Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

Journal: Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Published Date: Jun 23, 2015

Abstract

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate.

Authors

  • Haydn Hoffman
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Sunghoon I Lee
    Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA.
  • Jordan H Garst
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Derek S Lu
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Charles H Li
    Department of Radiology, The University of California, Irvine, Orange, California, USA.
  • Daniel T Nagasawa
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Nima Ghalehsari
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Nima Jahanforouz
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Mehrdad Razaghy
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Marie Espinal
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Amir Ghavamrezaii
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Brian H Paak
    Department of Neurosurgery, UCLA, Los Angeles, CA.
  • Irene Wu
    Department of Anesthesiology, University of California Los Angeles, Los Angeles, CA, USA.
  • Majid Sarrafzadeh
    Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA.
  • Daniel C Lu
    Department of Neurosurgery, UCLA, Los Angeles, CA.

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