ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2016
Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Racial Disparities and Comorbidities: Network Analysis of Maternal Outcomes in Alabama.Journal of racial and ethnic health disparities · 2026Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm.Sleep & breathing = Schlaf & Atmung · 2024Article
- OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics.Computers in biology and medicine · 2024Article
- Insomnia and Early Incident Atrial Fibrillation: A 16-Year Cohort Study of Younger Men and Women Veterans.Journal of the American Heart Association · 2023Article
- International Consensus Statement on Obstructive Sleep Apnea.International forum of allergy & rhinology · 2023Review
- Clinical impact of routine sleep assessment by peripheral arterial tonometry in patients with COPD.ERJ open research · 2023Article
- Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.International journal of environmental research and public health · 2023Article
- Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Disparities in brain health comorbidity management in intracerebral hemorrhage.Frontiers in neurology · 2023Article
- Reliability, validity, and psychometric properties of the Persian version of the Tayside children's sleep questionnaire.Sleep and biological rhythms · 2023Article
- Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea.Medicina (Kaunas, Lithuania) · 2022Review
- Mediating Effect of Self-Cognitive Oral Health Status on the Effect of Obstructive Sleep Apnea Risk Factors on Quality of Life (HINT-8) in Middle-Aged Korean Women: The Korea National Health and Nutrition Examination Survey.Life (Basel, Switzerland) · 2022Article
- Obstructive sleep apnea: transition from pathophysiology to an integrative disease model.Journal of sleep research · 2022Review
- Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters.Sleep & breathing = Schlaf & Atmung · 2022Article
- Effects of Obstructive Sleep Apnea and Obesity on 30-Day Readmissions in Patients with Chronic Obstructive Pulmonary Disease: A Cross-Sectional Mediation Analysis.Annals of the American Thoracic Society · 2022Article
- Predictors and outcomes of obstructive sleep apnea in patients with chronic obstructive pulmonary disease in China.BMC pulmonary medicine · 2022Observational
- Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach.Computational intelligence and neuroscience · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
STUDY
objectiveObstructive sleep apnea (OSA) is a treatable contributor to morbidity and mortality. However, most patients with OSA remain undiagnosed. We used a new machine learning method known as SLIM (Supersparse Linear Integer Models) to test the hypothesis that a diagnostic screening tool based on routinely available medical information would be superior to one based solely on patient-reported sleep-related symptoms.
methodsWe analyzed polysomnography (PSG) and self-reported clinical information from 1,922 patients tested in our clinical sleep laboratory. We used SLIM and 7 state-of-the-art classification methods to produce predictive models for OSA screening using features from: (i) self-reported symptoms; (ii) self-reported medical information that could, in principle, be extracted from electronic health records (demographics, comorbidities), or (iii) both.
resultsFor diagnosing OSA, we found that model performance using only medical history features was superior to model performance using symptoms alone, and similar to model performance using all features. Performance was similar to that reported for other widely used tools: sensitivity 64.2% and specificity 77%. SLIM accuracy was similar to state-of-the-art classification models applied to this dataset, but with the benefit of full transparency, allowing for hands-on prediction using yes/no answers to a small number of clinical queries.
conclusionTo predict OSA, variables such as age, sex, BMI, and medical history are superior to the symptom variables we examined for predicting OSA. SLIM produces an actionable clinical tool that can be applied to data that is routinely available in modern electronic health records, which may facilitate automated, rather than manual, OSA screening. COMMENTARY: A commentary on this article appears in this issue on page 159.
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