Gibson GJ (2004) Obstructive sleep apnoea syndrome: underestimated and undertreated. Br Med Bull 72(1):49–64. https://doi.org/10.1093/bmb/ldh044
Article CAS PubMed Google Scholar
Benjafield AV, Ayas NT, Eastwood PR et al (2019) Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med 7(8):687–698. https://doi.org/10.1016/S2213-2600(19)30198-5
Article PubMed PubMed Central Google Scholar
Patil SP, Schneider H, Schwartz AR, Smith PL (2007) Adult obstructive sleep apnea: pathophysiology and diagnosis. Chest 132(1):325–337. https://doi.org/10.1378/chest.07-0040
Shamsuzzaman ASM, Gersh BJ, Somers VK (2003) Obstructive sleep apnea implications for cardiac and vascular disease. JAMA 290(14):1906–1914. https://doi.org/10.1001/jama.290.14.1906
Article CAS PubMed Google Scholar
Marchi NA, Allali G, Heinzer R (2024) Obstructive sleep apnea, cognitive impairment, and dementia: is sleep microstructure an important feature? Sleep 47(12):zsae161. https://doi.org/10.1093/sleep/zsae161
Article PubMed PubMed Central Google Scholar
Gildeh N, Drakatos P, Higgins S, Rosenzweig I, Kent BD (2016) Emerging co-morbidities of obstructive sleep apnea: cognition, kidney disease, and cancer. J Thorac Dis 8(9):E901–E917. https://doi.org/10.21037/jtd.2016.09.23
Article PubMed PubMed Central Google Scholar
Tan BKJ, Teo YH, Tan NKW et al (2022) Association of obstructive sleep apnea and nocturnal hypoxemia with all-cancer incidence and mortality: a systematic review and meta-analysis. J Clin Sleep Med 18(5):1427–1440. https://doi.org/10.5664/jcsm.9772
Article PubMed PubMed Central Google Scholar
Teo YH, Tan BKJ, Tan NKW et al (2022) Obstructive sleep apnea and the incidence and mortality of gastrointestinal cancers: a systematic review and meta-analysis of 5,120,837 participants. J Gastrointest Oncol 13(6):2789–2798. https://doi.org/10.21037/jgo-22-153
Article PubMed PubMed Central Google Scholar
Cheong AJY, Tan BKJ, Teo YH et al (2022) Obstructive sleep apnea and lung cancer: a systematic review and meta-analysis. Ann Am Thorac Soc 19(3):469–475. https://doi.org/10.1513/AnnalsATS.202108-960OC
Tan BKJ, Tan NKW, Teo YH et al (2022) Association of obstructive sleep apnea with thyroid cancer incidence: a systematic review and meta-analysis. Eur Arch Otorhinolaryngol 279(11):5407–5414. https://doi.org/10.1007/s00405-022-07457-w
Laratta CR, Ayas NT, Povitz M, Pendharkar SR (2017) Diagnosis and treatment of obstructive sleep apnea in adults. CMAJ 189(48):E1481–E1488. https://doi.org/10.1503/cmaj.170296
Article PubMed PubMed Central Google Scholar
Pivetta B, Chen L, Nagappa M et al (2021) Use and performance of the STOP-Bang questionnaire for obstructive sleep apnea screening across geographic regions: a systematic review and meta-analysis. JAMA Netw Open 4(3):e211009. https://doi.org/10.1001/jamanetworkopen.2021.1009
Article PubMed PubMed Central Google Scholar
Qin H, Steenbergen N, Glos M et al (2021) The different facets of heart rate variability in obstructive sleep apnea. Front Psychiatry 12:642333. https://doi.org/10.3389/fpsyt.2021.642333
Article PubMed PubMed Central Google Scholar
Yılmaz B, Asyalı MH, Arıkan E, Yetkin S, Özgen F (2010) Sleep stage and obstructive apneaic epoch classification using single-lead ECG. BioMed Eng OnLine 9(1):39. https://doi.org/10.1186/1475-925X-9-39
Article PubMed PubMed Central Google Scholar
Kamga P, Mostafa R, Zafar S (2022) The use of wearable ECG devices in the clinical setting: a review. Curr Emerg Hosp Med Rep 10(3):67–72. https://doi.org/10.1007/s40138-022-00248-x
Article PubMed PubMed Central Google Scholar
Karasulu L, Dalar L, Sökücü S, Altın S (2012) Heart rate variability analysis of single-channel electrocardiogram can help to differentiate high-risk patients with obstructive sleep apnea syndrome - a study on diagnostic accuracy. Anadolu Kardiyol Derg 12(4):331–338. https://doi.org/10.5152/akd.2012.097
Tan BKJ, Gao EY, Tan NKW et al (2025) Machine listening for OSA diagnosis: a bayesian meta-analysis. Chest. https://doi.org/10.1016/j.chest.2025.04.006
Article PubMed PubMed Central Google Scholar
Gao EY, Tan BKJ, Tan NKW et al (2024) Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis. Sleep Breath 29(1):36. https://doi.org/10.1007/s11325-024-03173-3
Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 13(21):4994. https://doi.org/10.1136/bmj.n71
Harrison H, Griffin SJ, Kuhn I, Usher-Smith JA (2020) Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol 20(1):7. https://doi.org/10.1186/s12874-020-0897-3
Article PubMed PubMed Central Google Scholar
Whiting PF, Rutjes AWS, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Schünemann HJ, Mustafa RA, Brozek J et al (2020) GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy. J Clin Epidemiol 122:129–141. https://doi.org/10.1016/j.jclinepi.2019.12.020
Cerullo E, Sutton AJ, Jones HE, Wu O, Quinn TJ, Cooper NJ (2023) MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard. BMC Med Res Methodol 23(1):127. https://doi.org/10.1186/s12874-023-01910-y
Article PubMed PubMed Central Google Scholar
Debray TPA, Damen JAAG, Snell KIE et al (2017) A guide to systematic review and meta-analysis of prediction model performance. BMJ 356:i6460. https://doi.org/10.1136/bmj.i6460
Mizutani S, Zhou Y, Tian Y, Takagi T, Ohkubo T, Hattori S (2023) DTAmetasa : an R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias. Res Synth Methods 14(6):916–925. https://doi.org/10.1002/jrsm.1666
Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ (2019) Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Med Res Methodol 19(1):81. https://doi.org/10.1186/s12874-019-0724-x
Article PubMed PubMed Central Google Scholar
Patel A, Cooper N, Freeman S, Sutton A (2021) Graphical enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data. Res Synth Methods 12(1):34–44. https://doi.org/10.1002/jrsm.1439
Zhou Y, Huang A, Hattori S (2023) A likelihood-based sensitivity analysis for publication bias on the summary receiver operating characteristic in meta-analysis of diagnostic test accuracy. Stat Med 42(6):781–798. https://doi.org/10.1002/sim.9643
Carpenter B, Gelman A, Hoffman MD et al (2017) Stan: a probabilistic programming language. J Stat Soft 76(1):1–32. https://doi.org/10.18637/jss.v076.i01
Khandoker AH, Palaniswami M, Karmakar CK (2009) Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Trans Inf Technol Biomed 13(1):37–48. https://doi.org/10.1109/TITB.2008.2004495
Comments (0)