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Remote Monitoring of Positive Airway Pressure Data: Challenges, Pitfalls, and Strategies to Consider for Optimal Data
Guillaume Bottaz-Bosson1, Alphanie Midelet2, Monique Mendelson3
1Laboratoire HP2, U1300 Inserm, University Grenoble Alpes, Grenoble, France; Jean Kuntzmann Laboratory, University Grenoble Alpes, Grenoble, France.
Chest
|December 5, 2022
Summary
Positive airway pressure (PAP) remote monitoring generates valuable data for obstructive sleep apnea (OSA) management. Standardizing data processing is crucial for reliable analysis and advancing sleep research with AI.
Area of Science:
- Sleep Medicine
- Data Science in Healthcare
- Respiratory Physiology
Background:
- Remote monitoring of positive airway pressure (PAP) therapy for obstructive sleep apnea (OSA) has become widespread, generating substantial patient data.
- Current analyses often simplify complex longitudinal data using static metrics like mean or median values for adherence and residual apnea-hypopnea index (AHI).
- This underutilizes the rich information contained within PAP monitoring data for sleep research.
Purpose of the Study:
- To propose improvements in data cleaning and processing for PAP remote monitoring data.
- To address key challenges in data science applications, including residual AHI reliability, device indicator standardization, missing data, and treatment interruptions.
- To advocate for rigorous data handling to enable fair comparisons and reduce bias in research.
Main Methods:
- The article reviews current practices in analyzing longitudinal PAP remote monitoring data.
- It identifies critical areas for enhancing data processing and management strategies.
- Recommendations focus on improving data quality and standardization for advanced analysis.
Main Results:
- Current methods for analyzing PAP data are often simplistic, limiting the insights gained.
- Several critical issues hinder the reliable application of data science to PAP monitoring, including lack of standardization and handling of missing data.
- Addressing these issues is essential for unlocking the full potential of PAP data.
Conclusions:
- Standardized and rigorous processing of PAP remote monitoring data is necessary for reliable interpretation and comparison across studies.
- Improving data quality and handling will facilitate global data sharing among sleep specialists.
- This standardization is a prerequisite for applying advanced artificial intelligence strategies to sleep apnea research.
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