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Classification of orthostatic intolerance through data analytics

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Abstract

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. We use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

Machine learning tools utilized to analyze heart rate (HR) and blood pressure (BP) time-series data from syncope and control patients. Results show that machine learning can provide accurate classification of disease groups for 98% of patients and we identified two subgroups within the control patients differentiated by their BP response.

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References

  1. Bartoletti A, Alboni P, Ammirati F, Brignole M, Del Rosso A, Manzillo G, Menozzi C, Raviele A, Sutton R (2000) The Italian protocol: a simplified head-up tilt testing potentiated with oral nitroglycerin to assess patients with unexplained syncope. Europace 2(4):339–342

    Article  CAS  PubMed  Google Scholar 

  2. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  3. Brennan M, Palaniswami M, Kamen P (2001) Do existing measures of poincaré plot geometry reflect nonlinear features of heart rate variability? Cureus 48(11):1342–1347

    CAS  Google Scholar 

  4. Brignole M (2007) Diagnosis and treatment of syncope. Heart 93(1):130–136

    Article  PubMed  PubMed Central  Google Scholar 

  5. Brignole M, Alboni P, Benditt D, Bergfeldt L, Blanc JJ, Thomsen PB, van Dijk J, Fitzpatrick A, Hohnloser S, Janousek J, Kapoor W, Kenny R, Kulakowski P, Masotti G, Moya A, Raviele A, Sutton R, Theodorakis G, Ungar A, Wieling W (2004) Guidelines on management (diagnosis and treatment) of syncope. Eurospace 6(6):467–573

    Article  Google Scholar 

  6. Brignole M, Moya A, de Lange F, Deharoa JC, Elliott M, Fanciulli A, Federowski A, Furlan R, Kenny R, Martin A, Probst V, Reed M, Rice C, Sutton R, Ungar A, van Dijk J (2018) 2018 ESC guidelines for the diagnosis and management of syncope. Eur Heart J 39(21):1883–1948

    Article  PubMed  Google Scholar 

  7. Brignole M, Ungar A, Casagranda I, Gulizia M, Lunati M, Ammirati F, Del Rosso A, Sasdelli M, Santini M, Maggi R, Vitale E, Morrione A, Francese G, Vecchi M, Giada F (2010) Syncope Unit (SUP) investigators: prospective multicentre systematic guideline-based management of patients referred to the syncope units of general hospitals. Europace 12(1):109–118

    Article  PubMed  Google Scholar 

  8. Cubera K, Stryjewski P, Kuczaj A, Nessler J, Nowalany-Kozielska E, Pytko-Polonzyk J (2017) The impact of gender on the frequency of syncope provoking factors and prodromal signs in patients with vasovagal syncope. Przeglad Lekarski 74(4):147–149

    PubMed  Google Scholar 

  9. Ferreira R, da Silva L (2016) The current indication for pacemaker in patients with cardioinhibitory vasovagal syncope. Open Cardiovasc Med J 10:179–187

    Article  Google Scholar 

  10. Fletcher J (2020) What to know about vasovagal syncope. https://www.medicalnewstoday.com/articles/327406

  11. Fu Q, Vangundy T, Galbreath M, Shibata S, Jain M, Hastings J, Bhella P, Levine B (2010) Cardiac origins of the postural orthostatic tachycardia syndrome. J Am Coll Cardiol 55(25):2858–2868

    Article  PubMed  PubMed Central  Google Scholar 

  12. Furlan R, Diedrich A, Rimoldi A, Palazzolo L, Porta C, Diedrich L, Harris P, Sleight P, Biagioni I, Robertson D, Bernardi L (2003) Effects of unilateral and bilateral carotid baroreflex stimulation on cardiac and neural sympathetic discharge oscillatory patterns. Circulation 108(6):717–723

    Article  PubMed  Google Scholar 

  13. Furlan R, Jacob G, Snell M, Robertson D, Porta A, Parris P, Mosqueda-Garcia R (1998) Chronic orthostatic intolerance: a disorder with discordant cardiac and vascular sympathetic control. Circulation 98(20):2154–2159

    Article  CAS  PubMed  Google Scholar 

  14. Geddes J, Mehlsen J, Olufsen M (2020) Characterization of blood pressure and heart rate oscillations in pots patients via uniform phase empirical mode decomposition. IEEE Trans Biomed Eng 67(11):3016–3025

    Article  PubMed  Google Scholar 

  15. Greve Y, Geier F, Popp S, Bertsch T, Singler K, Meier F, Smolarsky A, Mang H, Müller C, Christ M (2014) The prevalence and prognostic significance of near syncope and syncope: a prospective study of 395 cases in an emergency department (the speed study). Dtsch Arztebl Int 111(12):197–204

    PubMed  PubMed Central  Google Scholar 

  16. Guyton A, Hall J (2016) Medical physiology, 13th edn. Elsevier, Philadelphia

    Google Scholar 

  17. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer Series in Statistics. Springer, New York

    Google Scholar 

  18. Jacob G, Barbic F, Glago M, Dipaola F, Porta A, Furlan R (2018) Cardiovascular autonomic profile in women with constitutional hypotension. J Hypertens 36(10):2068–2076

    Article  CAS  PubMed  Google Scholar 

  19. Khodor N (2014) Dynamical analysis of mutivariate time series for the early detection of syncope during head-up tilt test. Ph.D. thesis, Université Rennes 1, Rennes, France

  20. Khodor N, Carrault G, Matelot D, Amoud H, Khalil M, Boullay N, Carré F, Hernandez A (2016) Early syncope detection during head up tilt test by analyzing interactions between cardio-vascular signals. Dig Signal Process 49:86–94

    Article  Google Scholar 

  21. Klemenc M, Strumbelj E (2015) Predicting the outcome of head-up tilt test using heart rate variability and baroreflex sensitivity parameters in patients with vasovagal syncope. Clinical Autonomic Res 25 (6):391–398

    Article  Google Scholar 

  22. Kuran A, Erickson M, Petersen M, Franzen A, Williams T, Sutton R (2000) Respiratory changes in vasovagal syncope. J Cardiovasc Electrophysiol 11(6):607–611

    Article  Google Scholar 

  23. Kurbaan A, Bowker T, Wijesekera N, Heaven D, Itty S, Sutton R (2003) Age and hemodynamic responses to tilt testing in those with syncope of unknown origin. J Am Coll Cardiol 41(6):1004–1007

    Article  PubMed  Google Scholar 

  24. Li H, Liao Y, Han Z, Wang Y, Liu P, Zhang C, Tang C, Du J, Jin H (2018) Head-up tilt test provokes dynamic alterations in total peripheral resistance and cardiac output in children with vasovagal syncope. Acta Paediatr 107(10):1786–1791

    Article  PubMed  Google Scholar 

  25. Mark A (1983) The bezold-jarisch reflex revisited: clinical implications of inhibitory reflexes originating in the heart. JAJCC 1(1):69–72

    Google Scholar 

  26. Mehlsen J, Kaijer M, Mehlsen AB (2008) Autonomic and electrocardiographic changes in cardioinhibitory syncope. Europace 10(1):91–95

    Article  PubMed  Google Scholar 

  27. Moya A, Sutton R, Ammirati F, Blanc JJ, Brignole M, Dahm J, Deharo JC, Gajek J, Gjesdal K, Krahn A, Massin M, Pepi M, Pezawas T, Ruiz Granell R, Sarasin F, Ungar A, van Dijk J, Walma E, Wieling W (2009) Guidelines for the diagnosis and management of syncope (version 2009). Europ Heart J 30:2631–2671

    Article  Google Scholar 

  28. Ocon A, Medow M, Taneja I, Stewart J (2011) Respiration drives phase synchronization between blood pressure and rr interval following loss of cardiovagal baroreflex during vasovagal syncope. Am J Physiol 300(2):H527–H540

    CAS  Google Scholar 

  29. Parati G, Bretinieri M, Pomidossi G, Casadei R, Groppeli A, Pedotti A, Zanchetti A, Mancia G (1988) Evaluation of the baroreceptor-heart rate reflex by 24-hour intra-arterial blood pressure monitoring in humans. Hypertension 12(2):214– 222

    Article  CAS  PubMed  Google Scholar 

  30. Parlow J, Viale J, Annat G, Hughson R, Quintin L (1995) Spontaneous cardiac baroreflex in humans: comparison with drug-induced responses. Hypertension 25(5):1058–1068

    Article  CAS  PubMed  Google Scholar 

  31. Probst M, Kanzaria H, Gbedemah M, Richardson L, Sun B (2015) National trends in resource utilization associated with ed visits for syncope. American. J Emergency Med 33(8):998–1001

    Google Scholar 

  32. Randall E, Billeschou A, Brinth L, Mehlsen J, Olufsen M (2019) A model-based analysis of autonomic nervous function in response to the valsalva maneuver. J Appl Physiol 127(5):1386– 1402

    Article  PubMed  Google Scholar 

  33. Randall E, Randolph N, Olufsen M (2020) Persistent instability in a nonhomogeneous delay differential equation system of the valsalva maneuver. Math Biosci 319:108292

    Article  PubMed  Google Scholar 

  34. Remmen J, Aengevaeren W, Verheugt F, van ver Werf T, Luijten H, A Bos RJ (2002) Finapres arterial pulse wave analysis with modelflow is not a reliable non-invasive method for assessment of cardiac output. Clin Sci (Lond) 103(2):143–149

    Article  Google Scholar 

  35. Robertson D, Biaggioni I, Low P (2011) Primer on the autonomic nervous system, 3rd edn. Elsevier, London

    Google Scholar 

  36. Romme J, van Dijk N, Boer K, Dekker L, Stam J, Reitsma J, Wieling W (2008) Influence of age and gender on the occurrence and presentation of reflex syncope. Clin Autonom Res 28(3):127–133

    Article  Google Scholar 

  37. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comp Appl Math 20:53–65

    Article  Google Scholar 

  38. Sander-Jensen K, Mehlsen J, Stadeager C, Christensen N, Fahrenkrug J, Schwartz T, Warberg J, Bie P (1988) Increase in vagal activity during hypotensive lower-body negative pressure in humans. Am J Physiol 255(1 Pt 2):R149–R156

    CAS  PubMed  Google Scholar 

  39. Sheldon R, Grubb B, Olshansky B (2015) 2015 heart rhythm society expert consensus statement on the diagnosis and treatment of postural tachycardia syndrome, inappropriate sinus tachycardia, and vasovagal syncope. Heart Rhythm 12(6):e41–e63

    Article  PubMed  PubMed Central  Google Scholar 

  40. van Dijk J, Thijs R, Benditt D, Wieling W (2009) A guide to disorders causing transient loss of consciousness: focus on syncope. Nat Rev Neurol 5(8):438–448

    Article  PubMed  Google Scholar 

  41. Virag N, Sutton R, Vetter R, Markowitz T (2007) Prediction of vasovagal syncope from heart rate and blood pressure trend and variability: experience in 1,155 patients. Heart Rhythm 4(11):1375–1382

    Article  PubMed  Google Scholar 

  42. Vlasta B, Marchi A, De Maria B, Rossato G, Nollo G, Faes L, Porta A (2016) Nonlinear effects of respiration on the crosstalk between cardiovascular and cerebrovascular systems. Philos Trans A Math Phys Eng Sci 374(2067):2015079

    Google Scholar 

  43. Williams N, Wind-Willassen O, Wright A, Program R, Mehlsen J, Ottesen J, Olufsen M (2014) Patient-specific modelling of head-up tilt. Math Med Biol 31(4):365–392

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank the Statistical and Applied Mathematical Sciences Institute (SAMSI), where this work was initiated, and Peter Novak, Brigham and Women’s Hospital, Boston, MA 02115, for helpful discussions.

Funding

This work was supported in part by the National Institutes of Health and the National Science Foundation under Grants NSF-DMS 1557761 and NSF-DMS-1745654 and the National Institutes of Health through grant NIH 5P50GM094503-06 VPR sub-award to North Carolina State University.

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, 27695, USA

    Steven Gilmore, Justen Geddes, Pierre Gremaud & Mette S. Olufsen

  2. Sandia National Laboratories, Albuquerque, NM, 87123, USA

    Joseph Hart

  3. Novo Nordisk A/S, 2880, Bagsværd, Denmark

    Christian H. Olsen

  4. Rigshospitalet, University of Copenhagen, 2100, Copenhagen Ø, Denmark

    Jesper Mehlsen

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  1. Steven Gilmore
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Correspondence to Mette S. Olufsen.

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This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. SAND2020-2937 J.

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Gilmore, S., Hart, J., Geddes, J. et al. Classification of orthostatic intolerance through data analytics. Med Biol Eng Comput 59, 621–632 (2021). https://doi.org/10.1007/s11517-021-02314-0

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