Top

Published in:

28-12-2022 | Artificial Intelligence | Original Article

A Systematic and Universal Artificial Intelligence Screening Method for Oropharyngeal Dysphagia: Improving Diagnosis Through Risk Management

Authors: Alberto Martin-Martinez, Jaume Miró, Cristina Amadó, Francisco Ruz, Antonio Ruiz, Omar Ortega, Pere Clavé

Published in: Dysphagia | Issue 4/2023

Abstract

Oropharyngeal dysphagia (OD) is underdiagnosed and current screening is costly. We aimed: (a) to develop an expert system (ES) based on machine learning that calculates the risk of OD from the electronic health records (EHR) of all hospitalized older patients during admission, and (b) to implement the ES in a general hospital. In an observational, retrospective study, EHR and swallowing assessment using the volume-viscosity swallow test for OD were captured over 24 months in patients > 70 yr admitted to Mataró Hospital. We studied the predictive power for OD of 25,000 variables. ES was obtained using feature selection, the final prediction model was built with non-linear methods (Random Forest). The database included 2809 older patients (mean age 82.47 ± 9.33 yr), severely dependent (Barthel Index 47.68 ± 31.90), with multiple readmissions (4.06 ± 7.52); 75.76% had OD. The psychometrics of the ES built with a non-linear model were: Area under the ROC Curve of 0.840; sensitivity 0.940; specificity, 0.416; Positive Predictive Value 0.834; Negative Predictive Value 0.690; positive likelihood ratio (LH), 1.61 and negative LH, 0.146. The ES screens in 6 s all patients admitted to a 419-bed hospital, identifies patients at greater risk of OD, and shows the risk for OD in the clinician’s workstation. It is currently in use at our institution. Our ES provides accurate, systematic and universal screening for OD in real time during hospital admission of older patients, allowing the most appropriate diagnostic and therapeutic strategies to be selected for each patient.

United Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2019: Highlights (ST/ESA/SER.A/430). 2019.

International Statistical Classification of Diseases and Related Health Problems (ICD). Available from: https://www.who.int/standards/classifications/classification-of-diseases. Accessed 07 April 2022.

Baijens LWJ, Clavé P, Cras P, Ekberg O, Forster A, Kolb GF, et al. European society for swallowing disorders - European union geriatric medicine society white paper: oropharyngeal dysphagia as a geriatric syndrome. Clin Interv Aging. 2016;11:1403–28. https://doi.org/10.2147/CIA.S107750.CrossRefPubMedPubMedCentral

Carrión S, Cabré M, Monteis R, Roca M, Palomera E, Serra-Prat M, et al. Oropharyngeal dysphagia is a prevalent risk factor for malnutrition in a cohort of older patients admitted with an acute disease to a general hospital. Clin Nutr. 2015;34:436–42. https://doi.org/10.1016/j.clnu.2014.04.014.CrossRefPubMed

Clavé P, Shaker R. Dysphagia: current reality and scope of the problem. Nat Rev Gastroenterol Hepatol. 2015;12(5):259–70. https://doi.org/10.1038/nrgastro.2015.49.CrossRefPubMed

Espinosa-Val C, Martín-Martínez A, Graupera M, Arias O, Elvira A, Cabré M, et al. Prevalence, risk factors, and complications of oropharyngeal dysphagia in older patients with dementia. Nutrients. 2020;12(3):863. https://doi.org/10.3390/nu12030863.CrossRefPubMedPubMedCentral

Almirall J, Rofes L, Serra-Prat M, Icart R, Palomera E, Arreola V, et al. Oropharyngeal dysphagia is a risk factor for community-acquired pneumonia in the elderly. Eur Respir J. 2013;41:923–8. https://doi.org/10.1183/09031936.00019012.CrossRefPubMed

Marin S, Serra-Prat M, Ortega O, AudouardFericgla M, Valls J, Palomera E, et al. Healthcare costs of post-stroke oropharyngeal dysphagia and its complications: malnutrition and respiratory infections. Eur J Neurol. 2021;28:3670–81. https://doi.org/10.1111/ene.14998.CrossRefPubMed

Leow LP, Huckabee ML, Anderson T, Beckert L. The impact of dysphagia on quality of life in ageing and parkinson’s disease as measured by the swallowing quality of life (SWAL-QOL) questionnaire. Dysphagia. 2010;25:216–20. https://doi.org/10.1007/s00455-009-9245-9.CrossRefPubMed

10.

Sinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R. Diabetes and global ageing among 65–99-year-old adults: findings from the international diabetes federation diabetes atlas 9th edition. Diabetes Res Clin Pract. 2020;162:108078. https://doi.org/10.1016/j.diabres.2020.108078.CrossRefPubMed

11.

Robbins J, Langmore S, Hind JA, Erlichman M. Dysphagia research in the 21st century and beyond: proceedings from Dysphagia Experts Meeting, August 21, 2001. J Rehabil Res Dev. 2002;39:543–7.PubMed

12.

Attrill S, White S, Murray J, Hammond S, Doeltgen S. Impact of oropharyngeal dysphagia on healthcare cost and length of stay in hospital: a systematic review. BMC Health Serv Res. 2018. https://doi.org/10.1186/s12913-018-3376-3.CrossRefPubMedPubMedCentral

13.

Belafsky PC, Mouadeb DA, Rees CJ, Pryor JC, Postma GN, Allen J, Leonard RJ. Validity and reliability of the eating assessment tool (EAT-10). Ann Otol Rhinol Laryngol. 2008;117(12):919–24. https://doi.org/10.1177/000348940811701210.CrossRefPubMed

14.

Rofes L, Arreola V, Mukherjee R, Swanson J, Clavé P. The effects of a xanthan gum-based thickener on the swallowing function of patients with dysphagia. Aliment Pharmacol Ther. 2014;39:1169–79. https://doi.org/10.1111/apt.12696.CrossRefPubMed

15.

Wallace KL, Middleton S, Cook IJ. Development and validation of a self-report symptom inventory to assess the severity of oral-pharyngeal dysphagia. Gastroenterology. 2000;118(4):678–87. https://doi.org/10.1016/s0016-5085(00)70137-5.CrossRefPubMed

16.

Cohen JT, Manor Y. Swallowing disturbance questionnaire for detecting dysphagia. Laryngoscope. 2011;121:1383–7. https://doi.org/10.1002/lary.21839.CrossRefPubMed

17.

Rofes L, Arreola V, Mukherjee R, Clavé P. Sensitivity and specificity of the eating assessment tool and the volume-viscosity swallow test for clinical evaluation of oropharyngeal dysphagia. Neurogastroenterol Motil. 2014;26:1256–65. https://doi.org/10.1111/nmo.12382.CrossRefPubMedPubMedCentral

18.

Kertscher B, Speyer R, Palmieri M, Plant C. Bedside screening to detect oropharyngeal dysphagia in patients with neurological disorders: an updated systematic review. Dysphagia. 2014;29:204–12. https://doi.org/10.1007/s00455-013-9490-9.CrossRefPubMed

19.

Abu-Ghanem S, Chen S, Amin MR. Oropharyngeal dysphagia in the elderly: evaluation and prevalence. Curr Otorhinolaryngol Rep. 2020. https://doi.org/10.1007/s40136-020-00258-xGERIATRIC.CrossRef

20.

Speyer R. Oropharyngeal dysphagia: screening and assessment. Otolaryngol Clin North Am. 2013;46:989–1008. https://doi.org/10.1016/j.otc.2013.08.004.CrossRefPubMed

21.

Clavé P, Arreola V, Romea M, Medina L, Palomera E, Serra-Prat M. Accuracy of the volume-viscosity swallow test for clinical screening of oropharyngeal dysphagia and aspiration. Clin Nutr. 2008;27:806–15. https://doi.org/10.1016/j.clnu.2008.06.011.CrossRefPubMed

22.

Riera SA, Marin S, Serra-Prat M, Tomsen N, Arreola V, Ortega O, et al. A systematic and a scoping review on the psychometrics and clinical utility of the volume-viscosity swallow test (V-vst) in the clinical screening and assessment of oropharyngeal dysphagia. 2021. Foods. https://doi.org/10.3390/foods10081900.CrossRefPubMedPubMedCentral

23.

Rosenbek JC, Robbins JA, Roecker EB, Coyle JL, Wood JL. A penetration-aspiration Scale dysphagia. Dysphagia. 1996. https://doi.org/10.1007/BF00417897.CrossRefPubMed

24.

Logemann JA. Dysphagia: evaluation and treatment. Folia Phoniatr Logop. 1995;47(3):140–64. https://doi.org/10.1159/000266348.CrossRefPubMed

25.

Langmore SE. Evaluation of oropharyngeal dysphagia: which diagnostic tool is superior? Curr Opin Otolaryngol Head Neck Surg. 2003. https://doi.org/10.1097/00020840-200312000-00014.CrossRefPubMed

26.

Barczi SR, Sullivan PA, Robbins J. How should dysphagia care of older adults differ? establishing optimal practice patterns. Semin Speech Lang. 2000;21(4):347–61. https://doi.org/10.1055/s-2000-8387.CrossRefPubMed

27.

Cohen SM, Lekan D, Risoli T, Lee HJ, Misono S, Whitson HE, et al. Association between dysphagia and inpatient outcomes across frailty level among patients ≥ 50 years of age. Dysphagia Springer. 2020;35:787–97. https://doi.org/10.1007/s00455-019-10084-z.CrossRef

28.

Cabré M, Serra-Prat M, Force L, Almirall J, Palomera E, Clavé P. Oropharyngeal dysphagia is a risk factor for readmission for pneumonia in the very elderly persons: observational prospective study. J Gerontol - Ser A Biol Sci Med Sci. 2014;69:330–7. https://doi.org/10.1093/gerona/glt099.CrossRef

29.

Rofes L, Arreola V, Romea M, Palomera E, Almirall J, Cabré M, et al. Pathophysiology of oropharyngeal dysphagia in the frail elderly. Neurogastroenterol Motil. 2010. https://doi.org/10.1111/j.1365-2982.2010.01521.x.CrossRefPubMed

30.

Tomsen N, Ortega O, Nascimento W, Carrión S, Clavé P. Oropharyngeal dysphagia in older people is associated with reduced pharyngeal sensitivity and low substance P and CGRP concentration in saliva. Dysphagia. 2022;37:48–57. https://doi.org/10.1007/s00455-021-10248-w.CrossRefPubMed

31.

Rofes L, Ortega O, Vilardell N, Mundet L, Clavé P. Spatiotemporal characteristics of the pharyngeal event-related potential in healthy subjects and older patients with oropharyngeal dysfunction. Neurogastroenterol Motil. 2017;29:1–11. https://doi.org/10.1111/nmo.12916.CrossRefPubMed

32.

Muhle P, Wirth R, Glahn J, Dziewas R. Schluckstörungen im alter: physiologie und pathophysiologie. Nervenarzt Springer Verlag. 2015;86:440–51. https://doi.org/10.1007/s00115-014-4183-7.CrossRef

33.

Arreola V, Vilardell N, Ortega O, Rofes L, Muriana D, Palomeras E, et al. Natural history of swallow function during the three-month period after stroke. Geriatr. 2019. https://doi.org/10.3390/geriatrics4030042.CrossRef

34.

Cabib C, Ortega O, Vilardell N, Mundet L, Clavé P, Rofes L. Chronic post-stroke oropharyngeal dysphagia is associated with impaired cortical activation to pharyngeal sensory inputs. Eur J Neurol. 2017;24:1355–62. https://doi.org/10.1111/ene.13392.CrossRefPubMed

35.

Rofes L, Muriana D, Palomeras E, Vilardell N, Palomera E, Alvarez-Berdugo D, et al. Prevalence, risk factors and complications of oropharyngeal dysphagia in stroke patients: a cohort study. Neurogastroenterol Motil. 2018. https://doi.org/10.1111/nmo.13338.CrossRefPubMed

36.

Serra-Prat M, Palomera M, Gomez C, Sar-Shalom D, Saiz A, Montoya JG, et al. Oropharyngeal dysphagia as a risk factor for malnutrition and lower respiratory tract infection in independently living older persons: a population-based prospective study. Age Ageing. 2012;41:376–81. https://doi.org/10.1093/ageing/afs006.CrossRefPubMed

37.

Carrión S, Cabré M, Monteis R, Roca M, Palomera E, Serra-Prat M, et al. Oropharyngeal dysphagia is a prevalent risk factor for malnutrition in a cohort of older patients admitted with an acute disease to a general hospital. Clin Nutr Churchill Livingstone. 2015;34:436–42. https://doi.org/10.1016/j.clnu.2014.04.014.CrossRef

38.

Miarons Font M, Rofes SL. Antipsychotic medication and oropharyngeal dysphagia: systematic review. Eur J Gastroenterol Hepatol. 2017;29(12):1332–9. https://doi.org/10.1097/MEG.0000000000000983.CrossRefPubMed

39.

Harrison JE, Weber S, Jakob R, Chute CG. ICD-11: an international classification of diseases for the twenty-first century. BMC Med Inform Decis Mak. 2021. https://doi.org/10.1186/s12911-021-01534-6.CrossRefPubMedPubMedCentral

40.

WHO. Anatomical Therapeutic Chemical (ATC) Classification [Internet]. Available from: https://www.who.int/tools/atc-ddd-toolkit/atc-classification. Accessed 14 february 2022

41.

Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 2019;62(1):15–25.CrossRef

42.

Mitchell TM. Machine Learning. New York: MCGraw-Hill; 1997.

43.

Du CJ, Sun DW. Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng. 2006;72:39–55.CrossRef

44.

Chowdhury GG. Natural language processing. Annu. Rev. Inf. Sci. Technol. 2003. Available from: http://eprints.cdlr.strath.ac.uk/2611/

45.

Tibau X-A, Reimers C, Requena-Mesa C, Runge J. Spatio-temporal Autoencoders in Weather and Climate Research. Deep Learn Earth Sci A Compr Approach Remote Sens, Climate Sci Geosci. 2021. https://doi.org/10.1002/9781119646181.ch13.CrossRef

46.

Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online. 2014;13:1–9. https://doi.org/10.1186/1475-925X-13-94.CrossRef

47.

Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J. 2020. https://doi.org/10.1183/13993003.01104-2020.CrossRefPubMedPubMedCentral

48.

Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323–37. https://doi.org/10.1016/j.jclinepi.2004.10.012.CrossRefPubMed

49.

Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66:149–53. https://doi.org/10.1093/cid/cix731.CrossRefPubMed

50.

Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16(8):440–56.CrossRefPubMed

51.

Gultepe E, Green JP, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Informatics Assoc. 2014;21:315–25. https://doi.org/10.1136/amiajnl-2013-001815.CrossRef

52.

Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning Data Mining, Inference, and Prediction. New York: Springer Series in Statistics; 2009.

53.

Seber GA, Lee AJ. Linear regression analysis. 2nd ed. New York: John Wiley & Sons; 2012.

54.

Goutam S. Applying logistic regression model to the examination results data. J Reliab Stat Stud. 2011;4(2):105–17.

55.

Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.CrossRef

56.

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. npj Digit Med. 2018. https://doi.org/10.1038/s41746-018-0029-1.CrossRefPubMedPubMedCentral

57.

Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff Project HOPE. 2014;33:1123–31. https://doi.org/10.1377/hlthaff.2014.0041.CrossRef

58.

Gotz D, Borland D. Data-driven healthcare: challenges and opportunities for interactive visualization. IEEE Comput Graph Appl. 2016;36(3):90–6. https://doi.org/10.1109/MCG.2016.59.CrossRefPubMed

59.

Svetnik V, Liaw A, Tong C, Wang T. LNCS 3077 - Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules. Springer, New York: International workshop on multiple Classifier systems; 2004.

60.

Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982. https://doi.org/10.1148/radiology.143.1.7063747.CrossRefPubMed

61.

Pepe PMS, M. S. The statistical evaluation of medical tests for classification and prediction. USA: Oxford University Press; 2003.

62.

Hardesty LA, Klym AH, Shindel BE, Chough DM, Sumkin JH, Gur D. Is maximum positive predictive value a good indicator of an optimal screening mammography practice? AJR Am J Roentgenol. 2005;184(5):1505–7. https://doi.org/10.2214/ajr.184.5.01841505.CrossRefPubMed

63.

Deeks JJ, Altman DG. Diagnostic tests 4: likelihood ratios. BMJ. 2004;329(7458):168–9. https://doi.org/10.1136/bmj.329.7458.168.CrossRefPubMedPubMedCentral

64.

Delgado R, Tibau XA. Why Cohen’s kappa should be avoided as performance measure in classification. PLoS ONE. 2019;14(9): e0222916. https://doi.org/10.1371/journal.pone.0222916.CrossRefPubMedPubMedCentral

65.

TicSalut [Internet]. [cited 2022 May 5]. Available from: https://ticsalutsocial.cat/dpd-salut/eina-dpia/

66.

Martín A, Ortega O, Roca M, Arús M, Clavé P. Effect of a minimal-massive intervention in hospitalized older patients with oropharyngeal dysphagia: a proof of concept study. J Nutr Health Aging. 2018;22(6):739–47. https://doi.org/10.1007/s12603-018-1043-3.CrossRefPubMed

67.

Tuomanen EI, Austrian R, Masure HR. Pathogenesis of pneumococcal infection. N Engl J Med. 1995;332(19):1280–4. https://doi.org/10.1056/NEJM199505113321907.CrossRefPubMed

68.

Almirall J, Cabré M, Clavé P. Neumonía aspirativa [Aspiration pneumonia]. Med Clin. 2007;129(11):424–32. https://doi.org/10.1157/13110467.CrossRef

69.

Plowman EK, Tabor LC, Robison R, Gaziano J, Dion C, Watts SA, Vu T, Gooch C. Discriminant ability of the eating assessment Tool-10 to detect aspiration in individuals with amyotrophic lateral sclerosis. Neurogastroenterol Motil. 2016;28(1):85–90. https://doi.org/10.1111/nmo.12700. (Epub 2015 Oct 28).CrossRefPubMed

70.

Lienhart AM, Kramer D, Jauk S, Gugatschka M, Leodolter W, Schlegl T. Multivariable risk prediction of dysphagia in hospitalized patients using machine learning. Stud Health Technol Inform. 2020. https://doi.org/10.3233/SHTI200071.CrossRefPubMed

71.

Martino R, Foley N, Bhogal S, Diamant N, Speechley M, Teasell R. Dysphagia after stroke: Incidence, diagnosis, and pulmonary complications. Stroke. 2005;36:2756–63. https://doi.org/10.1161/01.STR.0000190056.76543.eb.CrossRefPubMed

Title: A Systematic and Universal Artificial Intelligence Screening Method for Oropharyngeal Dysphagia: Improving Diagnosis Through Risk Management
Authors: Alberto Martin-Martinez
Jaume Miró
Cristina Amadó
Francisco Ruz
Antonio Ruiz
Omar Ortega
Pere Clavé
Publication date: 28-12-2022
Publisher: Springer US
Keywords: Artificial Intelligence
Dysphagia
Dysphagia
Dysphagia
Published in: Dysphagia / Issue 4/2023
Print ISSN: 0179-051X
Electronic ISSN: 1432-0460
DOI: https://doi.org/10.1007/s00455-022-10547-w

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A Systematic and Universal Artificial Intelligence Screening Method for Oropharyngeal Dysphagia: Improving Diagnosis Through Risk Management

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 4/2023

Effect of the Effortful Swallow on Pharyngeal Cavity Volume: Kinematic Analysis in Three Dimensions Using 320-Row Area Detector Computed Tomography

Relating Physiologic Swallowing Impairment, Functional Swallowing Ability, and Swallow-Specific Quality of Life

Ultrasonography as Biofeedback to Increase Muscle Activation During the Mendelsohn Maneuver in Healthy Adults

Translation and Validation of the Dysphagia Handicap Index in Polish-Speaking Patients

A Prospective, Longitudinal and Exploratory Study of Head and Neck Lymphoedema and Dysphagia Following Chemoradiotherapy for Head and Neck Cancer

Effect of Modified Pharyngeal Electrical Stimulation on Patients with Severe Chronic Neurogenic Dysphagia: A Single-Arm Prospective Study