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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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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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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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