Va-IRI™ — Vaccination Immune Readiness Index

Executive Summary

Patients with Long COVID, ME/CFS, MCAS, and dysautonomia face elevated risks when vaccines are administered without regard for immune terrain stability. Despite mounting evidence of post-vaccination syndromes, no framework exists to quantify readiness before dosing.

The Va-IRI™ (Vaccination Immune Readiness Index) provides a structured 0–100 readiness score derived from common labs, symptom baselines, and patient function. Built on the STAIR Stable Method™ (Stabilization, Tolerance, And Immune Readiness), Va-IRI™ adapts low-and-slow principles pioneered by patients and refined through collaboration with leading researchers.

This framework builds directly on two major multi-institutional studies co-authored by Cynthia Adinig with Yale, Mount Sinai, and Harlan Krumholz , one characterizing Long COVID (Adinig et al., 2023) and one mapping post-vaccination immune signatures (Adinig et al., 2025). Together, these works provide the biologic foundation for readiness screening and terrain-based vaccination index.

Background and Rationale

• Long COVID and ME/CFS are associated with immune exhaustion and cytokine dysregulation (Komaroff & Lipkin, 2021; Eaton-Fitch et al., 2024).

• Severe COVID can leave lasting CD8+ impairment and altered cytokine balance (Vázquez-Alejo et al., 2023; Wiech et al., 2022).

• Mast cell activation and dysautonomia often complicate tolerability of immune interventions (Afrin et al., 2021; Nicola et al., 2024).

• The Yale preprint, co-authored with Akiko Iwasaki, David Putrino, Harlan Krumholz, and others, documented immune alterations in post-vaccination chronic illness (Adinig et al., 2025).

• Earlier collaborative work co-authored with Yale, Mount Sinai, and Harlan Krumholz described the clinical and biological characteristics of Long COVID at scale (Adinig et al., 2023).

Together, these studies highlight why a readiness framework like Va-IRI™ is necessary to guide vaccination safety.

The Problem

• Immune-fragile groups are excluded from trials, leaving safety gaps (Su et al., 2022; Peluso & Deeks, 2022).

• Patients report post-vaccination adverse events mirroring long COVID flares, yet no readiness frameworks are in use (Halma et al., 2025).

• Clinicians lack standardized tools to measure readiness, relying instead on guesswork, which erodes trust.

The Solution: Va-IRI™

Gatekeeper Principle

Vaccination should never proceed during active infection. If PCR/antigen testing, CBC, or symptoms suggest infection, readiness score defaults to Not Ready regardless of other metrics.

Scoring Domains (0–100 total)

1. Infection Clearance (0–20)

• PCR/antigen negative, no residual infection symptoms = higher score.

• CBC stable without lymphopenia.

• Active infection = automatic 0.

2. T-Cell Exhaustion / Function (0–20)

• CBC lymphocyte trends.

• Flow cytometry where available (CD4/CD8 ratios, PD-1, TIM-3).

• Cytokine balance: higher IL-2/IFN-γ vs. lower IL-6/TNF-α = higher score.

• Supported by immune exhaustion findings in ME/CFS and long COVID (Eaton-Fitch et al., 2024; Adinig et al., 2025).

3. Inflammation Terrain (0–15)

• hsCRP, ferritin, ESR, IL-6, TNF-α.

• Quiet, near-normal trends = higher readiness.

• Persistently high inflammatory markers = defer (Peluso & Deeks, 2022).

4. Clotting Terrain (0–15)

• D-dimer, fibrinogen, platelet count; optional TEG/ROTEM.

• Normalized markers = higher readiness.

• Elevated clotting risk = defer (Su et al., 2022).

5. Antibody Landscape (0–10)

• Amerimmune or equivalent panel: spike vs. nucleocapsid antibodies.

• Balanced plateaued titers = higher readiness.

• Chaotic or persistent spike antigen = lower score (Adinig et al., 2025).

6. Functional Baseline (0–20)

• PEM logs, sleep stability, med/supplement tolerance.

• Wearable data: resting HR, HRV, overnight O₂.

• Stable tolerance and function = higher readiness (Komaroff & Lipkin, 2021).

Zones

• Red (0–40): Not Ready → defer vaccination.

• Yellow (41–70): Borderline → proceed only with safeguards such as the STAIR sandwich (pre-support, micro-dosing, post-support).

• Green (71–100): Ready → functionally stable baseline with safeguards as needed.

Implementation Pathways

• Clinicians: Order common labs and compute readiness scores. Use safeguards (e.g., STAIR sandwich) for borderline cases.

• Researchers: Stratify trial participants by readiness band to improve safety and external validity.

• Policymakers: Incorporate readiness thresholds into guidance for vaccination in vulnerable populations.

• Patients: Advocate for readiness testing before boosters or new therapies.

Conclusion

The Va-IRI™ reframes vaccination as a terrain-readiness decision, not a calendar decision. By integrating immune exhaustion data from ME/CFS and long COVID (Komaroff & Lipkin, 2021; Eaton-Fitch et al., 2024; Vázquez-Alejo et al., 2023) with immune mapping of post-vaccination syndromes (Adinig et al., 2025) and long COVID characteristics (Adinig et al., 2023), Va-IRI™ becomes the first actionable scoring system for safe vaccination in immune-fragile populations.

References

1. Bhattacharjee B, Lu P, Silva Monteiro V, Tabachnikova A, Wang K, Hooper WB, Bastos V, Greene K, Sawano M, Guirgis C, Tzeng TJ, Warner F, Baevova P, Kamath K, Reifert J, Hertz D, Dressen B, Tabacof L, Wood J, Cooke L, Doerstling M, Nolasco S, Ahmed A, Adinig C, Proal A, Putrino D, Guan L, Krumholz HM, Iwasaki A. Immunological and antigenic signatures associated with chronic illnesses after COVID-19 vaccination. medRxiv. 2025 Feb 18.

2. Sawano M, Adinig C, Tabacof L, Wood J, Putrino D, Iwasaki A, Krumholz HM, et al. Clinical and biological characteristics of Long COVID: patient-centered insights from multi-institutional collaboration. medRxiv. 2023.

3. Komaroff AL, Lipkin WI. Insights from studies of ME/CFS and Long COVID. Nat Rev Immunol. 2021;21(12):731-732.

4. Eaton-Fitch N, Johnston SC, Staines DR, Marshall-Gradisnik S. A systematic review of immune exhaustion in ME/CFS and Long COVID. JCI Insight. 2024;9(4):e183810.

5. Wiech M, Chroscicki P, Swatler J, et al. Remodeling of T cell subsets in COVID-19 convalescent individuals with persistent symptoms. Front Immunol. 2022;13:886431.

6. Vázquez-Alejo E, Rueda CM, Velilla PA. Severe COVID-19 induces long-lasting functional impairment of CD8+ T cells. J Clin Invest. 2023;133(10):e167804.

7. Afrin LB, Weinstock LB, Molderings GJ. Covid-19 hyperinflammation and post-COVID illness may be rooted in mast cell activation syndrome. Int J Infect Dis. 2021;100:327-332.

8. Nicola S, et al. COVID-19 vaccine tolerability in mast cell activation syndrome patients: a prospective cohort study. Front Allergy. 2024;4:10893122.

9. Su Y, Yuan D, Chen DG, et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell. 2022;185(5):881-895.e20.

10. Peluso MJ, Deeks SG. Early clues regarding the pathogenesis of long COVID. Trends Immunol. 2022;43(4):268-270.

11. Halma M, Bouma J, van den Berg J, et al. Post-vaccination syndrome (PVS): emerging clinical patterns after COVID-19 vaccines. Heliyon. 2025;11(5):e1864.ganization.

Author Contribution Note

The Va-IRI™ framework was developed by Cynthia Adinig (CYNAERA), who also co-authored two multi-institutional collaborations with Yale, Mount Sinai, and Harlan Krumholz. In addition to published research, Adinig served as a patient advisor at Mount Sinai, where she introduced environmental auditing and micro-testing practices to clinicians including Dr. David Putrino and Dr. Amy Proal. These methods, identifying hidden allergic profiles, testing medications in micro-doses, and re-examining overlooked allergic histories in EHRs — later informed the stabilization logic of the STAIR Stable Method™, which was subsequently embedded into Va-IRI™.

Author’s Note:

All insights, frameworks, and recommendations in this white paper reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models developed through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs).

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

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Learn More: https://www.cynaera.com/systems

About the Author 

Cynthia Adinig is an internationally recognized systems strategist, health policy advisor, and the founder of CYNAERA, an AI-powered intelligence platform advancing diagnostic reform, clinical trial simulation, and real-world modeling for infection-associated chronic conditions (IACCs). She has developed 400+ Core AI Frameworks, 1 Billion + Dynamic AI Modules. including the IACC Progression Continuum™, US-CCUC™, and RAEMI™, which reveal hidden prevalence, map disease pathways, and close gaps in access to early diagnosis and treatment.

Her clinical trial simulator, powered by over 675 million synthesized individual profiles, offers unmatched modeling of intervention outcomes for researchers and clinicians.

Cynthia has served as a trusted advisor to the U.S. Department of Health and Human Services, collaborated with experts at Yale and Mount Sinai, and influenced multiple pieces of federal legislation related to Long COVID and chronic illness. 

She has been featured in TIME, Bloomberg, USA Today, and other leading publications. Through CYNAERA, she develops modular AI platforms that operate across 32+ sectors and 180+ countries, with a local commitment to resilience in the Northern Virginia and Washington, D.C. region.