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

Hoffman, Haydn; Lee, Sunghoon Ivan; Garst, Jordan H.; Lu, Derek S.; Li, Charles; Nagasawa, Daniel T.; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir A; Paak, Brian H.; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C.

doi:10.1016/j.jocn.2015.04.002

Cited by 36 publications

(23 citation statements)

References 38 publications

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“…Table 3 shows that SVR with a radial kernel, which is a nonlinear regression model, performs better than the two linear models (MLR and SVR with a linear kernel). This demonstrates that the relationships between the predictors and the ODI scores can be more accurately described using nonlinear functions, which partially agrees with the findings in Hoffman et al [42]. The reported estimation accuracy (MAD = 0.12 and r = 0.76) can be achieved when FirstQMeanErr, ∆Gain, and VEL-INC are used to construct an SVM-based model.…”

Section: Estimation Of Perceived Motor Deficitssupporting

confidence: 90%

“…Nonetheless, the reported results demonstrate that the handgrip device and algorithms can together quantify the hand motor function with close correlation to the level of perceived deficits in performing ADL with acceptable accuracy. The estimated ODI scores, as well as the actual ODI scores that were collected postoperatively, showed statistically significant correlation to arm pain duration (p < 0.007 and p < 0.006, respectively), which agrees with findings in prior work [42]. Table 3 shows that SVR with a radial kernel, which is a nonlinear regression model, performs better than the two linear models (MLR and SVR with a linear kernel).…”

Section: Estimation Of Perceived Motor Deficitssupporting

confidence: 87%

“…The ODI investigated the degree of interference from pain in performing a number of ADL involving both upper and lower limbs. The ODI has been widely used to assess functionality of patients with CSM in a number of previous studies [7,22,42,[45][46], because a vast majority of patients with CSM have complaints about the use of their hands (upper limb) as well as difficulties in gait (lower limb) [43]. However, other patient-reported outcomes such as the Neck Disability Index (NDI) [47] may provide better correlation to hand motor skills that can be captured by the handgrip device.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

Lee

Huang²,

Mortazavi³

et al. 2016

J Rehabil Res Dev

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Abstract-Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of patients with CSM using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring testretest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of hand motor function impairment and surgical outcomes.

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Section: Estimation Of Perceived Motor Deficitssupporting

confidence: 90%

Section: Estimation Of Perceived Motor Deficitssupporting

confidence: 87%

Section: Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

Lee

Huang²,

Mortazavi³

et al. 2016

J Rehabil Res Dev

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“…We chose this activity because it requires coordinated manipulation of grip strength (including active muscle contraction and relaxation) that is abundant in the ADLs. Moreover, this test has previously shown to be feasible and efficacious in measuring fine motor function of patients with other neurologic disorders [15, 27]. We observed a significant improvement in accuracy on our handgrip-based tracking task over time.…”

Section: Discussionsupporting

confidence: 56%

Rehabilitation of hand function after spinal cord injury using a novel handgrip device: a pilot study

Hoffman

Sierro

Niu

et al. 2017

J NeuroEngineering Rehabil

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BackgroundActivity-based therapy (ABT) for patients with spinal cord injury (SCI), which consists of repetitive use of muscles above and below the spinal lesion, improves locomotion and arm strength. Less data has been published regarding its effects on hand function. We sought to evaluate the effects of a weekly hand-focused therapy program using a novel handgrip device on grip strength and hand function in a SCI cohort.MethodsPatients with SCI were enrolled in a weekly program that involved activities with the MediSens (Los Angeles, CA) handgrip. These included maximum voluntary contraction (MVC) and a tracking task that required each subject to adjust his/her grip strength according to a pattern displayed on a computer screen. For the latter, performance was measured as mean absolute accuracy (MAA). The Spinal Cord Independence Measure (SCIM) was used to measure each subject’s independence prior to and after therapy.ResultsSeventeen patients completed the program with average participation duration of 21.3 weeks. The cohort included patients with American Spinal Injury Association (ASIA) Impairment Scale (AIS) A (n = 12), AIS B (n = 1), AIS C (n = 2), and AIS D (n = 2) injuries. The average MVC for the cohort increased from 4.1 N to 21.2 N over 20 weeks, but did not reach statistical significance. The average MAA for the cohort increased from 9.01 to 21.7% at the end of the study (p = .02). The cohort’s average SCIM at the end of the study was unchanged compared to baseline.ConclusionsA weekly handgrip-based ABT program is feasible and efficacious at increasing hand task performance in subjects with SCI.

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“…To this end, multiple groups have recently published articles on the use of ML in predictive modelling related to both DCM and nontraumatic lumbar SCI. One of the early articles on applying ML to DCM was published by Hoffman and colleagues, 35 who compared a support vector regression model (SVR -a regression variant of SVM) with a multivariate logistic regression model in the prediction of functional outcome (specifically, the Oswetry Disability Index or ODI) after surgery for DCM. The authors found that the SVR model outperformed its logistic regression counterpart.…”

Section: Machine Learning Algorithms In Nontraumatic Spinal Cord Injurymentioning

confidence: 99%

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

et al. 2019

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Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

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

Cited by 36 publications

References 38 publications

Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests

Rehabilitation of hand function after spinal cord injury using a novel handgrip device: a pilot study

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

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