Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

Introduction: Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea–hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization. Methods: Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016–2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range). Results: 981 subjects were studied: 41% females, age 56 years (45–66), overall AHI 23events/h (13–42) and body mass index (BMI) 30kg/m2 (27–34). Three PSG clusters were identified: Cluster 1: “Supine and obstructive apnea predominance” (433 patients, 44%). Cluster 2: “Central, REM and shorter-hypopnea predominance” (374 patients, 38%). Cluster 3: “Severe hypoxemic burden and higher wake after sleep onset” (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3. Conclusions: The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder. (AU)

Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach.

INTRODUCTION: Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization. METHODS: Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range). RESULTS: 981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m2 (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3. CONCLUSIONS: The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.

Telemedicine home CPAP titration and follow-up in the COVID-19 scenario.

Introduction: Continuous positive airway pressure (CPAP) titration was dramatically affected by the coronavirus disease 2019 (COVID-19) pandemic due to increased biological risk. This study aimed to compare successful CPAP adaptation and compliance with home telemedicine CPAP titration with the usual method based on face-to-face visits. Methodology: A prospective cohort using telemedicine home CPAP titration and follow-up during the COVID-19 pandemic (TC) was compared with a retrospective cohort receiving face-to-face pre-pandemic home titration (RC). The TC included a subgroup with a smartphone application (TC-APP). Successful CPAP adaptation and compliance at 1 month of follow-up were the main endpoints, while patient satisfaction and costs were secondary endpoints. Results: 210 consecutive patients were evaluated (80 RC and 130 TC). 36 patients were in the TC-APP subgroup. CPAP titration was successful in 90% in RC versus 95% in TC and 100% in TC-APP. No compliance differences between groups were found at 1  month (4.79 h·night-1 RC, 4.33  h·night-1 TC and 4.59 h·night-1 TC-APP). Mean±sd patient satisfaction out of 10 was 7.69±2.05 in RC versus 9.02±0.64 in TC (p<0.001). 64% of the TC-APP subgroup reported that their telemedicine strategy influenced an increase in CPAP use (p=0.011). CPAP adaptation with follow-up had an estimated direct staff cost per patient of EUR 19.61±8.61 in TC with no smartphone application used versus EUR 23.79±9.94 in TC-APP (p=0.048). Conclusions: Telemedicine in CPAP titration and early follow-up is equivalent to the usual care in terms of successful adaptation and compliance, while achieving greater patient satisfaction.