Using clinical data to predict obstructive sleep apnea

BackgroundObstructive sleep apnea (OSA) is a common disorder and associated with motor vehicle accidents, reduced quality of life and various comorbidities. It is necessary to identify clinical parameters that may predict the presence and severity of OSA.MethodsSubjects with suspected OSA were consecutively recruited for development and validation of the models. Clinical data collected from participants included general information, OSA-related symptoms, questionnaire responses, and physical examination. Logistic and linear regressions were used to develop models to determine the presence and severity of OSA.ResultsAll 202 subjects (157 men, 45 women; age range, 18–68 years) underwent polysomnography (PSG) and clinical assessment, of whom 62.3% were diagnosed with OSA. The presence of OSA was defined using the equation, 1.00 × central obesity + 2.05 × snoring + 1.80 × witnessed nocturnal apnea + 1.73 × lateral narrowing – 3.25; and apnea-hypopnea index (AHI) was defined using, 12.5 × central obesity + 17.1 × witnessed nocturnal apnea + 6.2 × tonsillar size + 9.0 × lateral narrowing – 19.7. The model demonstrated a sensitivity of 81.1% (95% CI: 73.2–87.5%) and a specificity of 76.0% (95% CI: 64.7–85.1%) at the optimal cut-off value for OSA detection. The positive and negative likelihood ratios were 3.4 (95% CI: 2.2–5.1) and 0.3 (95% CI: 0.2–0.4), respectively. The area under the receiver operating characteristic curve for the predictive model (83.7%) was significantly greater than that of the Berlin Questionnaire (53.5%), Epworth Sleepiness Scale (61.1%), and STOP-BANG questionnaire (73.8%). 101 subjects were recruited as the validation group. The models to determine the presence and severity of OSA had an accuracy of 0.812 and 0.416 in the validation group.ConclusionsResults of the present study suggest that a combination of clinical data may be helpful in identify patients who are at increased risk for OSA.