Medical practices
Earlier identification and structured information before the next clinical step.
Sleep-risk stratification technology
Detect earlier.
Understand risk.
Guide the next step.
SNOZAS is a technology designed to structure sleep-related patient information, identify relevant risk profiles and help integrate the right patients into an appropriate care pathway.
Clinical decision support — not a replacement for diagnosis or medical judgement.
Structured patient signals
SNOZAS
Risk stratification
01
Detect
02
Stratify
03
Guide
Clinical context
Actionable pathway
The challenge
Sleep disorders are common.
Early identification is still difficult.
Sleep-disordered breathing can remain unidentified until symptoms, cardiovascular consequences or significant daytime impairment have already appeared.
SNOZAS is designed to bring structured risk assessment earlier into existing healthcare workflows — without creating a parallel medical pathway.
01
Detect
Capture structured sleep and clinical signals.
02
Stratify
Identify relevant risk profiles.
03
Guide
Support the appropriate next step.
04
Evaluate
Keep medical decisions with qualified professionals.
Built from real-world care
Technology shaped by an operational sleep-care pathway.
SNOZAS grew from experience gathered through SleepizZzy, an operating sleep-care pathway in France covering assessment, medical consultation, home sleep testing, treatment initiation and follow-up.
16,000+
sleep assessments
1,000+
home sleep studies
55+
physicians involved
200+
patients/month supported through the pathway
These operational figures describe the SleepizZzy real-world environment and are distinct from the SNOZAS evaluation dataset presented below.
Early scientific signal
Exploratory concordance study of SNOZAS+.
An exploratory study included 1,351 adults assessed through the sleep triage pathway. The respiratory orientation component of SNOZAS+ was compared with a 7-item STOP-Bang score, with 1,277 patients evaluable for this comparison. Available ambulatory polygraphy results were also analysed.
The findings showed agreement between SNOZAS+ and STOP-Bang. Among the 115 patients with confirmed AHI ≥15 who underwent polygraphy, SNOZAS+ identified 110/115 (95.7%) and STOP-Bang ≥3 identified 107/115 (93.0%). Further prospective validation is ongoing.
Exploratory study
n = 1,351
To be presented at Maastricht · October 2026
Evaluable comparison
n = 1,277
Agreement
82.0%
SNOZAS+
110 / 115
STOP-Bang ≥3
107 / 115
- 146 polygraphy studies were performed and reported.
- Among these patients, 115 had confirmed OSA with AHI ≥15.
- The 110/115 and 107/115 results relate only to these 115 confirmed cases.
Cohen's kappa
κ = 0.60
Confirmed AHI ≥15 cases
SNOZAS+ identified 95.7% (110/115), compared with 93.0% (107/115) using STOP-Bang ≥3.
Exploratory findings. Among patients who underwent polygraphy, 115 had confirmed OSA with AHI ≥15. Polygraphy was not systematically available across all triage categories. Further prospective validation using systematic objective sleep studies is required. These results should not be interpreted as standalone diagnostic performance claims.
Designed around clinical practice
Support the decision.
Do not replace the clinician.
SNOZAS helps structure patient information, highlight relevant risk profiles and facilitate the next appropriate step.
Diagnosis, prescription, interpretation of examinations and medical decisions remain under the responsibility of qualified healthcare professionals.
- Structured information before the medical decision
- Clear separation between algorithmic guidance and diagnosis
- Compatible with independent clinical and provider choice
Deployment
One technology.
Different healthcare environments.
SNOZAS is designed to fit into existing workflows rather than require organisations to rebuild them.
Sleep & respiratory care
Risk stratification and pathway support across specialist and home-care networks.
Prevention programmes
Scalable entry points for occupational health, insurers and population programmes.
Digital health platforms
Deployment through web, embedded experiences or integration with existing systems.
Standalone web
QR-enabled pathways
Embedded workflows
API-enabled integration
SNOZAS+
From screening to a broader sleep decision-support layer.
SNOZAS is evolving toward a broader approach combining structured clinical information with longitudinal signals from connected health data and other relevant sources.
Detect the right patient.
At the right moment.
Facilitate the right next step.
Maastricht · October 2026
Meet SNOZAS.
We are open to clinical, scientific and strategic collaborations around validation, integration and deployment.