Predictors of obstructive sleep apnoea in patients admitted for acute coronary syndrome

[摘要] Identifying undiagnosed obstructive sleep apnoea (OSA) patients in cardiovascular clinics could improve their management. Aiming to build an OSA predictive model; a broad analysis of clinical variables was performed in a cohort of acute coronary syndrome (ACS) patients.Sociodemographic; anthropometric; life-style and pharmacological variables were recorded. Clinical measures included blood pressure; electrocardiography; echocardiography; blood count; troponin levels and a metabolic panel. OSA was diagnosed using respiratory polygraphy. Logistic regression models and classification and regression trees were used to create predictive models.A total of 978 patients were included (298 subjects with apnoeaxe2x80x93hypopnoea index (AHI) <15xe2x80x85eventsxc2xb7hxe2x88x921 and 680 with AHI xe2x89xa515xe2x80x85eventsxc2xb7hxe2x88x921). Age; BMI; Epworth sleepiness scale; peak troponin levels and use of calcium antagonists were the main determinants of AHI xe2x89xa515xe2x80x85eventsxc2xb7hxe2x88x921 (C statistic 0.71; sensitivity 94%; specificity 24%). Age; BMI; blood triglycerides; peak troponin levels and Killip class xe2x89xa5II were determinants of AHI xe2x89xa530xe2x80x85eventsxc2xb7hxe2x88x921 (C statistic of 0.67; sensitivity 31%; specificity 86%).Although a set of variables associated with OSA was identified; no model could successfully predict OSA in patients admitted for ACS. Given the high prevalence of OSA; the authors propose respiratory polygraphy as a to-be-explored strategy to identify OSA in ACS patients.

[学科分类] 呼吸医学