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ORIGINAL RESEARCH

Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness

ORCID Icon, , , , , , , , , , & show all
Pages 639-652 | Received 31 Jan 2024, Accepted 25 May 2024, Published online: 31 May 2024

References

  • Jordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. Lancet. 2014;383:736–747. doi:10.1016/S0140-6736(13)60734-5
  • Gottlieb DJ, Punjabi NM. Diagnosis and management of obstructive sleep apnea: a review. JAMA. 2020;323:1389–1400. doi:10.1001/jama.2020.3514
  • Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7:687–698. doi:10.1016/S2213-2600(19)30198-5
  • Barateau L, Pizza F, Plazzi G, et al. Narcolepsy. J Sleep Res. 2022;31:e13631. doi:10.1111/jsr.13631
  • Sansa G, Iranzo A, Santamaria J. Obstructive sleep apnea in narcolepsy. Sleep Med. 2010;11:93–95. doi:10.1016/j.sleep.2009.02.009
  • Frauscher B, Ehrmann L, Mitterling T, et al. Delayed diagnosis, range of severity, and multiple sleep comorbidities: a clinical and polysomnographic analysis of 100 patients of the Innsbruck Narcolepsy Cohort. J Clin Sleep Med. 2013;9:805–812. doi:10.5664/jcsm.2926
  • Pataka AD, Frangulyan RR, Mackay TW, et al. Narcolepsy and sleep-disordered breathing. Eur J Neurol. 2012;19:696–702. doi:10.1111/j.1468-1331.2011.03610.x
  • Thorpy MJ, Krieger AC. Delayed diagnosis of narcolepsy: characterization and impact. Sleep Med. 2014;15:502–507. doi:10.1016/j.sleep.2014.01.015
  • Sureshbabu S, Asranna A, Peter S, et al. Secondary narcolepsy masquerading as obstructive sleep apnea. Ann Indian Acad Neurol. 2019;22:537–538. doi:10.4103/aian.AIAN_19_19
  • Fietze I, Laharnar N, Bargiotas P, et al. Management of obstructive sleep apnea in Europe - A 10-year follow-up. Sleep Med. 2022;97:64–72. doi:10.1016/j.sleep.2022.06.001
  • Ferreira-Santos D, Amorim P, Silva Martins T, et al. Enabling early obstructive sleep apnea diagnosis with machine learning: systematic review. J Med Internet Res. 2022;24:e39452. doi:10.2196/39452
  • Ding Y, Sun Y, Li Y, et al. Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning. J Clin Sleep Med. 2022;18:2663–2672. doi:10.5664/jcsm.9798
  • Holfinger SJ, Lyons MM, Keenan BT, et al. Diagnostic Performance Of Machine Learning-Derived OSA prediction tools in large clinical and community-based samples. Chest. 2022;161:807–817. doi:10.1016/j.chest.2021.10.023
  • Zhang Y, Wang S, Hermann A, et al. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disord. 2021;279:1–8. doi:10.1016/j.jad.2020.09.113
  • Zhang X, Bellolio MF, Medrano-Gracia P, et al. Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Med Inform Decis Mak. 2019;19:287. doi:10.1186/s12911-019-1006-6
  • Hu G, Yuan N, Pan Y, et al. Electroclinical features of sleep-related head jerk. Nat Sci Sleep. 2021;13:2113–2123. doi:10.2147/NSS.S331893
  • Berry RB, Budhiraja R, Gottlieb DJ, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2012;8:597–619. doi:10.5664/jcsm.2172
  • Zucconi M, Ferri R, Allen R, et al. The official world association of sleep medicine (WASM) standards for recording and scoring periodic leg movements in sleep (PLMS) and wakefulness (PLMW) developed in collaboration with a task force from the International Restless Legs Syndrome Study Group (IRLSSG). Sleep Med. 2006;7:175–183. doi:10.1016/j.sleep.2006.01.001
  • Chang TH, Stuart EA. Propensity score methods for observational studies with clustered data: a review. Stat Med. 2022;41:3612–3626. doi:10.1002/sim.9437
  • Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for Trees. Nat Mach Intell. 2020;2:56–67. doi:10.1038/s42256-019-0138-9
  • Sahni AS, Carlucci M, Malik M, et al. Management of excessive sleepiness in patients with narcolepsy and OSA: current challenges and future prospects. Nat Sci Sleep. 2019;11:241–252. doi:10.2147/NSS.S218402
  • Filardi M, Demir N, Pizza F, et al. Prevalence and neurophysiological correlates of sleep disordered breathing in pediatric type 1 narcolepsy. Sleep Med. 2020;65:8–12. doi:10.1016/j.sleep.2019.07.004
  • Asadikia A, Rajabifard A, Kalantari M. Region-income-based prioritisation of sustainable development goals by gradient boosting machine. Sustain Sci. 2022;17:1939–1957. doi:10.1007/s11625-022-01120-3
  • Hawkins DM, Basak SC, Mills D. Assessing model fit by cross-validation. J Chem Inf Comput Sci. 2003;43:579–586. doi:10.1021/ci025626i
  • Dauvilliers Y, Montplaisir J, Molinari N, et al. Age at onset of narcolepsy in two large populations of patients in France and Quebec. Neurology. 2001;57:2029–2033. doi:10.1212/wnl.57.11.2029
  • Zhang Y, Ren R, Yang L, et al. Polysomnographic nighttime features of narcolepsy: a systematic review and meta-analysis. Sleep Med Rev. 2021;58:101488. doi:10.1016/j.smrv.2021.101488
  • Sateia MJ. International classification of sleep disorders-third edition: highlights and modifications. Chest. 2014;146:1387–1394. doi:10.1378/chest.14-0970
  • Roche J, Gillet V, Perret F, et al. Obstructive sleep apnea and sleep architecture in adolescents with severe obesity: effects of a 9-month lifestyle modification program based on regular exercise and a balanced diet. J Clin Sleep Med. 2018;14:967–976. doi:10.5664/jcsm.7162
  • Walter LM, Nixon GM, Davey MJ, et al. Sleep disturbance in pre-school children with obstructive sleep apnoea syndrome. Sleep Med. 2011;12:880–886. doi:10.1016/j.sleep.2011.07.007
  • Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy. 2020;23. doi:10.3390/e23010018
  • Rosenberg R, Hirshkowitz M, Rapoport DM, et al. The role of home sleep testing for evaluation of patients with excessive daytime sleepiness: focus on obstructive sleep apnea and narcolepsy. Sleep Med. 2019;56:80–89. doi:10.1016/j.sleep.2019.01.014
  • Caples SM, Anderson WM, Calero K, et al. Use of polysomnography and home sleep apnea tests for the longitudinal management of obstructive sleep apnea in adults: an American Academy of Sleep Medicine clinical guidance statement. J Clin Sleep Med. 2021;17:1287–1293. doi:10.5664/jcsm.9240
  • Zhou ZR, Wang WW, Li Y, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. 2019;7:796. doi:10.21037/atm.2019.08.63