Prediction of obstructive sleep apnea in patients presenting to a tertiary care center

Abstract

The objective of this prospective observational clinical study is to derive and validate a diagnostic model for prediction of obstructive sleep apnea (OSA) in subjects presenting with non-sleep-related complaints in a tertiary care center in north India. We included 102 subjects (group I, range 31-70 years) presenting to the hospital with non-sleep-related complaints. None of the subjects had any significant comorbid illness such as respiratory or congestive cardiac failure. All subjects underwent detailed evaluation including polysomnography (PSG). Various parameters were compared between the cases (apnea-hypopnea index, AHI > or =15/h) and controls (AHI <15/h). Using multivariate logistic regression analysis, a diagnostic model for prediction of OSA was derived. Subsequently, using similar selection criteria, 104 subjects (group II, range 32-68 years) were included for validation of the newly derived diagnostic model. Body mass index [BMI; OR (95% CI), 1.14(1.1-1.2)], male gender 5.0(1.4-27.1), relative-reported snoring index (SI) 2.8(1.7-5.0), and choking index (ChI) 8.1(1.4-46.5) were significant, independent predictors of OSA. Diagnostic model was computed as score = [1.61 x (gender)] + [1.01 x (S1)] + [2.09 x (ChI)] + [0.1 x (BMI)] where, gender: 0 = female, 1 = male and SI, ChI, BMI are actual values. The diagnostic model had an area under the receiver operator characteristics curve of 89.6%. A cutoff of 4.3 for the score was associated with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 91.3, 68.5, 70.5, and 92.3%, respectively. Misclassification rate with the application of the diagnostic model on group II subjects was 13.5% (14/104). Sensitivity, specificity, PPV, and NPV of the model for predicting OSA in this group were 82, 90.7, 89.1, and 84.5%, respectively. BMI, male gender, SI, and ChI are independent predictors of OSA. Diagnostic model derived from these parameters is useful for predicting presence of OSA and screening subjects for PSG.

Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

“…clinical prediction Models: Four studies evaluated the performance of clinical prediction models against PSG, 61,[76][77][78] and three studies 75,79,80 evaluated these models against HSAT. Two of the studies compared respiratory parameters against PSG: a study involving a Chinese cohort that evaluated snoring while sitting 76 and another single study assessing respiratory conductance and oximetry.…”

“…Other studies compared clinical prediction rules including age, waist circumference, ESS score and minimum oxygen saturation, and another evaluated gender, nocturnal choking, snoring and body mass index against PSG; these reported reasonably high sensitivity (range of 0.72-0.94) and specificity (range of 0.75-0.91) considering different AHI thresholds. 61,77 Clinical prediction rules have been evaluated against HSAT in select populations, i.e., the elderly, 75 bariatric surgery candidates, 79 and commercial drivers. 80 These studies reported sensitivities ranging from 0.76-0.97 and specificities ranging from 0.19-0.75 using an AHI cutoff of ≥ 30 75,79,80 (see supplemental material, Table S29 through Table S31).…”

Revision of Estimation Method for an Established Morphometric Model for Prediction of Obstructive Sleep Apnea: A Pilot Study

“…There has been only one previous Indian study in which a diagnostic model was derived and validated for the prediction of OSA in subjects presenting with non-sleep-related complaints [11]. The results of our study indicate that the relatively easier morphometric measurements afforded by the SMM enable faster screening for OSA in a primary health care setting.…”

Non-linear Poincaré analysis of respiratory efforts in sleep apnea

“…Previous studies for prediction of OSA have used a wide variety of statistical approaches, photographic analysis and clinical and photographic measurements to predict the presence of OSA (19)(20)(21)(22). These approaches have been limited by poor sensitivity, specifi city, lack of prospective validation and taking too much time for computations and analysis.…”