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


CONCLUSIONS
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.


CONCLUSIONS
A commentary on this article appears in this issue on page 159.
Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
Berk
B
Ustun
Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA.
M Brandon
MB
Westover
Sloan School of Management and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Cynthia
C
Rudin
Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA.|Division of Sleep Medicine, Harvard Medical School, Boston, MA.
Matt T
MT
Bianchi
D000328:Adult; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D008487:Medical History Taking; D008875:Middle Aged; D015233:Models, Statistical; D017286:Polysomnography; D015203:Reproducibility of Results; D057566:Self Report; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*
electronic health records; machine learning; medical scoring systems; sleep apnea; sparsity in predictive models