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Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms.

Publication ,  Journal Article
Ustun, B; Westover, MB; Rudin, C; Bianchi, MT
Published in: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
February 2016

Obstructive 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.We 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.For 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.To 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.A commentary on this article appears in this issue on page 159.

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Published In

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

DOI

EISSN

1550-9397

ISSN

1550-9389

Publication Date

February 2016

Volume

12

Issue

2

Start / End Page

161 / 168

Related Subject Headings

  • Sleep Apnea, Obstructive
  • Sensitivity and Specificity
  • Self Report
  • Reproducibility of Results
  • Polysomnography
  • Neurology & Neurosurgery
  • Models, Statistical
  • Middle Aged
  • Medical History Taking
  • Male
 

Citation

APA
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ICMJE
MLA
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Ustun, B., Westover, M. B., Rudin, C., & Bianchi, M. T. (2016). Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 12(2), 161–168. https://doi.org/10.5664/jcsm.5476
Ustun, Berk, M Brandon Westover, Cynthia Rudin, and Matt T. Bianchi. “Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms.Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine 12, no. 2 (February 2016): 161–68. https://doi.org/10.5664/jcsm.5476.
Ustun B, Westover MB, Rudin C, Bianchi MT. Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2016 Feb;12(2):161–8.
Ustun, Berk, et al. “Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms.Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, vol. 12, no. 2, Feb. 2016, pp. 161–68. Epmc, doi:10.5664/jcsm.5476.
Ustun B, Westover MB, Rudin C, Bianchi MT. Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2016 Feb;12(2):161–168.

Published In

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

DOI

EISSN

1550-9397

ISSN

1550-9389

Publication Date

February 2016

Volume

12

Issue

2

Start / End Page

161 / 168

Related Subject Headings

  • Sleep Apnea, Obstructive
  • Sensitivity and Specificity
  • Self Report
  • Reproducibility of Results
  • Polysomnography
  • Neurology & Neurosurgery
  • Models, Statistical
  • Middle Aged
  • Medical History Taking
  • Male