Crash Impact on Recovery Biomarkers Study

1 Background and Rationale

The Recovery Capital model (Speculation Integrative Speculations) proposes that patients possess finite biological reserves that deplete with each crash episode. If correct, crash frequency and severity should correlate with accelerated decline in Recovery Potential Index (RPI) components over time. This study would test this hypothesis directly in a pediatric cohort, where the range of outcomes (recovery vs. chronification) is wide enough to detect biomarker-outcome relationships.

2 Hypothesis

3 Study Design

3.1 Design Overview

Prospective observational cohort study with serial biomarker assessment and crash tracking.

3.2 Participants

  • n=50 pediatric/adolescent ME/CFS patients (ages 10–17)
  • Disease duration 6 months to 3 years at enrollment
  • Mild to moderate severity (able to attend quarterly study visits)
  • Parental consent plus child assent

3.3 Assessment Schedule

  • Baseline: Full RPI component panel (epigenetic age, naive T cell proportion, telomere length, HRV metrics, metabolic flexibility assessment), clinical severity, symptom measures
  • Quarterly (every 3 months): Abbreviated RPI panel (HRV, selected immune markers), symptom questionnaires, crash diary review
  • Annually (12 and 24 months): Full RPI panel, comprehensive clinical assessment
  • Continuous: Wearable activity monitoring, electronic crash diary with severity ratings

3.4 Crash Documentation

Participants (with parental assistance) will maintain electronic crash diaries including:

  • Date of crash trigger (exertion event)
  • Type of trigger (physical, cognitive, emotional, mixed)
  • Crash severity (1–10 scale, anchored descriptions)
  • Recovery duration (days to return to baseline)
  • Classification per crash severity tier (Table Crash Severity Dose-Response from treatment chapter)

4 Outcomes

4.1 Primary Outcome

Correlation between cumulative crash burden (sum of severity-weighted crashes) and change in composite RPI score from baseline to 24 months.

4.2 Secondary Outcomes

  • Correlation of crash burden with individual RPI components
  • Association between crash burden and 24-month recovery status
  • Time-varying analysis: Does crash burden in months 0–12 predict RPI decline in months 12–24?
  • Threshold analysis: Is there a crash burden threshold beyond which RPI decline accelerates?

5 Analysis Plan

  • Mixed-effects models with random intercepts for subjects to assess RPI trajectory
  • Crash burden as time-varying covariate
  • Adjustment for baseline severity, age, sex, disease duration
  • Sensitivity analyses with different crash severity weighting schemes

6 Sample Size Justification

With n=50 and 3 timepoints per subject (150 observations):

  • 80% power to detect correlation r=0.35 between crash burden and RPI change at \(\alpha\)=0.05
  • Sufficient for exploratory subgroup analyses

7 Expected Outcomes

If the hypothesis is supported, this study would:

  • Provide first direct evidence that crashes deplete measurable biological reserves
  • Validate crash prevention as disease-modifying intervention
  • Identify which RPI components are most crash-sensitive
  • Inform clinical recommendations about crash prevention intensity