0463 Deep Learning Classification of Future PAP Adherence based on CMS and other Adherence Criteria Article Swipe
Samuel Rusk
,
Yoav Nygate
,
Chris Fernandez
,
Jiaxiao Shi
,
Jessica Arguelles
,
Matthew T Klimper
,
Nathaniel F. Watson
,
Robert Stretch
,
Michelle Zeidler
,
Anupamjeet Kaur Sekhon
,
Kendra Becker
,
Joseph Kim
,
Dennis Hwang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsad077.0463
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsad077.0463
Introduction Improving positive airway pressure (PAP) adherence is crucial to sleep apnea therapy success. Although behavioral interventions may be deployed to increase PAP adherence, operationalization remains an ongoing clinical challenge. Treatment outcomes may be optimized by forecasting PAP use to identify patients at risk for non-adherence enabling early intervention. We build upon our previous work by leveraging a larger dataset, additional metadata, and new Deep Learning approaches to forecast future PAP adherence. Methods We collected a cohort of N=21,397 subjects with daily PAP usage recorded during 2015-2021. We defined the input to models as the number of minutes the PAP machine was used during each day for the first 30-days. The ground truth was defined as the PAP adherence of the patients at the 3-month, 6- month, and 1-year endpoints. Adherence was calculated based on a 30-day window as ≥4-hours of usage for ≥70% of nights. We evaluated a Deep Neural Network (DNN) model with 10-fold cross- validation to forecast future adherence by leveraging previous daily usage information. Results were compared to a naive method which assumes adherence at each time point equals adherence during the first 30-days. Results The DNN models predicted adherence with a sensitivity of 90%, 81%, 77% and a specificity of 90%, 81%, 77%, for 3-month, 6-month, and 1-year endpoints, with ROC-AUC values of 0.97, 0.89, and 0.84 respectively. The models converged to ROC-AUC performance >0.90 for the first 90-days within the first 7 to 14-days of PAP use. Conclusion DNN models demonstrated strong predictive performance for PAP adherence, as defined by the CMS adherence criteria, measured by sensitivity, specificity, and overall ROC-AUC results at clinically relevant 90-day, 6-month, and 1-year timepoints. AI approaches show promise as early predictors of the likelihood to meet key therapy utilization thresholds within the first 1-2 weeks of therapy, enabling early PAP intervention or transition to alternative therapies. Support (if any) AASM Foundation SRA205-SR-19
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/sleep/zsad077.0463
- https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A206/50466850/zsad077.0463.pdf
- OA Status
- bronze
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378610545
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378610545Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/sleep/zsad077.0463Digital Object Identifier
- Title
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0463 Deep Learning Classification of Future PAP Adherence based on CMS and other Adherence CriteriaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-01Full publication date if available
- Authors
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Samuel Rusk, Yoav Nygate, Chris Fernandez, Jiaxiao Shi, Jessica Arguelles, Matthew T Klimper, Nathaniel F. Watson, Robert Stretch, Michelle Zeidler, Anupamjeet Kaur Sekhon, Kendra Becker, Joseph Kim, Dennis HwangList of authors in order
- Landing page
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https://doi.org/10.1093/sleep/zsad077.0463Publisher landing page
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https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A206/50466850/zsad077.0463.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A206/50466850/zsad077.0463.pdfDirect OA link when available
- Concepts
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Medicine, Psychological intervention, Positive airway pressure, Receiver operating characteristic, Cohort, Machine learning, Internal medicine, Obstructive sleep apnea, Computer science, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | SLEEP |
| best_oa_location.landing_page_url | https://doi.org/10.1093/sleep/zsad077.0463 |
| primary_location.id | doi:10.1093/sleep/zsad077.0463 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S42921845 |
| primary_location.source.issn | 0161-8105, 1550-9109 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0161-8105 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | SLEEP |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | |
| primary_location.pdf_url | https://academic.oup.com/sleep/article-pdf/46/Supplement_1/A206/50466850/zsad077.0463.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | SLEEP |
| primary_location.landing_page_url | https://doi.org/10.1093/sleep/zsad077.0463 |
| publication_date | 2023-05-01 |
| publication_year | 2023 |
| referenced_works_count | 0 |
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