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Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.
Hoffman, Haydn; Lee, Sunghoon I; Garst, Jordan H; Lu, Derek S; Li, Charles H; Nagasawa, Daniel T; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir; Paak, Brian H; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C.
Afiliação
  • Hoffman H; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Lee SI; Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA; Wireless Health Institute, University of California Los Angeles, Los Angeles, CA, USA.
  • Garst JH; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Lu DS; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Li CH; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Nagasawa DT; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Ghalehsari N; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Jahanforouz N; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Razaghy M; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Espinal M; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Ghavamrezaii A; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Paak BH; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Wu I; Department of Anesthesiology, University of California Los Angeles, Los Angeles, CA, USA.
  • Sarrafzadeh M; Department of Anesthesiology, University of California Los Angeles, Los Angeles, CA, USA; Wireless Health Institute, University of California Los Angeles, Los Angeles, CA, USA.
  • Lu DC; Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA; Wireless Health Institute, University of California Los Angeles, Los Angeles, CA, USA; Department of Orthopedic Surgery, University of California Los Angeles, Los Angeles, C
J Clin Neurosci ; 22(9): 1444-9, 2015 Sep.
Article em En | MEDLINE | ID: mdl-26115898
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças da Medula Espinal / Recuperação de Função Fisiológica / Espondilose / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2015 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças da Medula Espinal / Recuperação de Função Fisiológica / Espondilose / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2015 Tipo de documento: Article