Evidence map›Paper›PMID 26115898›Full record

ArticleJournal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia2015

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

Haydn Hoffman, Sunghoon I Lee, Jordan H Garst, Derek S Lu, Charles H Li, Daniel T Nagasawa, Nima Ghalehsari, Nima Jahanforouz, Mehrdad Razaghy, Marie Espinal and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Haydn HoffmanDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Sunghoon I LeeDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA; Wireless Health Institute, University of California Los Angeles, Los Angeles, CA, USA.
Jordan H GarstDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Derek S LuDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Charles H LiDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Daniel T NagasawaDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Nima GhalehsariDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Nima JahanforouzDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Mehrdad RazaghyDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Marie EspinalDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Amir GhavamrezaiiDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Brian H PaakDepartment of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
Irene WuDepartment of Anesthesiology, University of California Los Angeles, Los Angeles, CA, USA.
Majid SarrafzadehDepartment of Anesthesiology, University of California Los Angeles, Los Angeles, CA, USA; Wireless Health Institute, University of California Los Angeles, Los Angeles, CA, USA.
Daniel C LuDepartment 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, CA, USA; Neuromotor Recovery and Rehabilitation Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA. Electronic address: dclu@mednet.ucla.edu.

Funding

Enabling forelimb function with agonist drug and epidural stimulation in SCIU01EB015521 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EDGERTON, REGGIE, LU, DANIEL · 2013 to 2017
$6.0M
NIBIB NIH HHS U01 EB015521
6 · The paper itself

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.

Indexed as

Recovery of FunctionSupport Vector MachineAdultAgedCervical VertebraeFemaleHumansLinear ModelsMaleMiddle AgedSpinal Cord DiseasesSpondylosisCervical spondylotic myelopathyMultivariate linear regressionSupport vector regressionSurgical outcomes

Identifiers

PMID26115898
PMCPMC4842312

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.