Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms.
Journal Article (Journal Article)
Study objective
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.Methods
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.Results
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.Conclusion
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.Commentary
A commentary on this article appears in this issue on page 159.Full Text
Duke Authors
Cited Authors
- Ustun, B; Westover, MB; Rudin, C; Bianchi, MT
Published Date
- February 2016
Published In
Volume / Issue
- 12 / 2
Start / End Page
- 161 - 168
PubMed ID
- 26350602
Pubmed Central ID
- PMC4751423
Electronic International Standard Serial Number (EISSN)
- 1550-9397
International Standard Serial Number (ISSN)
- 1550-9389
Digital Object Identifier (DOI)
- 10.5664/jcsm.5476
Language
- eng