Polysomnography performed in home during the pandemic as a diagnostic alternative for patients in research of respiratory sleep disorders

ABSTRACT

Introduction: Obstructive sleep apnea and hypopnea syndrome (OSAHS) is characterized by pauses in breathing during sleep, due to multiple collapses and/or narrowing of the upper airway, causing a drop in blood oxygen saturation and sleep fragmentation. Obesity, male gender, craniofacial abnormalities, tonsil hypertrophy, nasal obstruction, endocrine abnormalities, and family history are predictive factors for OSAHS. Objective: The aim of the study was to investigate OSAHS through home polysomnography during the COVID-19 pandemic. Methods: Data were collected between March 16, 2020, and June 25, 2021 in the city of Surubim-PE, totaling 191 subjects. The Apnea link Air portable polysomnographic device was used. With the following channels respiratory effort, pulse, oxygen saturation, nasal flow and snoring. Data were analyzed using IBM SPSS 28.0 software. Results: The apnea and hypopnea index (AHI) was considered Normal in 33 women (17.0%;2.1+1.3) and 41 men (21.2%;2.5+1.2);Mild on 18 women (9.3%;9.4+3.3) and 39 men (20.2%;8.8+3.1);Moderate in 5 women (2.5%;19.5+4.5) and 27 men (13.9%;21.4+4.7);and Severe in 8 women (4.1%;52.5+14.4) and 22 men (11.3%;51.5+17.4). The oxygen desaturation index (IDO) was, 32 women (16.5%;2.1 + 1.7) and 40 men (20.7%;2.6 + 1.1) were classified as normal;Mild in 18 women (9.3%;9.6 + 2.9) and 30 men (15.5%;8.8 + 2.8);Moderate in 6 women (3.1%;20.9 + 5.2) and 27 men (13.9%;21.4 + 4.4);and Severe in 7 women (3.6%;53.2 + 11.5) and 22 men (11.4%;50.2 + 17.3). A positive correlation was observed both between BMI and the apnea-hypopnea index (AHI) (r=0.527;p=<0.01), as well as the BMI and oxygen desaturation index (ODI) (r=0.516;p=<0.01). There is an effect of gender (p<0.008) and age (p<0.001) on the AHI, but there is no interaction between the variables gender and age (p=0.936) A similar result was observed for the ODI, in which there is an effect of gender (p<0.009) and age (p<0.001), but not in the interaction between the two variables (p=0.948) BMI had an effect on the AHI (p<0.001) and ODI (p<0.001) but not in the interaction between BMI and gender (p=0.250 and p=0.223 respectively). Conclusion: High BMI implies higher values of AHI and ODI in the study population. Although there is an effect of gender and age on the AHI and ODI, an interaction between these two variables is not maintained when analyzed.