Prediction of the risk of C5 palsy after posterior laminectomy and fusion with cervical myelopathy using a support vector machine: an analysis of 184 consecutive patients

Wang, Haosheng; Tang, Zhi-Ri; Li, Wenle; Fan, Tingting; Zhao, Jianwu; Kang, Mingyang; Dong, Ran; Qu, Yang

doi:10.1186/s13018-021-02476-5

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…10,13, 18, 19, 21, 22, 25,2 9-33, 36-38, 40,42 However, 5 studies required a grade of ≤ 3 or a reduction by > 2 grades on MMT for a patient to be diagnosed with postoperative C5P. 23,34,35,39,43 Additionally, 3 studies included potential sensory changes in their C5P definition. 16,43,46…”

Section: C5p Definitionsmentioning

confidence: 99%

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Defining postoperative C5 palsy and recovery: a systematic review

Deshpande¹,

Stino

Smith

et al. 2023

Journal of Neurosurgery: Spine

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OBJECTIVE Postoperative C5 palsy (C5P) is a well-recognized and often-delayed complication of cervical spine surgery. Most patients recover within 6 months of onset, but the prognosis of severe cases is poor. The clinical significance and natural history of mild versus severe C5P appear to differ substantially, but palsy severity and recovery have been poorly characterized in the literature. METHODS Owing to the varying prognoses and expanding treatment options such as nerve transfer surgery to reconstruct the C5 myotome, this systematic review attempted to describe how C5P severity is classified and how C5P and its recovery are defined, with the aim of proposing a postoperative C5P scale to support clinical decision-making. PubMed was searched for articles in English published since 2000 that offer a clear definition of postoperative C5P or its recovery. Only articles reporting exclusively on C5 palsy for patients undergoing surgery for degenerative disease were included. A single reviewer screened titles and abstracts and reviewed the full text of relevant articles, with consultation as needed from a second reviewer. Data collected included postoperative C5P definitions, classification of C5P severity, and definition and/or classification of C5P recovery. Qualitative analysis was performed. RESULTS Full-text reviews were conducted of 98 of 272 articles identified and screened, and 43 met the inclusion criteria. Postoperative C5P was most commonly defined as a reduction in deltoid muscle strength by ≥ 1 grade using manual muscle testing (MMT), with potential biceps involvement also noted by some studies. The few studies that stratified C5P on the basis of severity unanimously characterized severe C5P as MMT grade ≤ 2. Nine studies reported on C5P recovery. Deltoid muscle strength improvement of MMT grade 5 commonly defined complete recovery, with no MMT improvement considered partial recovery. CONCLUSIONS This review identified clear discrepancies in the definitions of C5P and its recovery, leading to heterogeneity in its evaluation and management. With the emergence of therapeutic procedures for severe C5P, standardization of the definitions of C5P and its recovery is critical. The authors propose MMT grades of 4, 3, and ≤ 2 to classify C5P as mild, moderate, and severe, respectively, and grades of 5, 4, and 3 to classify recovery as complete, sufficient, and useful, respectively.

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Section: C5p Definitionsmentioning

confidence: 99%

“…23,34,35,39,43 Additionally, 3 studies included potential sensory changes in their C5P definition. 16,43,46…”

Section: C5p Definitionsmentioning

confidence: 99%

Defining postoperative C5 palsy and recovery: a systematic review

Deshpande¹,

Stino

Smith

et al. 2023

Journal of Neurosurgery: Spine

View full text Add to dashboard Cite

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“…6 The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China. 7 Intelligent Healthcare Team, Baidu Inc, Beijing 100089, China. 8 Emergency Department, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan 750000, China.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…This reliable visual graph ensures that clinicians can conveniently and rapidly in get predictive results. By combining the incorporated predictors, the graph provides a personalized estimate of the risk of events, including the probability of disease recurrence or mortality, and is helpful in making scientifically sound clinical decisions and improving the prognosis of patients [ 6 , 7 ]. In recent years, web calculators have become an emerging tendency in clinical predictive modeling, with the advantage of reducing the learning costs required to assess patient survival.…”

Section: Introductionmentioning

confidence: 99%

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study

Dong

Lin

et al. 2022

BMC Cancer

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Objective The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES). Methods Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model. Results The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility. Conclusions The tool built in this paper to predict 1- and 3-year survival in ES patients (https://drwenleli0910.shinyapps.io/EwingApp/) has a good identification and predictive power.

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“…There are various statistical methods for estimating the probability of occurrence of binary events such as NVCF, the most commonly used being logistic regression (LR). With the rapid development in the field of artificial intelligence, machine learning (ML) is increasingly used in the medical field [ 8 , 9 ], in particular, when using large datasets for prediction [ 10 , 11 ]. However, it is unclear whether ML methods can provide better predictive power than traditional logistic regression algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

Yuan

et al. 2023

J Orthop Surg Res

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Background The use of machine learning has the potential to estimate the probability of a second classification event more accurately than traditional statistical methods, and few previous studies on predicting new fractures after osteoporotic vertebral compression fractures (OVCFs) have focussed on this point. The aim of this study was to explore whether several different machine learning models could produce better predictions than logistic regression models and to select an optimal model. Methods A retrospective analysis of 529 patients who underwent percutaneous kyphoplasty (PKP) for OVCFs at our institution between June 2017 and June 2020 was performed. The patient data were used to create machine learning (including decision trees (DT), random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), neural networks (NNET), and regularized discriminant analysis (RDA)) and logistic regression models (LR) to estimate the probability of new fractures occurring after surgery. The dataset was divided into a training set (75%) and a test set (25%), and machine learning models were built in the training set after ten cross-validations, after which each model was evaluated in the test set, and model performance was assessed by comparing the area under the curve (AUC) of each model. Results Among the six machine learning algorithms, except that the AUC of DT [0.775 (95% CI 0.728–0.822)] was lower than that of LR [0.831 (95% CI 0.783–0.878)], RA [0.953 (95% CI 0.927–0.980)], GBM [0.941 (95% CI 0.911–0.971)], SVM [0.869 (95% CI 0.827–0.910), NNET [0.869 (95% CI 0.826–0.912)], and RDA [0.890 (95% CI 0.851–0.929)] were all better than LR. Conclusions For prediction of the probability of new fracture after PKP, machine learning algorithms outperformed logistic regression, with random forest having the strongest predictive power. Graphical Abstract

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Prediction of the risk of C5 palsy after posterior laminectomy and fusion with cervical myelopathy using a support vector machine: an analysis of 184 consecutive patients

Cited by 13 publications

References 39 publications

Defining postoperative C5 palsy and recovery: a systematic review

Defining postoperative C5 palsy and recovery: a systematic review

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

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