{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:46:22Z","timestamp":1792518382479,"version":"4.5.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013076","name":"National Major Science and Technology Projects of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013076","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ODI was strongly correlated with apnea-hypopnea index (AHI) (r\n                      <jats:sub>s<\/jats:sub>\n                      = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917\u20130.994), 0.962 (95%CI 0.943\u20130.981), and 0.976 (95%CI 0.956\u20130.996) for predicting AHI \u22655\/h, \u226515\/h, and \u226530\/h, respectively. We identified the cutoff of ODI 5\/h, 15\/h, and 25\/h, as having important predictive value for AHI \u22655\/h, \u226515\/h, and \u226530\/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904\u20130.978), 0.969 (95%CI 0.969\u20130.991), and 0.949 (95%CI 0.915\u20130.983) for predicting AHI \u22655\/h, \u226515\/h, and \u226530\/h, respectively. The sensitivity of ODI \u22655\/h, \u226515\/h, and \u226525\/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ODI is a sensitive and specific tool to predict SDB in patients with diabetes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2021.598470","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T01:58:31Z","timestamp":1615255111000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With 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