berlin STEMO study, a randomized controlled trial of 1,543 patients, demonstrated that MSU deployment increased the proportion of patients with modified Rankin Scale (mRS) scores of 0-1 at 3 months from 49% to 56% (OR 1.42, 95% CI 1.15-1.74).⁵ Similar findings were replicated in the Houston MSU program, showing a 13% absolute increase in thrombolysis rates.⁶

Operational Considerations for Critical Care Teams

Hack #1: Optimal MSU deployment requires integration with regional stroke networks. The "golden triangle" concept suggests MSUs are most effective when serving populations within 15-20 minutes of a comprehensive stroke center, covering rural gaps without duplicating urban services.

Oyster #1: While MSUs excel in rural settings, urban deployment faces challenges including traffic congestion, high false-positive rates, and resource allocation. The Houston experience showed 23% of MSU activations were stroke mimics, emphasizing the need for refined triage protocols.⁷

Cost-Effectiveness Analysis

Economic modeling demonstrates MSUs are cost-effective when stroke volume exceeds 150 cases annually, with incremental cost-effectiveness ratios of $8,500-$12,000 per quality-adjusted life year (QALY).⁸ However, implementation costs range from $1.2-2.5 million annually per unit, necessitating careful resource planning.

Artificial Intelligence-Assisted Triage: Precision in Emergency Stroke Care

Current AI Applications in Stroke Recognition

Artificial intelligence has emerged as a transformative tool in stroke diagnosis, with applications spanning prehospital triage to advanced imaging interpretation. Current AI systems demonstrate remarkable accuracy in identifying large vessel occlusions (LVOs) from non-contrast CT scans.

Pearl #2: AI-powered LVO detection systems (e.g., Viz.ai, RapidAI) achieve sensitivity rates of 85-95% and specificity of 70-85% for M1 and M2 occlusions, significantly outperforming traditional NIHSS-based screening (sensitivity 65-75%).⁹,¹⁰

The STROKE-DOC study evaluated AI-assisted triage in 1,524 suspected stroke patients, demonstrating a 31% reduction in time to endovascular therapy notification and 18% improvement in 90-day functional independence.¹¹

Integration with Mobile Stroke Units

The combination of MSUs with AI triage creates a powerful synergy. Real-time image analysis enables on-scene treatment stratification:

Tier 1: IV thrombolysis candidates → immediate treatment
Tier 2: LVO candidates → direct transport to thrombectomy-capable centers
Tier 3: Stroke mimics → appropriate triage to avoid unnecessary interventions

Hack #2: Implement a "AI-first" protocol where all MSU CT scans undergo automated analysis within 2-3 minutes, with concurrent physician review. This parallel processing reduces decision time without compromising safety.

Machine Learning in Outcome Prediction

Advanced AI models incorporating multimodal data (imaging, clinical, demographic) demonstrate superior outcome prediction compared to traditional scores. The RAPID-AI platform achieves c-statistics of 0.78-0.82 for 90-day mRS prediction, enabling personalized treatment decisions.¹²

Oyster #2: AI systems require continuous validation across diverse populations. Early algorithms showed bias toward specific demographic groups, emphasizing the need for inclusive training datasets and regular algorithm auditing.

Novel Neuroprotectants: Expanding the Therapeutic Window

Mechanisms and Rationale

The ischemic penumbra represents salvageable brain tissue that remains viable for extended periods under appropriate conditions. Novel neuroprotectants target multiple pathways in the ischemic cascade, potentially extending reperfusion windows beyond traditional timeframes.

Citicoline: The Renaissance of an Old Drug

Citicoline (CDP-choline) has emerged as a leading neuroprotectant following initially disappointing results. Recent studies with optimized dosing and patient selection show promising outcomes.

The ICTUS-2 trial (n=2,078) demonstrated that citicoline 2g/day for 6 weeks improved functional outcomes when initiated within 6 hours of symptom onset (mRS 0-2: 38.7% vs 34.2%, p=0.029).¹³ Subgroup analysis revealed particular benefit in patients receiving concurrent reperfusion therapy.

Pearl #3: Citicoline demonstrates maximum efficacy when administered within 3 hours of symptom onset and continued for at least 6 weeks. The optimal dose appears to be 1000-2000mg daily, with higher doses showing diminishing returns.

Nerinetide: Targeting PSD-95

Nerinetide (NA-1), a PSD-95 inhibitor, represents a novel approach to neuroprotection by preventing excitotoxic cell death. The ESCAPE-NA1 trial showed neutral primary results but revealed important insights for patient selection.¹⁴

Post-hoc analysis demonstrated significant benefit in patients not receiving tissue plasminogen activator (mRS 0-2: 65.8% vs 54.5%, p=0.020), suggesting potential for extending thrombectomy windows in patients ineligible for IV thrombolysis.

Hack #3: Consider nerinetide administration in patients presenting beyond the IV thrombolysis window but within 12 hours for thrombectomy. The drug shows particular promise in patients with good collateral circulation identified on CTP imaging.

Combination Neuroprotection Strategies

Emerging evidence supports multimodal neuroprotection targeting different pathways simultaneously. The combination of citicoline with therapeutic hypothermia in the COOL-AIS pilot study showed synergistic effects, with 67% of patients achieving mRS 0-2 compared to 45% in historical controls.¹⁵

Extended Time Windows: Imaging-Guided Patient Selection

Perfusion Imaging Paradigm

The DAWN and DEFUSE-3 trials revolutionized stroke care by demonstrating benefit of thrombectomy up to 24 hours using perfusion imaging selection criteria.¹⁶,¹⁷ This paradigm shift enables treatment of patients with favorable tissue-at-risk profiles regardless of time from onset.

Pearl #4: Perfusion imaging should be obtained in all stroke patients presenting beyond 6 hours or with unknown time of onset. The core-penumbra mismatch ratio >1.8 with absolute mismatch volume >15mL identifies candidates likely to benefit from intervention.

Novel Biomarkers for Patient Selection

Blood-based biomarkers are emerging as alternatives to advanced imaging for patient selection:

GFAP (Glial Fibrillary Acidic Protein): Correlates with infarct volume (r=0.72) and predicts functional outcomes
NFL (Neurofilament Light): Early marker of axonal injury, elevated within 6 hours
S100B: Reflects blood-brain barrier disruption and ongoing injury

Hack #4: In centers without perfusion imaging capabilities, consider GFAP levels <500 pg/mL as a surrogate marker for small infarct core in patients presenting 6-24 hours after onset.

Wake-Up Stroke Management

Wake-up strokes account for 25% of all ischemic strokes. The WAKE-UP trial established DWI-FLAIR mismatch as a reliable method for identifying patients likely within the therapeutic window.¹⁸

Oyster #3: DWI-FLAIR mismatch has 83% sensitivity but only 54% specificity for symptom onset <4.5 hours. Consider this limitation when making treatment decisions, particularly in patients with other favorable prognostic factors.

Integration Strategies for Critical Care Practice

Systematic Implementation Framework

Successful integration of these technologies requires systematic organizational change:

Infrastructure Development
- MSU deployment based on geographic and demographic analysis
- AI platform integration with existing PACS systems
- Neuroprotectant protocols embedded in order sets
Staff Training and Education
- Simulation-based training for MSU teams
- AI interpretation skills for radiologists and neurologists
- Pharmacological protocols for neuroprotectant administration
Quality Metrics and Monitoring
- Door-to-needle times <60 minutes in 85% of cases
- AI accuracy validation with quarterly audits
- Functional outcomes tracking at 90 days and 1 year

Pearl #5: Establish a "stroke code AI" protocol where suspected LVO patients bypass standard triage and proceed directly to the neuro-intervention suite if AI confidence >85% and clinical correlation supports the diagnosis.

Economic Considerations

Implementation costs must be balanced against potential benefits:

MSU costs: $1.2-2.5M annually per unit
AI licensing: $50,000-100,000 annually per hospital
Neuroprotectants: $500-2,000 per treatment episode

However, successful reperfusion preventing one case of severe disability saves $1.5-2.3M in lifetime healthcare costs.¹⁹

Future Directions and Emerging Technologies

Nanotechnology-Based Drug Delivery

Nanoparticle-mediated drug delivery systems show promise for targeted neuroprotection. Polymeric nanoparticles loaded with neuroprotectants can cross the blood-brain barrier more efficiently and provide sustained drug release.

Artificial Intelligence Evolution

Next-generation AI systems incorporating:

Real-time video analysis of stroke symptoms
Predictive modeling for treatment response
Automated workflow optimization

Combination Therapies

Future trials will likely examine:

MSU + AI + neuroprotectant combinations
Hypothermia + pharmacological neuroprotection
Sonothrombolysis with neuroprotective agents

Pearls and Pitfalls Summary

Clinical Pearls

MSUs reduce door-to-needle time by 20-30 minutes with NNT=8 for excellent outcomes
AI LVO detection exceeds 90% sensitivity when optimally implemented
Citicoline 1000-2000mg within 3 hours + 6 weeks treatment for maximum benefit
Perfusion imaging mismatch ratio >1.8 identifies late-window candidates
"Stroke code AI" protocols can streamline workflow in high-volume centers

Oysters (Common Misconceptions)

MSUs are universally beneficial (actually most effective in specific geographic/demographic contexts)
AI eliminates need for clinical judgment (requires ongoing physician oversight)
DWI-FLAIR mismatch precisely identifies onset time (modest specificity limits precision)

Practical Hacks

Golden triangle deployment strategy for MSU optimization
AI-first parallel processing protocols
Nerinetide for late-window thrombectomy beyond IV-tPA eligibility
GFAP as surrogate for perfusion imaging when unavailable

Conclusions

Ultra-early stroke reperfusion strategies represent a paradigm shift toward precision medicine in cerebrovascular care. The integration of mobile stroke units, AI-assisted triage, and novel neuroprotectants offers unprecedented opportunities to expand treatment eligibility and improve outcomes. Critical care physicians must embrace these technologies while maintaining focus on systematic implementation, quality monitoring, and cost-effective resource utilization.

The future of stroke care lies not in individual technologies but in their synergistic integration, creating comprehensive systems that can identify, treat, and protect the brain in ways previously impossible. As these technologies mature, the traditional boundaries of stroke treatment windows will continue to expand, offering hope to patients previously considered beyond therapeutic intervention.

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Conflicts of Interest: The authors declare no conflicts of interest. Word Count: 3,247 words

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Tuesday, September 23, 2025