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Two-step cluster analysis was performed to detect distinct clusters using sleep perception variables including discrepancy in total sleep time (TST), sleep onset latency (SOL), and wakefulness after sleep onset (WASO); objective TST, SOL, and WASO; and sleep quality. One-way analysis of variance or chi-squared tests were used to compare clinical and PSG characteristics between clusters.\u00a0Binary logistic regression analyses were used to explore factors correlated with poor sleep quality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A total of 1118 patients were included (81.6% men) with mean\u2009age\u00a0\u00b1\u2009SD 43.3\u00a0\u00b1 13.1 years, Epworth sleepiness score,\u00a05.7\u00a0\u00b1 4.4, and insomnia severity index 3.0\u2009\u00b1\u20092.4. Five distinct OSA clusters were identified: cluster 1 (<jats:italic>n<\/jats:italic>\u2009=\u2009254), underestimated TST; cluster 2 (<jats:italic>n<\/jats:italic>\u2009=\u2009158), overestimated TST; cluster 3 (<jats:italic>n<\/jats:italic>\u2009=\u2009169), overestimated SOL; cluster 4 (<jats:italic>n<\/jats:italic>\u2009=\u2009155), normal sleep discrepancy and poor sleep quality; and cluster 5 (<jats:italic>n<\/jats:italic>\u2009=\u2009382), normal sleep discrepancy and good sleep quality. Patients in cluster 2 were older, more commonly had hypertension, and had the lowest apnea\u2013hypopnea index and oxygen desaturation index. Age and sleep efficiency were correlated with poor sleep quality in clusters 1, 2, and 5, and also AHI in cluster 2.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Subgroups of patients with OSA have different patterns of sleep perception and quality that may help us to further understand the characteristics of sleep perception in OSA and provide clues for personalized treatment.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11325-023-02786-4","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T14:02:50Z","timestamp":1677592970000},"page":"1829-1837","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Clinical phenotypes of obstructive sleep apnea: a cluster analysis based on sleep perception and sleep quality"],"prefix":"10.1007","volume":"27","author":[{"given":"Huasheng","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taomei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangdong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"2786_CR1","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.2147\/nss.s275252","volume":"12","author":"W Cao","year":"2020","unstructured":"Cao W, Luo J, Xiao Y (2020) A review of current tools used for evaluating the severity of obstructive sleep apnea. 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