Prospector Study

Title: PROSPECTOR-HRV-CALIBRE Study – Limited Antibiotic Use and Early Exacerbation Prediction in COPD

Objective
To evaluate a decision-support strategy for reducing unnecessary antibiotic use in COPD exacerbations and to develop a predictive model integrating HRV, respiratory metabolic data, and clinical features. The study will inform design of a prototype metabolic mask with integrated HRV sensors capable of real-time early warning and clinician alerts.

Design
Prospective, multicentre, randomized crossover study. Each COPD exacerbation triggers a new randomization (Prospector-guided vs. Anthonisen criteria). Target: 120 patients, ~200 exacerbations.

Participants
Adults with stable COPD (GOLD II–IV), ≥1 exacerbation in past 12 months. Exclusion: active malignancy, unstable cardiac disease, inability to perform CPET.

Interventions
• Strategy A (Prospector-guided): Clinical algorithm integrating symptoms, CRP, infection probability, and patient profile.
• Strategy B (Anthonisen): Standard clinical triad-based decision.
Antibiotics prescribed only if indicated by assigned strategy.

Measurements
• Daily HRV: 5-min Kubios-based recording using chest HRV sensor.
• Daily metabolic data: Calibre mask measuring resting ventilation, work of breathing, and gas exchange patterns.
• CPET: Two assessments per patient (baseline; post-exacerbation/recovery) for physiological profiling and prognostic evaluation.
• Clinical data: Symptoms, medication use, hospitalisations, adverse events.

Outcomes
Primary: Reduction in antibiotic prescriptions per exacerbation.
Secondary: Safety, recurrence, hospitalisations, changes in CPET parameters, HRV and respiratory-pattern shifts preceding exacerbations, usability of daily monitoring.

Predictive Model Development
HRV (time, frequency, non-linear metrics), ventilation dynamics, metabolic markers, Prospector scores, and CPET data will be integrated to train gradient-boosting and sequence-based models. Aim: detect exacerbation risk 24–72 h before onset.

Prototype Metabolic Mask
Post-analysis, study data will guide development of a wearable metabolic mask combining gas-exchange sensors with a chest HRV module. Based on the validated early-warning algorithm, the device will:
• detect physiological deterioration,
• alert the patient,
• transmit alerts to the clinical dashboard.
The system will link patient and physician apps, supporting remote monitoring and early intervention.

Study Flow

1. Baseline visit: consent, randomisation registration, CPET-1, device training.

2. Exacerbation: re-randomisation; treatment per assigned strategy.

3. Daily monitoring: HRV + Calibre; symptom reporting.

4. Recovery visit: CPET-2, safety evaluation.

5. Repeat cycle for each new exacerbation.

Statistical Plan
Mixed-effects models accounting for repeated events within individuals. Intention-to-treat analysis for antibiotic use. Model performance assessed using AUROC, precision-recall, and calibration.

Expected Impact
• Reduced antibiotic exposure in COPD.
• Validated digital biomarkers (HRV + ventilation).
• Early-warning system enabling pre-emptive care.
• Prototype mask and app ecosystem for scalable Horizon Europe implementation.