Research article Special Issues

Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index

  • Received: 19 May 2022 Revised: 18 July 2022 Accepted: 21 July 2022 Published: 10 August 2022
  • Age, sex, and body mass index (BMI) were associated with obstructive sleep apnea (OSA). Although various methods have been used in OSA prediction, this study aimed to develop predictions using simple and general predictors incorporating machine learning algorithms. This single-center, retrospective observational study assessed the diagnostic relevance of age, sex, and BMI for OSA in a cohort of 9, 422 patients who had undergone polysomnography (PSG) between 2015 and 2020. The participants were randomly divided into training, testing, and independent validation groups. Multivariable logistic regression (LR) and artificial neural network (ANN) algorithms used age, sex, and BMI as predictors to develop risk-predicting models for moderate-and-severe OSA. The training-testing dataset was used to assess the model generalizability through five-fold cross-validation. We calculated the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the independent validation set to assess the performance of the model. The results showed that age, sex, and BMI were significantly associated with OSA. The validation AUCs of the generated LR and ANN models were 0.806 and 0.807, respectively. The independent validation set's accuracy, sensitivity, specificity, PPV, and NPV were 76.3%, 87.5%, 57.0%, 77.7%, and 72.7% for the LR model, and 76.4%, 87.7%, 56.9%, 77.7%, and 73.0% respectively, for the ANN model. The LR- and ANN-boosted models with the three simple parameters effectively predicted OSA in patients referred for PSG examination and improved insight into risk stratification for OSA diagnosis.

    Citation: Yi-Chun Kuan, Chien-Tai Hong, Po-Chih Chen, Wen-Te Liu, Chen-Chih Chung. Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11409-11421. doi: 10.3934/mbe.2022532

    Related Papers:

  • Age, sex, and body mass index (BMI) were associated with obstructive sleep apnea (OSA). Although various methods have been used in OSA prediction, this study aimed to develop predictions using simple and general predictors incorporating machine learning algorithms. This single-center, retrospective observational study assessed the diagnostic relevance of age, sex, and BMI for OSA in a cohort of 9, 422 patients who had undergone polysomnography (PSG) between 2015 and 2020. The participants were randomly divided into training, testing, and independent validation groups. Multivariable logistic regression (LR) and artificial neural network (ANN) algorithms used age, sex, and BMI as predictors to develop risk-predicting models for moderate-and-severe OSA. The training-testing dataset was used to assess the model generalizability through five-fold cross-validation. We calculated the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the independent validation set to assess the performance of the model. The results showed that age, sex, and BMI were significantly associated with OSA. The validation AUCs of the generated LR and ANN models were 0.806 and 0.807, respectively. The independent validation set's accuracy, sensitivity, specificity, PPV, and NPV were 76.3%, 87.5%, 57.0%, 77.7%, and 72.7% for the LR model, and 76.4%, 87.7%, 56.9%, 77.7%, and 73.0% respectively, for the ANN model. The LR- and ANN-boosted models with the three simple parameters effectively predicted OSA in patients referred for PSG examination and improved insight into risk stratification for OSA diagnosis.



    加载中


    [1] D. J. Eckert, A. Malhotra, Pathophysiology of adult obstructive sleep apnea, Proc. Am. Thorac. Soc., 5 (2008), 144–153. https://doi.org/10.1513/pats.200707-114MG doi: 10.1513/pats.200707-114MG
    [2] D. J. Gottlieb, N. M. Punjabi, Diagnosis and management of obstructive sleep apnea: A review, JAMA, 323 (2020), 1389–1400. https://doi.org/10.1001/jama.2020.3514 doi: 10.1001/jama.2020.3514
    [3] K. J. Ruskin, J. A. Caldwell, J. L. Caldwell, E. A. Boudreau, Screening for sleep apnea in morbidly obese pilots, Aerosp. Med. Hum. Perform., 86 (2015), 835–841. https://doi.org/10.3357/amhp.4163.2015 doi: 10.3357/amhp.4163.2015
    [4] C. V. Senaratna, J. L. Perret, C. J. Lodge, A. J. Lowe, B. E. Campbell, M. C. Matheson, et al., Prevalence of obstructive sleep apnea in the general population: A systematic review, Sleep Med. Rev., 34 (2017), 70–81. https://doi.org/10.1016/j.smrv.2016.07.002 doi: 10.1016/j.smrv.2016.07.002
    [5] A. G. Andrade, O. M. Bubu, A. W. Varga, R. S. Osorio, The relationship between obstructive sleep apnea and Alzheimer's disease, J. Alzheimers Disease, 64 (2018), S255–S270. https://doi.org/10.3233/jad-179936 doi: 10.3233/jad-179936
    [6] L. A. Salman, R. Shulman, J. B. Cohen, Obstructive sleep apnea, hypertension, and cardiovascular risk: Epidemiology, pathophysiology, and management, Curr. Cardiol. Rep., 22 (2020), 6. https://doi.org/10.1007/s11886-020-1257-y doi: 10.1007/s11886-020-1257-y
    [7] M. Knauert, S. Naik, M. B. Gillespie, M. Kryger, Clinical consequences and economic costs of untreated obstructive sleep apnea syndrome, World J. Otorhinolaryngology-Head Neck Surgery, 1 (2015), 17–27. https://doi.org/10.1016/j.wjorl.2015.08.001 doi: 10.1016/j.wjorl.2015.08.001
    [8] V. K. Kapur, D. H. Auckley, S. Chowdhuri, D. C. Kuhlmann, R. Mehra, K. Ramar, et al., Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: An american academy of sleep medicine clinical practice guideline, J. Clin. Sleep Med., 13 (2017), 479–504. https://doi.org/10.5664/jcsm.6506 doi: 10.5664/jcsm.6506
    [9] S. A. Stewart, R. Skomro, J. Reid, E. Penz, M. Fenton, J. Gjevre, et al., Improvement in obstructive sleep apnea diagnosis and management wait times: A retrospective analysis of home management pathway for obstructive sleep apnea, Can, Respir, J., 22 (2015), 167–170. https://doi.org/10.1155/2015/516580 doi: 10.1155/2015/516580
    [10] D. N. Polesel, K. T. Nozoe, L. Bittencourt, S. Tufik, M. L. Andersen, M. T. B. Fernandes, et al., Waist-to-height ratio and waist circumference as the main measures to evaluate obstructive sleep apnea in the woman's reproductive life stages, Women Health, 61 (2021), 277–288. https://doi.org/10.1080/03630242.2020.1862386 doi: 10.1080/03630242.2020.1862386
    [11] K.-H. Yu, A. L. Beam, I. S. Kohane, Artificial intelligence in healthcare, Nat. Biomed. Eng., 2 (2018), 719–731. https://doi.org/10.1038/s41551-018-0305-z doi: 10.1038/s41551-018-0305-z
    [12] T. Davenport, R. Kalakota, The potential for artificial intelligence in healthcare, Future Healthc. J., 6 (2019), 94–98. https://doi.org/10.7861/futurehosp.6-2-94 doi: 10.7861/futurehosp.6-2-94
    [13] J. C. Stoltzfus, Logistic regression: a brief primer, Acad. Emerg. Med., 18 (2011), 1099–1104. https://doi.org/10.1111/j.1553-2712.2011.01185.x doi: 10.1111/j.1553-2712.2011.01185.x
    [14] S. Agatonovic-Kustrin, R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharm. Biomed. Anal., 22 (2000), 717–727. https://doi.org/10.1016/S0731-7085(99)00272-1 doi: 10.1016/S0731-7085(99)00272-1
    [15] D. Álvarez, A. Cerezo-Hernández, A. Crespo, G. C. Gutiérrez-Tobal, F. Vaquerizo-Villar, V. Barroso-García, et al., A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow, Sci. Rep., 10 (2020), 5332. https://doi.org/10.1038/s41598-020-62223-4 doi: 10.1038/s41598-020-62223-4
    [16] R. B. Berry, R. Budhiraja, D. J. Gottlieb, D. Gozal, C. Iber, V. K. Kapur, 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., 8 (2012), 597–619. https://doi.org/10.5664/jcsm.2172 doi: 10.5664/jcsm.2172
    [17] F. Dankers, A. Traverso, L. Wee, S. M. J. van Kuijk, Prediction modeling methodology, In Fundamentals of Clinical Data Science, (eds. P. Kubben, M. Dumontier, A. Dekker), Springer, (2019), 101–120. https://doi.org/10.1007/978-3-319-99713-1_8
    [18] B. A. Edwards, A. Wellman, S. A. Sands, R. L. Owens, D. J. Eckert, D. P. White, et al., Obstructive sleep apnea in older adults is a distinctly different physiological phenotype, Sleep, 37 (2014), 1227–1236. https://doi.org/10.5665/sleep.3844 doi: 10.5665/sleep.3844
    [19] I. Fietze, N. Laharnar, A. Obst, R. Ewert, S. B. Felix, C. Garcia, et al., Prevalence and association analysis of obstructive sleep apnea with gender and age differences—Results of SHIP-trend, J. Sleep Res., 28 (2019), e12770. https://doi.org/10.1111/jsr.12770 doi: 10.1111/jsr.12770
    [20] F. O. Martins, S. V. Conde, Gender differences in the context of obstructive sleep apnea and metabolic diseases, Front. Physiol., 12 (2021), 792633. https://doi.org/10.3389/fphys.2021.792633 doi: 10.3389/fphys.2021.792633
    [21] C. M. Lin, T. M. Davidson, S. Ancoli-Israel, Gender differences in obstructive sleep apnea and treatment implications, Sleep Med. Rev., 12 (2008), 481–496. https://doi.org/10.1016/j.smrv.2007.11.003 doi: 10.1016/j.smrv.2007.11.003
    [22] L. Rezaie, S. Maazinezhad, D. J. Fogelberg, H. Khazaie, D. Sadeghi-Bahmani, S. Brand, Compared to individuals with mild to moderate Obstructive Sleep Apnea (OSA), individuals with severe OSA had higher BMI and respiratory-disturbance scores., Life (Basel), 11 (2021). https://doi.org/10.3390/life11050368
    [23] R. Huxley, S. Mendis, E. Zheleznyakov, S. Reddy, J. Chan, Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk—a review of the literature, Eur. J. Clin. Nutr., 64 (2010), 16–22. https://doi.org/10.1038/ejcn.2009.68 doi: 10.1038/ejcn.2009.68
    [24] C.-C. Chung, L. Chan, O. A. Bamodu, C.-T. Hong, H.-W. Chiu, Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death, Sci. Rep., 10 (2020), 20501. https://doi.org/10.1038/s41598-020-77546-5 doi: 10.1038/s41598-020-77546-5
    [25] C. C. Chung, W. T. Chiu, Y. H. Huang, L. Chan, C. T. Hong, H. W. Chiu, Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks, J. Neurol. Sci., 425 (2021), 117445. https://doi.org/10.1016/j.jns.2021.117445 doi: 10.1016/j.jns.2021.117445
    [26] W. T. Chiu, C. C. Chung, C. H. Huang, Y. S. Chien, C. H. Hsu, C. H. Wu, et al., Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks, J. Formos. Med. Assoc., 121 (2022), 490–499. https://doi.org/10.1016/j.jfma.2021.07.004 doi: 10.1016/j.jfma.2021.07.004
    [27] S.-Y. Chou, O. A. Bamodu, W.-T. Chiu, C.-T. Hong, L. Chan, C.-C. Chung, Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management, Sci. Rep., 12 (2022), 7254. https://doi.org/10.1038/s41598-022-11201-z doi: 10.1038/s41598-022-11201-z
    [28] Y. J. Kim, J. S. Jeon, S. E. Cho, K. G. Kim, S. G. Kang, Prediction models for obstructive sleep apnea in korean adults using machine learning techniques, Diagnostics (Basel), 11 (2021). https://doi.org/10.3390/diagnostics11040612 doi: 10.3390/diagnostics11040612
    [29] W. C. Huang, P. L. Lee, Y. T. Liu, A. A. Chiang, F. Lai, Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample, Sleep, 43 (2020). https://doi.org/10.1093/sleep/zsz295 doi: 10.1093/sleep/zsz295
    [30] D. E. Jonas, H. R. Amick, C. Feltner, R. P. Weber, M. Arvanitis, A. Stine, et al., Screening for obstructive sleep apnea in adults: Evidence report and systematic review for the US preventive services task force, JAMA, 317 (2017), 415–433. https://doi.org/10.1001/jama.2016.19635 doi: 10.1001/jama.2016.19635
    [31] S. Tsuiki, T. Nagaoka, T. Fukuda, Y. Sakamoto, F. R. Almeida, H. Nakayama, et al., Machine learning for image-based detection of patients with obstructive sleep apnea: An exploratory study, Sleep Breath, 25 (2021), 2297–2305. https://doi.org/10.1007/s11325-021-02301-7 doi: 10.1007/s11325-021-02301-7
    [32] L. Zhang, Y. R. Yan, S. Q. Li, H. P. Li, Y. N. Lin, N. Li, et al., Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: A cross-sectional study, BMJ Open, 11 (2021), e048482. https://doi.org/10.1136/bmjopen-2020-048482 doi: 10.1136/bmjopen-2020-048482
    [33] C. Y. Tsai, W. T. Liu, Y. T. Lin, S. Y. Lin, R. Houghton, W. H. Hsu, et al., Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile, Inform. Health Soc. Care, (2021), 1–16. https://doi.org/10.1080/17538157.2021.2007930 doi: 10.1080/17538157.2021.2007930
    [34] D. J. Sargent, Comparison of artificial neural networks with other statistical approaches, Cancer, 91 (2001), 1636–1642. https://doi.org/10.1002/1097-0142(20010415)91:8+<1636::AID-CNCR1176>3.0.CO;2-D doi: 10.1002/1097-0142(20010415)91:8+<1636::AID-CNCR1176>3.0.CO;2-D
    [35] F. S. Arkin, G. Aras, E. Dogu, Comparison of artificial neural networks and logistic regression for 30-days survival prediction of cancer patients, Acta Inform. Med., 28 (2020), 108–113. https://doi.org/10.5455/aim.2020.28.108-113 doi: 10.5455/aim.2020.28.108-113
    [36] B. Eftekhar, K. Mohammad, H. E. Ardebili, M. Ghodsi, E. Ketabchi, Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data, BMC Med. Inform. Decis. Mak., 5 (2005), 3. https://doi.org/10.1186/1472-6947-5-3 doi: 10.1186/1472-6947-5-3
    [37] C. C. Chung, O. A. Bamodu, C. T. Hong, L. Chan, H. W. Chiu, Application of machine learning-based models to boost the predictive power of the SPAN index, Int. J. Neurosci., (2021), 1–11. https://doi.org/10.1080/00207454.2021.1881092 doi: 10.1080/00207454.2021.1881092
    [38] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification models: A methodology review, J. Biomed. Inform., 35 (2002), 352–359. https://doi.org/10.1016/S1532-0464(03)00034-0 doi: 10.1016/S1532-0464(03)00034-0
    [39] P. E. Peppard, T. Young, J. H. Barnet, M. Palta, E. W. Hagen, K. M. Hla, Increased prevalence of sleep-disordered breathing in adults, Am. J. Epidemiol., 177 (2013), 1006–1014. https://doi.org/10.1093/aje/kws342 doi: 10.1093/aje/kws342
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1979) PDF downloads(200) Cited by(0)

Article outline

Figures and Tables

Figures(3)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog