Dataset Information

Paired and matched analysis of neurological outcomes in revision surgery for cervical myelopathy following delayed neurological decline.

ABSTRACT:

Background

Delayed neurological decline may be experienced following successful decompression surgery for cervical myelopathy. Our objective was to analyze neurological recovery upon revision surgery with relation to the index procedure and a matched control.

Methods

Fourteen patients underwent both primary and revision decompression at a single academic center. Peri-operative clinical, radiological, and surgical details were retrieved. Neurological outcomes [change in modified Japanese Orthopedic Association (mJOA), recovery ratio] following the second surgery were compared to (I) the primary operation and (II) a control subject receiving primary decompression matched for gender, age, mJOA score, and surgical approach. The minimum clinically important difference (MCID) in mJOA score was set at 2.5.

Results

Revision decompressions were performed 6.8±4.2 years following the index surgery, when patients were 61.4±11.0 years of age. An increase in mJOA score of 2.7±2.0 following revision surgery was similar to that achieved after the primary operation (2.2±2.1, P=0.616). A recovery ratio of 38.1%±25.4% upon revision compared favorably to that following the primary operation (35.0%±37.4%, P=0.867). Non-inferiority testing between revision surgery and the first operation (P=0.02) demonstrated a similar capacity to achieve the MCID as did comparison with matched subjects (P<0.01).

Conclusions

Patients were able to make up for lost neurological gains following revision surgery. Careful selection of cases for revision likely facilitated recovery. Recovery trajectories should be consolidated upon larger sample sizes allowing for identification of prognostic factors.

SUBMITTER: Yee AHF

PROVIDER: S-EPMC10982917 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Paired and matched analysis of neurological outcomes in revision surgery for cervical myelopathy following delayed neurological decline.

Yee Andy Hon Fai AHF Zhang Changmeng C Koljonen Paul Aarne PA Shea Graham Ka Hon GKH

Journal of spine surgery (Hong Kong) 20240314 1

<h4>Background</h4>Delayed neurological decline may be experienced following successful decompression surgery for cervical myelopathy. Our objective was to analyze neurological recovery upon revision surgery with relation to the index procedure and a matched control.<h4>Methods</h4>Fourteen patients underwent both primary and revision decompression at a single academic center. Peri-operative clinical, radiological, and surgical details were retrieved. Neurological outcomes [change in modified Ja ...[more]

PMID: 38567002

Similar Datasets

Project description:IntroductionThis study aimed to develop machine learning models to predict neurological outcomes in patients with degenerative cervical myelopathy (DCM) after surgical decompression and identify key factors that contribute to a better outcome, providing a reference for patient consultation and surgical decision-making.MethodsThis retrospective study reviewed 1,895 patients who underwent cervical decompression surgery for DCM at Peking University Third Hospital from 2011 to 2020, with 672 patients included in the final analysis. Five machine learning methods, namely, linear regression (LR), support vector machines (SVM), random forest (RF), XGBoost, and Light Gradient Boosting Machine (LightGBM), were used to predict whether patients achieved the minimal clinically important difference (MCID) in the improvement in the Japanese Orthopedic Association (JOA) score, which was based on basic information, symptoms, physical examination signs, intramedullary high signals on T2-weighted (T2WI) magnetic resonance imaging (MRI), and various scale scores. After training and optimizing multiple ML algorithms, we generated a model with the highest area under the receiver operating characteristic curve (AUROC) to predict short-term outcomes following DCM surgery. We evaluated the importance of the features and created a feature-reduced model. The model's performance was assessed using an external dataset.ResultsThe LightGBM algorithm performed the best in predicting short-term neurological outcomes in the testing dataset, achieving an AUROC value of 0.745 and an area under the precision-recall curve (AUPRC) value of 0.810. The important features influencing performance in the short-term model included the preoperative JOA score, age, SF-36-GH, SF-36-BP, and SF-36-PF. The feature-reduced LightGBM model, which achieved an AUROC value of 0.734, also showed favorable performance. Moreover, the feature-reduced model showed an AUROC value of 0.785 for predicting the MCID of postoperative JOA in the external dataset, which included 58 patients from other hospitals.ConclusionWe developed models based on machine learning to predict postoperative neurological outcomes. The LightGBM model presented the best predictive power regarding the surgical outcomes of DCM patients. Feature importance analysis revealed that variables, including age, preoperative JOA score, SF-36-PF, SF-36-GH, and SF-36-BP, were essential factors in the model. The feature-reduced LightGBM model, designed for ease of application, achieved nearly the same predictive power with fewer variables.

Project description:Prospective observational cohort study.To determine if postoperative cervical sagittal balance is an independent predictor of health-related quality of life outcome after surgery for cervical spondylotic myelopathy.Both ventral and dorsal fusion procedures for CSM are effective at reducing the symptoms of myelopathy. The importance of cervical sagittal balance in predicting overall health-related quality of life outcome after ventral versus dorsal surgery for CSM has not been previously explored.A prospective, nonrandomized cohort of 49 patients undergoing dorsal and ventral fusion surgery for CSM was examined. Preoperative and postoperative C2-C7 sagittal vertical axis was measured on standing lateral cervical spine radiographs. Outcome was assessed with 2 disease-specific measures-the modified Japanese Orthopedic Association scale and the Oswestry Neck Disability Index and 2 generalized outcome measures-the Short-Form 36 physical component summary (SF-36 PCS) and Euro-QOL-5D. Assessments were performed preoperatively, and at 3 months, 6 months, and 1 year postoperatively. Statistical analyses were performed using SAS version 9.3 (SAS Institute).Most patients experienced improvement in all outcome measures regardless of approach. Both preoperative and postoperative C2-C7 sagittal vertical axis measurements were independent predictors of clinically significant improvement in SF-36 PCS scores (P = 0.03 and P = 0.02). The majority of patients with C2-C7 sagittal vertical axis values greater than 40 mm did not improve from an overall health-related quality of life perspective (SF-36 PCS) despite improvement in myelopathy. The postoperative sagittal balance value was inversely correlated with a clinically significant improvement of SF-36 PCS scores in patients undergoing dorsal surgery but not ventral surgery (P = 0.03 vs. P = 0.93).Preoperative and postoperative sagittal balance measurements independently predict clinical outcomes after surgery for CSM.2.

Project description:BackgroundDegenerative Cervical Myelopathy (DCM) is one of the most common degenerative disorders of cervical spine and sources of cord dysfunction in adults. It usually manifests with neurologic presentations such as loss of dexterity and gait issues. Treatment for moderate and severe cases of DCM is surgical decompression of the region. There are many approaches available for surgical intervention which could be categorized into anterior and posterior based on the side of neck where operation takes place. Additionally, for certain cases the hybridized anterior-posterior combined surgery is indicated. While there are many technical differences between these approaches with each having its own advantages, the complications and safety profiles of them are not fully disclosed. This protocol aims to systematically search for current reports on complications of surgical decompression methods of DCM and pool them for robust evidence generation.MethodSearch will be carried out in PubMed, Scopus, and Cochrane databases for retrospective and prospective surgical series, cohorts, or trials being performed for DCM with at least a sample size of 20 patients. Query strings will be designed to capture reports with details of complications with no year limit. Studies not being original (e.g., review articles, case reports, etc.), not in English, having patients younger than 18-years-old, and not reporting at least one complication will be excluded. Two independent reviewers will review the titles and abstracts for first round of screening. Full text of retrieved studies from previous round will be screened again by the same reviewers. In case of discrepancy, the third senior reviewer will be consulted. Eligible studies will then be examined for data extraction where data will be recorded into standardized form. Cumulative incidence and 95% confidence intervals of complication will be then pooled based on generalized linear mixed models with consideration of approach of surgery as subgroups. Heterogeneity tests will be performed for assessment of risk of bias.DiscussionThis systematic review is aimed at providing practical information for spine surgeons on the rates of complications of different surgical approaches of DCM decompression. Proper decision-making regarding the surgical approach in addition to informing patients could be facilitated through results of this investigation.

Dataset Information

Paired and matched analysis of neurological outcomes in revision surgery for cervical myelopathy following delayed neurological decline.

Background

Methods

Results

Conclusions

Publications

Paired and matched analysis of neurological outcomes in revision surgery for cervical myelopathy following delayed neurological decline.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets