OSA patients not treated with PAP - Evolution over 5 years according to the Baveno classification and cardiovascular outcomes

Autor: Daniela de Oliveira Werneck Rodrigues, R.J. Carneiro, M. van Zeller, Mariana Serino, Marta Drummond, Joana Ferra, Catarina Cardoso, Filipa Aguiar, Maria J. Redondo
Rok vydání: 2021
Předmět:
Zdroj: Sleep Medicine. 88:1-6
ISSN: 1389-9457
DOI: 10.1016/j.sleep.2021.09.010
Popis: Introduction The evolution of patients with obstructive sleep apnea (OSA) non-eligible for PAP-therapy at diagnosis is unknown. Currently, the severity of OSA is based on the apnea-hypopnea index (AHI), but its prognostic relevance has raised concerns. The Baveno classification may allow a better stratification of severity and therapeutic guidance in OSA. Methods Patients with AHI≥5/h in 2015, classified into Baveno groups A and B and non-eligible for PAP therapy at diagnosis and over 5 years, were analyzed. Patients were reclassified into Baveno groups (A-D) and changes in groups over 5 years were explored. Patients in Baveno groups C and D, who developed major cardiovascular comorbidities (CVC) or end-organ damage (EOD group), were compared with patients in Baveno groups A and B (non-EOD group). To identify predictors of the development of major CVC or EOD, a logistic regression analysis was performed. Results There were 76 patients, 58% male, mean age 51.9 ± 10.1 years, mean body mass index (BMI) of 30.3 ± 5.0 kg/m2 and median AHI of 8.9 (5.9–12.0) events/h. At diagnosis, 46% and 54% of patients were classified into Baveno group A and group B, respectively. In total, 21% of patients developed major CVC or EOD (Baveno group C or D); higher age (p = 0.011) and BMI (p = 0.004) and a higher percentage of central apneas (p = 0.012) at diagnosis significantly predicted it, while sex, sleepiness, insomnia, AHI, ODI and T90 were not. Conclusions A significant percentage of patients non-eligible for PAP-therapy at diagnosis of OSA developed CVC or EOD; higher age and BMI and a higher percentage of central apneas were significant predictors.
Databáze: OpenAIRE