Va-IRI™ — Vaccination Immune Readiness Index

Executive Summary

Patients living with Long COVID, ME/CFS, MCAS, dysautonomia, and related infection-associated chronic conditions may face elevated risk when vaccination occurs during periods of immune instability. Although post-vaccination chronic illness and prolonged flare syndromes are increasingly documented in the scientific literature, clinical systems still lack a standardized method to assess physiologic readiness before immune stimulation.

Va-IRI™ (Vaccination Immune Readiness Index) was developed to address this gap through a structured 0–100 readiness scoring framework integrating laboratory markers, symptom burden, autonomic instability, flare history, and functional status. Built within the broader STAIR Stable Method™ framework (Stabilization, Tolerance, and Immune Readiness), Va-IRI™ applies terrain-aware and low-and-slow stabilization principles that emerged from years of patient-led adaptation and cross-disciplinary collaboration among researchers, clinicians, and chronically ill

communities.

The biologic rationale for Va-IRI™ is supported by major collaborative research efforts involving Yale, Mount Sinai, and Harlan Krumholz. One study characterized the clinical and immunologic architecture of Long COVID across large patient populations (Klein et al., 2023), while a later Yale-led investigation examined immune signatures associated with post-vaccination chronic illness and persistent symptom development (Luna et al., 2025). Together, these studies reinforce the need for a terrain-based framework capable of identifying when immune systems may be too unstable for conventional intervention timing.

Background and Rationale

Long COVID, ME/CFS, and related post-infectious conditions are increasingly associated with immune dysregulation, autonomic dysfunction, inflammatory persistence, and impaired recovery signaling (Komaroff & Lipkin, 2021; Eaton-Fitch et al., 2024). Research has demonstrated that severe viral illness may leave lasting alterations in CD8+ T-cell function, cytokine regulation, and immune tolerance pathways (Vázquez-Alejo et al., 2023; Wiech et al., 2022), creating physiologic environments where additional immune stimulation may produce unpredictable responses.

At the same time, conditions such as MCAS and dysautonomia may significantly affect tolerability to medications, vaccines, and environmental stressors (Afrin et al., 2021; Nicola et al., 2024). These overlapping mechanisms are frequently observed in patients with relapsing-remitting symptom patterns, fluctuating inflammatory load, and heightened sensitivity to physiologic disruption.

A multi-institutional Yale-led study co-authored by Akiko Iwasaki, David Putrino, Harlan Krumholz, Cynthia Adinig, and others identified measurable immune abnormalities in individuals experiencing chronic illness following vaccination (Luna et al., 2025). Earlier collaborative work involving Yale, Mount Sinai, Harlan Krumholz, and Cynthia Adinig similarly characterized Long COVID as a complex multisystem condition with significant immune and neurologic involvement at scale (Klein et al., 2023). Taken together, these findings suggest that vaccination response may not simply depend on the product itself, but also on the biologic state of the patient at the time of exposure. Va-IRI™ was developed from this premise: that immune timing, stabilization, and terrain matter.

The Problem

Current vaccination frameworks largely assume immunologic uniformity despite growing evidence that immune-fragile populations may respond differently to physiologic stressors and inflammatory triggers. Patients with complex chronic illness are frequently excluded from clinical trials, limiting visibility into risk stratification, adverse response patterns, and stabilization requirements (Su et al., 2022; Peluso & Deeks, 2022).

Meanwhile, patient communities and emerging literature increasingly describe post-vaccination symptom trajectories that mirror mechanisms seen in Long COVID and related post-infectious syndromes, including autonomic dysfunction, inflammatory flares, exercise intolerance, neurologic symptoms, and mast cell activation patterns (Halma et al., 2025). Yet despite these reports, clinical systems currently offer no standardized readiness assessment prior to vaccination in medically complex populations.

As a result, clinicians are often left relying on subjective judgment and fragmented patient history rather than structured physiologic assessment. This absence of readiness modeling contributes not only to safety uncertainty, but also to declining public trust among chronically ill populations who feel their lived experiences remain unrecognized within conventional frameworks.

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 with immune mapping of post-vaccination syndromes and long COVID characteristics Va-IRI™ becomes the first actionable scoring system for safe vaccination in immune-fragile populations.

CYNAERA Framework Papers and Core Research Libraries

This paper draws on a defined subset of CYNAERA Institute white papers that establish the methodological and analytical foundations of CYNAERA’s frameworks. These publications provide deeper context on prevalence reconstruction, remission, combination therapies and biomarker approaches. Our Long COVID Library,  ME/CFS Library, Lyme Library,  Autoimmune Library and CRISPR Remission Library are also in depth resources.

• Adaptive Vaccination Protocol™ (AVP™)

• The Body First Trial Protocol™

• The Science of IACC Remission

• Primary Chronic Trigger (PCT)™ 

• CYNAERA Remission Standard™

• Aligned Intelligence Method (AIM™)

• The Hidden Public Health Cost of AI Data Centers

• Personalized CRISPR Remission™ for Autoimmune

• SymCas™: Symptom Flare Prediction in IACCs

• VitalGuard™ Environmental Flare Risk Engine

Author’s Note:

All insights, frameworks, and recommendations in this written material 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.

Patent-Pending Systems

Bioadaptive Systems Therapeutics™ (BST) and affiliated CYNAERA frameworks are protected under U.S. Provisional Patent Application No. 63/909,951. CYNAERA is built as modular intelligence infrastructure designed for licensing, integration, and strategic deployment across health, research, public sector, and enterprise environments.

Licensing and Integration

CYNAERA supports licensing of individual modules, bundled systems, and broader architecture layers. Current applications include research modernization, trial stabilization, diagnostic innovation, environmental forecasting, and population level modeling for complex chronic conditions. Basic licensing is available through CYNAERA Market, with additional pathways for pilot programs, institutional partnerships, and enterprise integration.

About the Author 

Cynthia Adinig is the founder of CYNAERA, a modular intelligence infrastructure company that transforms fragmented real world data into predictive insight across healthcare, climate, and public sector risk environments. Her work sits at the intersection of AI infrastructure, federal policy, and complex health system modeling, with a focus on helping institutions detect hidden costs, anticipate service demand, and strengthen planning in high uncertainty environments.

Cynthia has contributed to federal health and data modernization efforts spanning HHS, NIH, CDC, FDA, AHRQ, and NASEM, and has worked with congressional offices including Senator Tim Kaine, Senator Ed Markey,  Representative Don Beyer, and Representative Jack Bergman on legislative initiatives related to chronic illness surveillance, healthcare access, and data infrastructure. In 2025, she was appointed to advise the U.S. Department of Health and Human Services and has testified before Congress on healthcare data gaps and system level risk.

She is a PCORI Merit Reviewer, currently advises Selin Lab at UMass Chan, and has co-authored research  with Harlan Krumholz, MD, Akiko Iwasaki, PhD, and David Putrino, PhD, including through Yale’s LISTEN Study. She also advised Amy Proal, PhD’s research group at Mount Sinai through its CoRE advisory board and has worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. Her CRISPR Remission™ abstract was presented at CRISPRMED26 and she has authored a Milken Institute essay on artificial intelligence and healthcare.

Cynthia has been covered by outlets including TIME, Bloomberg, Fortune, and USA Today for her policy, advocacy, and public health work. Her perspective on complex chronic conditions is also informed by lived experience, which sharpened her commitment to reforming how chronic illness is understood, studied, and treated. She also advocates for domestic violence prevention and patient safety, bringing a trauma informed lens to her research, systems design, and policy work. Based in Northern Virginia, she brings more than a decade of experience in strategy, narrative design, and systems thinking to the development of cross sector intelligence infrastructure designed to reduce uncertainty, improve resilience, and support institutional decision making at scale.

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.