Hidden Markov model segmentation to demarcate trajectories of residual apnoea-hypopnoea index in CPAP-treated sleep apnoea patients to personalize follow-up and ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-11-25

AUTHORS

Alphanie Midelet, Sébastien Bailly, Renaud Tamisier, Jean-Christian Borel, Sébastien Baillieul, Ronan Le Hy, Marie-Caroline Schaeffer, Jean-Louis Pépin

ABSTRACT

BackgroundContinuous positive airway pressure (CPAP), the reference treatment for obstructive sleep apnoea (OSA), is used by millions of individuals worldwide with remote telemonitoring providing daily information on CPAP usage and efficacy, a currently underused resource. Here, we aimed to implement data science methods to provide tools for personalizing follow-up and preventing treatment failure.MethodsWe analysed telemonitoring data from adults prescribed CPAP treatment. Our primary objective was to use Hidden Markov models (HMMs) to identify the underlying state of treatment efficacy and enable early detection of deterioration. Secondary goals were to identify clusters of rAHI trajectories which need distinct therapeutic strategies.ResultsFrom telemonitoring records of 2860 CPAP-treated patients (age: 66.31 ± 12.92 years, 69.9% male), HMM estimated three states differing in variability within a given state and probability of shifting from one state to another. The daily inferred state informs on the need for a personalized action, while the sequence of states is a predictive indicator of treatment failure. Six clusters of rAHI trajectories were identified ranging from well-controlled patients (cluster 0: 669 (23%); mean rAHI 0.58 ± 0.59 events/h) to the most unstable (cluster 5: 470 (16%); mean rAHI 9.62 ± 5.62 events/h). CPAP adherence was 30 min higher in cluster 0 compared to clusters 4 and 5 (P value < 0.01).ConclusionThis new approach based on HMM might constitute the backbone for deployment of patient-centred CPAP management improving the personalized interpretation of telemonitoring data, identifying individuals for targeted therapy and preventing treatment failure or abandonment. More... »

PAGES

535-544

References to SciGraph publications

  • 2019-01-07. High-performance medicine: the convergence of human and artificial intelligence in NATURE MEDICINE
  • 2019-02-14. Obstructive sleep apnea and comorbidities: a dangerous liaison in MULTIDISCIPLINARY RESPIRATORY MEDICINE
  • 2015-06-25. Obstructive sleep apnoea syndrome in NATURE REVIEWS DISEASE PRIMERS
  • 2017-01-15. Hidden Markov Models for Protein Domain Homology Identification and Analysis in SH2 DOMAINS
  • 2018-12-03. From CPAP to tailored therapy for obstructive sleep Apnoea in MULTIDISCIPLINARY RESPIRATORY MEDICINE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13167-021-00264-z

    DOI

    http://dx.doi.org/10.1007/s13167-021-00264-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1143053726

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34956425


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