BACKGROUND: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnoea (OSA), but an evaluation of CPAP adherence is rarely carried out among patients with acute coronary syndrome (ACS). The goals of the study are to analyse long-term adherence and identify the predictors of non-compliance with CPAP treatment for patients with non-sleepy OSA and ACS. METHODS: This is an ancillary study of the ISAACC study, which is a multicentre, prospective, open-label, parallel, randomized, and controlled trial (NCT01335087) in patients with hospital admission for ACS. For the purpose of this study, only non-sleepy patients with moderate or severe OSA and randomized to receive CPAP treatment were analysed (n=357). Non-compliance was defined as CPAP dropout or average cumulative CPAP use of <4 hours/night. Multivariable logistic regression analysis was performed to identify predictors of CPAP adherence. RESULTS: Adherence to treatment was 35.3% at 12 months. According to the unadjusted analysis, higher apnoea-hypopnea index (AHI) (P<0.001) and oxygen desaturation index (ODI) (P=0.001) were associated with a lower risk of non-compliance. Multivariable logistic regression analysis showed that high AHI (P=0.0051), high amounts of smoking pack-year (P=0.0170), and long intensive care unit (ICU) stays (P=0.0263) were associated with lower odds of non-compliance. It also showed a significant interaction between ACS history and age (P=0.0131), such that young patients with their first ACS showed significantly lower odds of CPAP non-compliance than patients with recurrent ACS and significantly lower odds of CPAP non-compliance were associated with ageing only in patients with recurrent ACS. CONCLUSIONS: Protective factors against non-compliance with CPAP treatment in non-sleepy patients with ACS were illness severity (high values of AHI or ICU stay length) or smoking amount. Patients with no previous history of ACS showed lower odds of CPAP non-compliance than patients with a recurrent ACS with younger age.
BACKGROUND: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnoea (OSA), but an evaluation of CPAP adherence is rarely carried out among patients with acute coronary syndrome (ACS). The goals of the study are to analyse long-term adherence and identify the predictors of non-compliance with CPAP treatment for patients with non-sleepy OSA and ACS. METHODS: This is an ancillary study of the ISAACC study, which is a multicentre, prospective, open-label, parallel, randomized, and controlled trial (NCT01335087) in patients with hospital admission for ACS. For the purpose of this study, only non-sleepy patients with moderate or severe OSA and randomized to receive CPAP treatment were analysed (n=357). Non-compliance was defined as CPAP dropout or average cumulative CPAP use of <4 hours/night. Multivariable logistic regression analysis was performed to identify predictors of CPAP adherence. RESULTS: Adherence to treatment was 35.3% at 12 months. According to the unadjusted analysis, higher apnoea-hypopnea index (AHI) (P<0.001) and oxygen desaturation index (ODI) (P=0.001) were associated with a lower risk of non-compliance. Multivariable logistic regression analysis showed that high AHI (P=0.0051), high amounts of smoking pack-year (P=0.0170), and long intensive care unit (ICU) stays (P=0.0263) were associated with lower odds of non-compliance. It also showed a significant interaction between ACS history and age (P=0.0131), such that young patients with their first ACS showed significantly lower odds of CPAP non-compliance than patients with recurrent ACS and significantly lower odds of CPAP non-compliance were associated with ageing only in patients with recurrent ACS. CONCLUSIONS: Protective factors against non-compliance with CPAP treatment in non-sleepy patients with ACS were illness severity (high values of AHI or ICU stay length) or smoking amount. Patients with no previous history of ACS showed lower odds of CPAP non-compliance than patients with a recurrent ACS with younger age.
Authors: Megan R Crawford; Delwyn J Bartlett; Steven R Coughlin; Craig L Phillips; Alister M Neill; Colin A Espie; George C Dungan; John P H Wilding; Peter M A Calverley; Ronald R Grunstein; Nathaniel S Marshall Journal: Thorax Date: 2012-05-26 Impact factor: 9.139
Authors: Elliott M Antman; Daniel T Anbe; Paul Wayne Armstrong; Eric R Bates; Lee A Green; Mary Hand; Judith S Hochman; Harlan M Krumholz; Frederick G Kushner; Gervasio A Lamas; Charles J Mullany; Joseph P Ornato; David L Pearle; Michael A Sloan; Sidney C Smith; Joseph S Alpert; Jeffrey L Anderson; David P Faxon; Valentin Fuster; Raymond J Gibbons; Gabriel Gregoratos; Jonathan L Halperin; Loren F Hiratzka; Sharon Ann Hunt; Alice K Jacobs Journal: Circulation Date: 2004-08-03 Impact factor: 29.690
Authors: Colin Suen; Jean Wong; Clodagh M Ryan; Samuel Goh; Tiffany Got; Rabail Chaudhry; Douglas S Lee; Frances Chung Journal: J Clin Med Date: 2020-04-02 Impact factor: 4.241