EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function

  • Shikha Chaganti
  • Jamie R. Robinson
  • Camilo Bermudez
  • Thomas Lasko
  • Louise A. Mawn
  • Bennett A. Landman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Multi-modal analyses of diseases of the optic nerve, that combine radiological imaging with other electronic medical records (EMR), improve understanding of visual function. We conducted a study of 55 patients with glaucoma and 32 patients with thyroid eye disease (TED). We collected their visual assessments, orbital CT imaging, and EMR data. We developed an image-processing pipeline that segmented and extracted structural metrics from CT images. We derived EMR phenotype vectors with the help of PheWAS (from diagnostic codes) and ProWAS (from treatment codes). Next, we performed a principal component analysis and multiple-correspondence analysis to identify their association with visual function scores. We found that structural metrics derived from CT imaging are significantly associated with functional visual score for both glaucoma (R2 = 0.32) and TED (R2 = 0.4). Addition of EMR phenotype vectors to the model significantly improved (p < 1E−04) the R2 to 0.4 for glaucoma and 0.54 for TED.


CT imaging EMR Regression Optic nerve MCA PCA 



This research was supported by NSF CAREER 1452485 and NIH grants 5R21EY024036. This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. This study was in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. This project was supported in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06.


  1. 1.
    Rein, D.B., Zhang, P., Wirth, K.E., Lee, P.P., Hoerger, T.J., McCall, N., Klein, R., Tielsch, J.M., Vijan, S., Saaddine, J.: The economic burden of major adult visual disorders in the United States. Arch. Ophthalmol. 124, 1754–1760 (2006)CrossRefGoogle Scholar
  2. 2.
    Xiuya Yao, S.C., Nabar, K.P., Nelson, K., Plassard, A., Harrigan, R.L., Mawn, L.A., Landman, B.A.: Structural-functional relationships between eye orbital imaging biomarkers and clinical visual assessments. In: Proceedings of the SPIE Medical Imaging ConferenceGoogle Scholar
  3. 3.
    Chaganti, S., Nelson, K., Mundy, K., Luo, Y., Harrigan, R.L., Damon, S., Fabbri, D., Mawn, L., Landman, B.: Structural functional associations of the orbit in thyroid eye disease: Kalman filters to track extraocular rectal muscles. In: SPIE Medical Imaging, vol. 97847, p. 97841G. International Society for Optics and PhotonicsGoogle Scholar
  4. 4.
    Xierali, I.M., Hsiao, C.-J., Puffer, J.C., Green, L.A., Rinaldo, J.C., Bazemore, A.W., Burke, M.T., Phillips, R.L.: The rise of electronic health record adoption among family physicians. Ann. Fam. Med. 11, 14–19 (2013)CrossRefGoogle Scholar
  5. 5.
    Patel, V., Jamoom, E., Hsiao, C.-J., Furukawa, M.F., Buntin, M.: Variation in electronic health record adoption and readiness for meaningful use: 2008–2011. J. Gen. Intern. Med. 28, 957–964 (2013)CrossRefGoogle Scholar
  6. 6.
    Rondinelli, R.D., Genovese, E., Brigham, C.R.: Guides to the Evaluation of Permanent Impairment. American Medical Association, Chicago (2008)Google Scholar
  7. 7.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008)CrossRefGoogle Scholar
  8. 8.
    Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17, 194–208 (2013)CrossRefGoogle Scholar
  9. 9.
    Denny, J.C., Bastarache, L., Ritchie, M.D., Carroll, R.J., Zink, R., Mosley, J.D., Field, J.R., Pulley, J.M., Ramirez, A.H., Bowton, E.: Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1111 (2013)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Shlens, J.: A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 (2014)
  12. 12.
    Abdi, H., Valentin, D.: Multiple correspondence analysis. In: Encyclopedia of Measurement and Statistics, pp. 651–657 (2007)Google Scholar
  13. 13.
    McCullagh, P.: Generalized linear models. Eur. J. Oper. Res. 16, 285–292 (1984)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Draper, N.R., Smith, H., Pownell, E.: Applied Regression Analysis. Wiley, New York (1966)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceVanderbilt UniversityNashvilleUSA
  2. 2.Vanderbilt University Medical CenterNashvilleUSA
  3. 3.Department of Biomedical EngineeringVanderbilt UniversityNashvilleUSA
  4. 4.Department of Biomedical InformaticsVanderbilt UniversityNashvilleUSA
  5. 5.Vanderbilt Eye InstituteNashvilleUSA
  6. 6.Department of Electrical EngineeringVanderbilt UniversityNashvilleUSA

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