Cervical Spondylotic Myelopathy: An Updated Review

P, Lee George; Bhaveshkumar, Ahir; Nauman, Chaudhry; H, Engelhard Herbert

doi:10.23937/2643-4474/1710056

Cited by 2 publications

(1 citation statement)

References 41 publications

(53 reference statements)

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“…On the other hand, they reported that there was a knowledge gap in the selection of therapeutic options for mild DCM. Meanwhile, studies on the optimal surgical approach method in patients with DCM who decided to undergo surgery have also been reported ( 9 ). Several studies have compared outcomes after anterior and posterior surgical approaches (ASA and PSA, respectively) for DCM; however, no clear evidence of superiority has been established to date ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms

Park

Cho²,

Yang³

et al. 2022

Front. Surg.

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BackgroundTherapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM.MethodsThis retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification.ResultsIn total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors.ConclusionML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.

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Section: Introductionmentioning

confidence: 99%

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms

Park

Cho²,

Yang³

et al. 2022

Front. Surg.

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A Meta-Analysis of Surgical Outcomes in 25727 Patients Undergoing Anterior Cervical Discectomy and Fusion or Anterior Cervical Corpectomy and Fusion for Cervical Deformity

Barot,

Ruiz-Cardozo,

Singh

et al. 2024

Global Spine Journal

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Study Design Systematic Review. Objectives To evaluate which cervical deformity correction technique between anterior cervical discectomy and fusion (ACDF) and anterior cervical corpectomy and fusion (ACCF) produces better clinical, radiographic, and operative outcomes. Methods We conducted a meta-analysis comparing studies involving ACDF and ACCF. Adult patients with either original or previously treated cervical spine deformities were included. Two independent reviewers categorized extracted data into clinical, radiographic, and operative outcomes, including complications. Clinical assessments included patient-reported outcomes; radiographic evaluations examined C2-C7 Cobb angle, T1 slope, T1-CL, C2-7 SVA, and graft stability. Surgical measures included surgery duration, blood loss, hospital stay, and complications. Results 26 studies (25727 patients) met inclusion criteria and were extracted. Of these, 14 studies (19077 patients) with low risk of bias were included in meta-analysis. ACDF and ACCF similarly improve clinical outcomes in terms of JOA and NDI, but ACDF is significantly better at achieving lower VAS neck scores. ACDF is also more advantageous for improving cervical lordosis and minimizing the incidence of graft complications. While there is no significant difference between approaches for most surgical complications, ACDF is favorable for reducing operative time, intraoperative blood loss, and length of hospital stay. Conclusions While both techniques benefit cervical deformity patients, when both techniques are feasible, ACDF may be superior with respect to VAS neck scores, cervical lordosis, graft complications and certain perioperative outcomes. Further studies are recommended to address outcome variability and refine surgical approach selection.

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Cervical Spondylotic Myelopathy: An Updated Review

Cited by 2 publications

References 41 publications

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms

A Meta-Analysis of Surgical Outcomes in 25727 Patients Undergoing Anterior Cervical Discectomy and Fusion or Anterior Cervical Corpectomy and Fusion for Cervical Deformity

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