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Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort

Yassen, A. (author)
Coboeken, K. (author)
Bailly, S. (author)
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Burghaus, R. (author)
Buskova, J. (author)
Dogas, Z. (author)
Drummond, M. (author)
Gouveris, H. (author)
Joppa, P. (author)
Lippert, J. (author)
Lombardi, C. (author)
Mihaicuta, S. (author)
Pepin, J. L. (author)
Zou, Ding, 1970 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för invärtesmedicin och klinisk nutrition,Institute of Medicine, Department of Internal Medicine and Clinical Nutrition
Hedner, Jan A, 1953 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för invärtesmedicin och klinisk nutrition,Institute of Medicine, Department of Internal Medicine and Clinical Nutrition
Grote, Ludger, 1964 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för invärtesmedicin och klinisk nutrition,Institute of Medicine, Department of Internal Medicine and Clinical Nutrition
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 (creator_code:org_t)
2022-10-06
2022
English.
In: European Respiratory Journal Open Research (ERJ Open Research). - : European Respiratory Society (ERS). - 2312-0541. ; 8:4
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Introduction The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment. Method Prospective OSA patient data were recruited from 35 sleep clinics in 21 European countries. Unsupervised cluster analysis (anthropometrics, clinical variables) was performed in a random sample (n=5000). Subsequently, all patients were assigned to the clusters using a conditional inference tree classifier. Responses to PAP treatment change in apnoea severity and Epworth sleepiness scale (ESS) were assessed in relation to baseline patient clusters and at short- and long-term follow-up. Results At baseline, 20 164 patients were assigned (mean age 54.1 +/- 12.2 years, 73% male, median apnoea-hypopnoea index (AHI) 27.3 (interquartile range (IQR) 14.1-49.3) events.h(-1), and ESS 9.8 +/- 5.3) to seven distinct clusters based on anthropometrics, comorbidities and symptoms. At PAP follow-up (median 210 [IQR 134-465] days), the observed AHI reduction (n=1075) was similar, whereas the ESS response (n=3938) varied: largest reduction in cluster 3 (young healthy symptomatic males) and 6 (symptomatic males with psychiatric disorders, -5.0 and -5.1 units, respectively (all p<0.01), limited reduction in clusters 2 (obese males with systemic hypertension) and 5 (elderly multimorbid obese males, -4.2 (p<0.05) and -3.7 (p<0.001), respectively). Residual sleepiness in cluster 5 was particularly evident at long-term follow-up (p<0.05). Conclusion OSA patients can be classified into clusters based on clinically identifiable features. Importantly, these clusters may be useful for prediction of both short- and long-term responses to PAP intervention.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Keyword

cardiovascular events
phenotypes
cpap
adherence
disease
osa
Respiratory System

Publication and Content Type

ref (subject category)
art (subject category)

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