Obstructive sleep apnea syndrome (OSAS) is a common condition, affecting 5-20% of the adult population. (1) Validated diagnostic methods, including polysomnography and home sleep apnea testing (HSAT), are not available at every center, making them poorly accessible in most countries, including Chile. Clinical prediction models are useful in order to evaluate the population at risk for OSAS. (2) However, it remains unclear which instrument or measure is most effective. It seems to depend on the population studied, and there are no surveys or clinical parameters that have been validated for use in Chile. We designed a study to evaluate the performance of the Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index, Age, Neck circumference, and Gender (STOP-Bang) questionnaire, the Epworth Sleepiness Scale, (3) the snoring scale, (4) the Sleep Apnea Clinical Scale (SACS), (5) and determination of neck circumference in patients suspected of having OSAS in Chile.

Background: Snoring is a main concern in patients who consult an otolaryngologist (ENT physicians) and patients who have cardiovascular comorbidities or excessive daytime sleepiness who usually consult with other specialists. The aim of this study was to describe the clinical differences in patients with obstructive sleep apnea (OSA) referred from ENT or other specialists. Methods: A prospective study was carried out between June 2015 and July 2018 in a tertiary center. We included patients with suspected OSA referred by the Home Sleep Apnea Test (HSAT) from different specialties such as ENT or other specialties. The main outcome measures of our study were demographic characteristics, clinical characteristics, sleep questionnaire results and HSAT results between OSA patients referred from ENT or other specialists. We used a t-test and chi-squared test for analysis. The diagnostic accuracy of the sleep questionnaires was achieved using receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results: A total of 481 patients were included. OSA was occurred in 82.4% of the subjects (90 in ENT and 306 in other specialties). Patients with OSA referred from other specialists were older than ENT patients (55 ± 13 vs 44 ± 12; p < 0.001), there was more obesity (IMC 31 ± 5.0 vs 28.7 ± 3.8; p < 0,001), a larger neck circumference (42.2 cm ± 3.7 vs 40.6 cm ± 3.0; p < 0.001) and more reported comorbidities (p < 0.001). ENT patients reported mild OSA (46% vs 31%, p = 0.015) and more positional apnea (62% vs 39%, p = 0.002). In this group, the STOP-BANG questionnaire showed an AUC 0.695 vs AUC 0.804, and for sensitivity, the best cutoff was 4 points. Patients referred from otorhinolaryngology are different from those referred from other specialties. Clinical evaluation and screening of OSA should be patient-centered according to these clinical findings.

Background and Objectives: Obstructive sleep apnea (OSA) is a heterogeneous disease with diverse clinical presentations and high global prevalence. Obesity is a common comorbidity in OSA, but its relationship with symptom subtypes remains unclear. This study aimed to evaluate the association between OSA symptom subtypes and obesity in a clinical cohort. Methods: This observational study analyzed data from the Santiago Obstructive Sleep Apnea (SantOSA) prospective clinical cohort, including adults with OSA confirmed by home sleep apnea testing. Symptom subtypes were identified using latent class analysis. Associations between obesity and symptom subtypes were evaluated using multivariable regression models adjusted for age, sex, tobacco use, RDI, T90%, TST, hypertension, and diabetes. Statistical significance was set at p < 0.05. Results: A total of 1167 patients were included (943 men). Latent class analysis identified three symptom subtypes: non-sleepy, disturbed sleep, and excessive daytime sleepiness. Among obese patients, 30.7%, 50.0%, and 19.3% were classified into these subtypes, respectively. Obesity prevalence was 50.2%, and compared with non-obese OSA patients, obese patients showed a higher prevalence of severe OSA (46.1% vs. 26.9%), hypertension (54.4% vs. 34.9%), and diabetes (37.2% vs. 19.4%), as well as higher ESS scores and higher RDI and T90% values (all p < 0.01). In adjusted analyses, obesity remained independently associated with the excessive daytime sleepiness subtype after controlling for age, sex, tobacco use, RDI, T90%, TST, and comorbidities. Conclusions: Obesity is highly prevalent in OSA and is associated with specific symptom-defined phenotypes, particularly excessive daytime sleepiness and disturbed sleep. These findings support the relevance of considering symptom profiles alongside traditional severity metrics.