ME/CFS Individualized Regimen Engine™

Author: Cynthia Adinig

Executive Summary

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) continues to lack FDA-approved treatments, despite decades of research and millions affected worldwide (Komaroff & Bateman, 2021; Solve ME/CFS, 2023). Traditional approaches often recycle a handful of interventions without acknowledging the full complexity of patient trajectories.

The CYNAERA ME/CFS Individualized Regimen Engine™ is an educational modeling framework that simulates phased strategies for ME/CFS care. Built on CYNAERA’s ecosystem of validated modules — including Treatment Archetypes, Phenotyping Framework, Pathophysiology Drivers, and the Path of Remission Model — the Regimen Engine demonstrates how individualized patient care can be systematically structured.

This public version is abbreviated: it outlines the phases, inputs, and sample logic rules, but does not expose the proprietary internal weighting, escalation matrices, or AI-driven decision trees. Those remain embedded in CYNAERA’s licensed models. Clinicians, researchers, and advocates are encouraged to view this as a teaching scaffold, not as a prescriptive protocol.

Why This Matters

• Heterogeneity: ME/CFS patients differ dramatically in onset, phenotype, triggers, and co-morbid conditions (Jason et al., 2021).

• Undercount: As shown in our Men Undercount Paper, millions of men remain invisible in prevalence data, meaning treatment pathways designed around women may not generalize (Adinig, 2025).

• Pediatric Burden: CYNAERA’s Pediatric CDF model estimates 1.5–3 million U.S. children and 10–20 million worldwide with ME/CFS (CYNAERA, 2025), underscoring the need for age-adapted regimens.

• Environmental Triggers: As shown in our Environmental Triggers White Paper, pollutants and weather events amplify PEM and autonomic instability, demanding built-in flare safeguards.

The Regimen Engine synthesizes these cross-cutting insights, showing how to stratify, stabilize, and simulate individualized care.

Stratification: Patient Profiles

The Regimen Engine begins with phenotyping, categorizing patients into archetypes. These overlap with the six CYNAERA phenotypes but are simplified here into four public-facing types:

Type	Profile Traits	Risks
A. Post-Viral Gradual Onset	EBV, mono, or Long COVID history; worsening over 6–18 months	Immune rebound crashes, early POTS
B. Sudden-Onset Crash	Abrupt collapse post-virus, surgery, or stress	Severe PEM, dysautonomia, fragmented sleep
C. Pediatric / Young Adult	<25 yrs, fluctuating, frequent EDS/POTS comorbidity	Joint instability, school disruption, high relapse risk
D. MCAS-Dominant	Histamine flares, seasonal worsening, allergy-like profile	Mast cell storms, multi-system hypersensitivity

Note: Internal CYNAERA phenotyping uses six axes (immune, mitochondrial, autonomic, neurocognitive, endocrine, environmental) and over 150 micro-profiles, as described in the Phenotyping White Paper.

Phased Regimen Framework

The regimen engine organizes interventions into four phases, echoing the staged stabilization model from the Path of Remission White Paper.

Phase	Goal	Sample Duration	Logic
Stabilization	Halt decline	2–6 weeks	Introduce pacing, antihistamines, hydration, gentle supports.
Modulation	Nudge system	4–8 weeks	Add low-dose therapies (e.g., LDN, magnesium, antivirals if confirmed).
Response Mapping	Identify tolerance	2–4 weeks	Test one therapy at a time; map PEM windows.
Personalization	Lock in	Ongoing	Algorithm selects stabilizing combinations with lowest flare risk.

Each phase is scored internally using PEM delay, HR variability, and other markers. The public version is simplified; the internal engine uses multi-layered scoring rules tied to CYNAERA’s SymCas™ and Pathos™ systems.

Key Inputs Tracked

• PEM Delay: Hours between exertion and symptom spike.

• Heart Rate Variability: Orthostatic delta; >30 bpm suggests POTS/dysautonomia.

• Symptom Persistence: Days since last flare.

• Antihistamine Response: Relief % post H1/H2.

• Temperature Fluctuation: >1.5°F indicates hypothalamic stress.

Cross-linked: These inputs also appear in VitalGuard’s flare forecasting and Phenotyping endotype scoring.

Example Dosing Logic (Educational Version)

Treatment	Starting Rule	Sample Engine Rule
LDN	Start 0.25mg nightly	If sleep worsens, delay escalation.
Famotidine + Quercetin	Begin if MCAS suspected	Pause Quercetin if no benefit in 7 days.
Magnesium Glycinate	100–150mg daily	Maintain if muscle pain + sleep improve.
Antivirals	Only with confirmed reactivation	Monitor for Herxheimer-like flares.

Cross-linked: Full therapeutic scaffolding appears in the Treatment Archetypes White Paper, which details six mechanistic axes (mitochondrial, neuroimmune, autonomic, viral, immune reset, oxygenation).

Flare Simulation Protocol

If PEM or sudden crash occurs:

1. Pause non-essential therapies.

2. Increase electrolytes + fluids.

3. Optimize H1/H2 antihistamines.

4. Use binders if cytokine storm suspected.

5. Resume regimen only after 48 hrs of baseline stability.

Cross-linked: Aligns with VitalGuard flare stabilizers and Path of Remission thresholds.

Prompt Templates

Prompt 1 “You are a biomedical researcher studying ME/CFS. Apply the CYNAERA Regimen Engine framework to a sudden-onset case (Type B). Which phase are they likely in, and which supportive interventions might be simulated next in a non-clinical research model?”

Prompt 2 “Using the CYNAERA Regimen Engine, outline how a pediatric ME/CFS case with comorbid POTS (Type C) could progress through the Stabilization → Modulation → Mapping → Personalization phases. Highlight flare safeguards and school-related concerns.”

Prompt 3 " You are reviewing a complex ME/CFS case. Apply the CYNAERA ME/CFS Individualized Regimen Engine as a modeling framework. Use the Stabilization, Modulation, Mapping, and Personalization phases as scaffolding. Integrate patient-reported data (PEM delay, HR variability, symptom persistence, antihistamine response, temperature fluctuation) into your assessment. Provide a structured interpretation of which phase best describes the patient’s current trajectory, note potential areas for risk reduction, and highlight research-backed interventions worth consideration. All recommendations are exploratory, and defer to clinical judgment and institutional protocols."

Disclaimers

This framework is strictly educational. It illustrates how phased strategies and symptom inputs can be structured into simulations. It is not a treatment directive. Internal rules follow CYNAERA’s published white papers and are abbreviated here for transparency.

Custom Development

For institutions, research groups, or clinics seeking to integrate a validated version of this engine into practice or trial design, CYNAERA offers custom development under license. See the Custom Intelligence page for more.

References

• Addis, M. E., & Mahalik, J. R. (2003). Men, masculinity, and the contexts of help seeking. American Psychologist, 58(1), 5–14.

• Afrin, L. B., et al. (2020). Diagnosis of mast cell activation syndrome: A global consensus. Journal of Hematology & Oncology, 13(1).

• Carruthers, B. M., et al. (2011). International consensus criteria for ME/CFS. Journal of Internal Medicine, 270(4), 327–338.

• Courtenay, W. H. (2000). Constructions of masculinity and their influence on men's well-being. Social Science & Medicine, 50(10), 1385–1401.

• Jason, L. A., et al. (2020). Gender and ME/CFS underdiagnosis. Journal of Health Psychology, 25(6).

• Jason, L. A., et al. (2021). Pediatric ME/CFS prevalence and challenges. Pediatric Clinics of North America, 68(4).

• Komaroff, A. L., & Bateman, L. (2021). Clinical overview of ME/CFS. Trends in Molecular Medicine, 27(9).

• Solve ME/CFS Initiative. (2023). Patient registry reports.

• CYNAERA (2025). ME/CFS Treatment Archetypes White Paper.

• CYNAERA (2025). Phenotyping Framework White Paper.

• CYNAERA (2025). Path of Remission White Paper.

• CYNAERA (2025). Environmental Triggers White Paper.

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.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

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.