Obstructive sleep apnea–hypopnea syndrome (OSA) is being identified increasingly as an important health issue. It is typified by repeated episodes of upper airway collapse during sleep leading to occasional hypoxaemia, sleep fragmentation and poor sleep quality. OSA is also being considered as an independent risk factor for hypertension, diabetes and cardiovascular diseases, leading to increased multi-morbidity and mortality. Cluster analysis, a powerful statistical set of techniques, may help in investigating and classifying homogeneous groups of patients with similar OSA characteristics. This study aims to investigate the (possible) different groups of patients in an OSA population, and to analyse the relationships among the main clinical variables in each group to better understand the impact of OSA on patients. Starting from a well-characterized OSA population of 198 subjects afferent to our sleep centre, we identified three different communities of OSA patients. The first has a very severe disease [apnea–hypopnea index (AHI) = 65.91 ± 22.47] and sleep disorder has a strong impact on daily life: a low level of diurnal partial pressure of oxygen (PaO2) (77.39 ± 11.64 mmHg) and a high prevalence of hypertension (64%); the second, with less severe disease (AHI = 28.88 ± 17.13), in which sleep disorders seem to be less important for diurnal PaO2 and have a minimum impact on comorbidity; and the last with very severe OSA (AHI = 57.26 ± 15.09) but with a low risk of nocturnal hypoxaemia (T90 = 11.58 ± 8.54) and less sleepy (Epworth Sleepiness Scale 10.00 ± 4.77).

Characterization of obstructive sleep apnea–hypopnea syndrome (OSA) population by means of cluster analysis

Carpagnano G. E.;
2016-01-01

Abstract

Obstructive sleep apnea–hypopnea syndrome (OSA) is being identified increasingly as an important health issue. It is typified by repeated episodes of upper airway collapse during sleep leading to occasional hypoxaemia, sleep fragmentation and poor sleep quality. OSA is also being considered as an independent risk factor for hypertension, diabetes and cardiovascular diseases, leading to increased multi-morbidity and mortality. Cluster analysis, a powerful statistical set of techniques, may help in investigating and classifying homogeneous groups of patients with similar OSA characteristics. This study aims to investigate the (possible) different groups of patients in an OSA population, and to analyse the relationships among the main clinical variables in each group to better understand the impact of OSA on patients. Starting from a well-characterized OSA population of 198 subjects afferent to our sleep centre, we identified three different communities of OSA patients. The first has a very severe disease [apnea–hypopnea index (AHI) = 65.91 ± 22.47] and sleep disorder has a strong impact on daily life: a low level of diurnal partial pressure of oxygen (PaO2) (77.39 ± 11.64 mmHg) and a high prevalence of hypertension (64%); the second, with less severe disease (AHI = 28.88 ± 17.13), in which sleep disorders seem to be less important for diurnal PaO2 and have a minimum impact on comorbidity; and the last with very severe OSA (AHI = 57.26 ± 15.09) but with a low risk of nocturnal hypoxaemia (T90 = 11.58 ± 8.54) and less sleepy (Epworth Sleepiness Scale 10.00 ± 4.77).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/296344
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