Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy
| Autor: | Amir A Ghavamrezaii, Marie Espinal, Brian H. Paak, Daniel T. Nagasawa, Nima Jahanforouz, Nima Ghalehsari, Haydn Hoffman, Charles H. Li, Mehrdad Razaghy, Daniel C. Lu, Sunghoon Ivan Lee, Jordan H. Garst, Majid Sarrafzadeh, Irene Wu, Derek S. Lu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Adult
Male medicine.medical_specialty Support Vector Machine Clinical Sciences Article Spinal Cord Diseases Physiology (medical) Bayesian multivariate linear regression Spondylotic myelopathy Medicine Humans Fine motor Aged Neurology & Neurosurgery business.industry Absolute accuracy Cervical spondylotic myelopathy Neurosciences Surgical outcomes General Medicine Recovery of Function Middle Aged Surgery Oswestry Disability Index Brain Disorders Support vector machine Support vector regression Neurology Benefit analysis Cohort Cervical Vertebrae Linear Models Female Neurology (clinical) Spondylosis Patient Safety business Multivariate linear regression |
| Zdroj: | Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia, vol 22, iss 9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|