Peak GVHD Composite
Critical72.9
on 2026-03-02
RED Alerts in ±3d Window
None0/0
0 outside ±3d event window
Combined GVHD + BOS
Normal34.2
LOW
Clinical Context: Chronic GVHD affecting skin, liver, mouth (NIH 2014: moderate). Known acute decompensation on Feb 9, 2026 (validation target). rSLDS classified Feb 9 as N/A, while RED alerts were mostly off-window (0/0 outside ±3d), so treat this as a retrospective state-classification signal. Top ranked features in this run: HRV Median (RMSSD), Sleep Heart Rate, Lowest HR. Temperature deviation contributes inflammatory context but is not the leading feature.
1. Temperature Fluctuation Analysis
Temperature deviation from Oura measures deviations from personal baseline body temperature. GVHD causes micro-inflammatory signatures visible as increased night-to-night variability. CUSUM change-point detection identifies regime shifts in temperature dynamics.
Regime changes detected: 2026-01-31, 2026-02-11, 2026-03-02
2. Multi-Stream GVHD Composite Score
Six biometric streams weighted by clinical relevance: temperature (25%), HRV (20%), SpO2 (15%), sleep fragmentation (15%), resting HR (15%), activity (10%). Each component z-scored against first 14 days baseline, normalized to 0-100 (higher = more GVHD-like).
3. rSLDS State Model
4-state rSLDS (Remission, Pre-flare, Active Flare, Recovery) fitted via Laplace-EM (Linderman et al. 2017). Each discrete state governs linear dynamics in a continuous latent space, with recurrent transitions that depend on the latent state. Observations: composite score + temperature deviation + HRV (3 features).
rSLDS State Distribution
4. Retrospective Alert Burden
YELLOW alert: P(pre-flare) > 0.3 for 3+ consecutive days.
RED alert: P(pre-flare) > 0.5 OR P(active flare) > 0.2.
Retrospective validation against Feb 9, 2026 acute decompensation: 0/0 RED alerts fell within ±3d of the event and 0 occurred outside that window.
No figure available
Alert History (0 total)
| Date | Level | P(Pre-flare) | P(Flare) | Composite |
|---|
5. Predictive Feature Importance
Features ranked by combined score: point-biserial correlation (30%), mutual information (30%), composite correlation (20%), Cohen's d effect size (20%). Binary target: composite score > 65th percentile.
No figure available
Top Predictive Features for GVHD State
| Feature | Importance | Correlation | Mutual Info | Cohen's d |
|---|
| HRV Median (RMSSD) | 0.520 | -0.548 | 0.266 | -1.45 |
| Sleep Heart Rate | 0.503 | +0.594 | 0.124 | +1.46 |
| Lowest HR | 0.502 | +0.571 | 0.170 | +1.41 |
| Readiness Score | 0.353 | -0.410 | 0.079 | -0.89 |
| Temperature Deviation | 0.282 | +0.326 | 0.127 | +0.65 |
| Temperature Gradient | 0.208 | +0.255 | 0.064 | +0.53 |
| HRV Variability | 0.200 | -0.244 | 0.044 | -0.57 |
6. BOS Risk Integration
Bronchiolitis obliterans syndrome (BOS) is the pulmonary manifestation of chronic GVHD. BOS risk score (16.9/LOW) loaded from SpO2/BOS analysis and integrated with systemic GVHD composite. SpO2 trend analysis provides supplementary pulmonary assessment.
- BOS Risk Score: 16.9 (LOW)
- Combined Risk: 34.2 (LOW)
- SpO2 Mean: 96.07%
- SpO2 Trend: -0.0047 %/day
Disclaimer: This analysis is for research purposes only and should not be used as a sole basis for clinical decisions. N=1 retrospective case study - all detection metrics are descriptive, not inferential. Sensitivity/specificity cannot be computed from a single patient. Validation requires an external multi-patient cohort. Temperature deviation from consumer wearables has limited precision compared to clinical thermometry. All clinical decisions should be made in consultation with the treating hematologist.