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Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners

Abstract

Objective

The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists.

Methods

We enrolled 434 subjects with the following diagnoses: chronic inflammatory demyelinating polyneuropathy (99), Charcot-Marie-Tooth disease type 1A (124), hereditary neuropathy with liability to pressure palsy (46), diabetic polyneuropathy (67), and controls (98). In each diagnostic class, 90% of subjects were used as training set for SLAs to establish the best performing SLA by tenfold cross validation procedure and 10% of subjects were employed as test set. Performance indicators were accuracy, precision, sensitivity, and specificity.

Results

SVM showed the highest overall diagnostic accuracy both in training and test sets (90.5 and 93.2%) and ranked first in a multidimensional comparison analysis. Overall accuracy of neurophysiologists ranged from 54.5 to 81.8%.

Conclusions

This proof of principle study shows that SVM provides a high electrodiagnostic accuracy in polyneuropathies. We suggest that the use of SLAs in electrodiagnosis should be exploited to possibly provide a diagnostic support system especially helpful for the less experienced practitioners.

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Data Availability

Deidentified participant data, data dictionary and R script for the implementation of the statistical analysis will be available on request.

References

  1. Martyn CN, Hughes RA (1997) Epidemiology of peripheral neuropathy. J Neurol Neurosurg Psychiatry 62:310–318

    CAS  Article  Google Scholar 

  2. Dyck PJ, Dyck PJ, Grant IA, Fealey RD (1996) Ten steps in characterizing and diagnosing patients with peripheral neuropathy. Neurology 47:10–17

    CAS  Article  Google Scholar 

  3. England JD, Asbury AK (2004) Peripheral neuropathy. Lancet 363:2151–2161

    Article  Google Scholar 

  4. England JD, Gronseth GS, Franklin G, Miller RG, Asbury AK, Carter GT, Cohen JA, Fisher MA, Howard JF, Kinsella LJ, Latov N, Lewis RA, Low PA, Sumner AJ (2005) Distal symmetric polyneuropathy: a definition for clinical research, report of the AAN, AAEM, and AAPM&R. Neurology 64:199–207

    CAS  Article  Google Scholar 

  5. Watson JC, Dyck PJ (2015) Peripheral neuropathy: a practical approach to diagnosis and symptom management. Mayo Clin Proc 90:940–945

    Article  Google Scholar 

  6. Allen JA, Lewis RA (2015) CIDP diagnostic pitfalls and perception of treatment benefit. Neurology 85:498–504

    CAS  Article  Google Scholar 

  7. Allen JA, Lewis RA (2018) Electrodiagnostic errors contribute to chronic inflammatory demyelinating polyneuropathy misdiagnosis. Muscle Nerve 57:542–549

    CAS  Article  Google Scholar 

  8. Gelinas D, Katz J, Nisbet B, England JD (2018) Current practice pattern in CIDP: a cross-sectional survey of neurologists in the United States. J Neurol Sci 397:84–91

    Article  Google Scholar 

  9. Nicolas G, Maisonobe T, Le Forestier N, Léger JM, Bouche P (2002) Proposed revised electrophysiological criteria for chronic inflammatory demyelinating polyradiculoneuropathy. Muscle Nerve 25:26–30

    Article  Google Scholar 

  10. Van den Bergh PY, Pieret F (2004) Electrodiagnostic criteria for acute and chronic inflammatory demyelinating polyradiculoneuropathy. Muscle Nerve 29:565–574

    Article  Google Scholar 

  11. Wilson J, Chawla J, Fisher M (2005) Sensitivity and specificity of electrodiagnostic criteria for CIDP using ROC curves: comparison to patients with diabetic and MGUS associated neuropathies. J Neurol Sci 231:19–28

    CAS  Article  Google Scholar 

  12. Rajabally YA, Nicolas G, Pieret F, Bouche P, van den Bergh PY (2009) Validity of diagnostic criteria for chronic inflammatory demyelinating polyneuropathy: a multicentre European study. J Neurol Neurosurg Psychiatry 80:1364–1368

    CAS  Article  Google Scholar 

  13. Rondinelli RD, Stolov WC, Fujimoto WY, Osberg JS (1988) Electrodiagnosis of diabetic peripheral polyneuropathy. A multivariate analytic approach. Am J Phys Med Rehabil 67:12–23

    CAS  Article  Google Scholar 

  14. Rondinelli RD, Robinson LR, Hassanein KM, Stolov WC, Fujimoto WY, Rubner DE (1994) Further studies on the electrodiagnosis of diabetic peripheral polyneuropathy using discriminant function analysis. Am J Phys Med Rehabil 73:116–1123

    CAS  Article  Google Scholar 

  15. Uncini A, Ippoliti L, Shahrizaila N, Sekiguchi Y, Kuwabara S (2017) Optimizing the electrodiagnostic accuracy in Guillain-Barré syndrome subtypes: criteria sets and sparse linear discriminant analysis. Clin Neurophysiol 128:1176–1183

    Article  Google Scholar 

  16. Joint Task Force of the EFNS and the PNS (2010) European Federation of Neurological Societies/Peripheral Nerve Society guideline on management of chronic inflammatory demyelinating polyneuropathy: report of a joint task force of the European Federation of Neurological Societies and the Peripheral Nerve Society-First Revision. J Peripher Nerv Syst 2010(15):1–9

    Article  Google Scholar 

  17. Saperstein DS, Amato AA, Wolfe GI, Katz JS, Nations SP, Jackson CE et al (1999) Multifocal acquired demyelinative sensory and motor neuropathy: the Lewis-Sumner syndrome. Muscle Nerve 22:560–566

    CAS  Article  Google Scholar 

  18. Katz JS, Saperstein DS, Gronseth G, Amato AA, Barohn RJ (2000) Distal acquired demyelinating symmetric neuropathy. Neurology 54:615–620

    CAS  Article  Google Scholar 

  19. Dyck PJ, Albers JW, Andersen H, Arezzo JC, Biessels GJ, Bril V, Feldman EL, Litchy WJ, O'Brien PC, Russell JW, on behalf of The Toronto Expert Panel on Diabetic Neuropathy (2011) Toronto expert panel on diabetic neuropathy. Diabetic polyneuropathies: update on research definition, diagnostic criteria and estimation of severity. Diabetes Metab Res Rev 27:620–628

    Article  Google Scholar 

  20. Vapnik VN (2000) The nature of statistical learning theory. Springer-Verlag

  21. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining; inference and prediction, 2nd edn. Springer-Verlag, pp 241–251

  22. Flach P (2012) Machine learning. The art and science of algorithms that make sense of data. Cambridge University Press, pp 53–58

  23. Edwards A (1948) Note on the ‘correction for continuity’ in testing the significance of the difference between correlated proportions. Psychometrika 13:185–187

    CAS  Article  Google Scholar 

  24. Enders CK (2003) Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychol Methods 8:322–337

    Article  Google Scholar 

  25. Theodoridis S (2015) Machine Learning: A Bayesian and Optimization Perspective. Academic Press p 314

  26. James G, Vitten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, pp 24–26

  27. Bromberg M (2011) Review of the evolution of the electrodiagnostic criteria for chronic inflammatory demyelinating neuropathy. Muscle Nerve 43:780–794

    Article  Google Scholar 

  28. Rajabally YA, Fowle AJ, Van den Bergh PY (2015) Which criteria for research in chronic inflammatory demyelinating polyneuropathy? An analysis of current practice. Muscle Nerve 51:932–933

    Article  Google Scholar 

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Authors and Affiliations

Authors

Contributions

AU and GA equally contributed to the study. AU, GA, and LI designed the study and AU acted as study supervisor. FM, YS, LM, ST, AT, SK, and LS collected the data. GA made the statistical analysis. AU, GA, and LI analyzed and interpreted the results. All the authors contributed to drafting and revising the manuscript and gave their approval to the final version of the manuscript.

Corresponding author

Correspondence to Antonino Uncini.

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Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The study was carried out in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University Federico II of Naples (320/17) and the institutional Medical Ethics Research Committee of each center.

Consent to participate

Not applicable.

Consent for publication

All subjects signed a written informed consent that allowed the utilization of electrophysiological data for research purposes.

Informed consent

All subjects signed a written informed consent that allowed the utilization of electrophysiological data for research purposes.

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Not applicable (software application or custom code).

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Uncini, A., Aretusi, G., Manganelli, F. et al. Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners. Neurol Sci 41, 3719–3727 (2020). https://doi.org/10.1007/s10072-020-04499-y

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  • DOI: https://doi.org/10.1007/s10072-020-04499-y

Keywords

  • Polyneuropathies
  • Electrodiagnosis
  • Diagnostic accuracy
  • Supervised learning algorithms