Degenerative cervical myelopathy

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

BACKGROUND

Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

METHODS

The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.

RESULTS

For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort).

CONCLUSION

We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature

STUDY DESIGN

Topic modeling of literature.

OBJECTIVES

Our study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses.

METHODS

Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics".

RESULTS

Our analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics.

CONCLUSIONS

Our NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.

Comparison study of anterior cervical zero-profile fusion cage (ROI-C) and traditional titanium plate plus fusion technique for the treatment of spinal cord type cervical spondylosis

A retrospective comparative study. To compare and analyze the differences in the efficiency and safety of ROI-C and traditional titanium plate with fusion cage for the treatment of CSM patients. Clinical data of 105 patients with CSM who underwent surgical treatment at our hospital from January 2019 to December 2020 were retrospectively reviewed. Patients were divided into ROI-C and traditional groups according to the different fusion methods. The operation time, intraoperative blood loss, preoperative and postoperative JOA score, NDI score, cervical Cobb angle, intervertebral space height, and postoperative complications were recorded and compared between the 2 groups. A total of 105 patients were included in this study, with 57 patients in the ROI-C group and 48 patients in the traditional group. The baseline data were similar between the 2 groups (P > .05). The operative time, intraoperative blood loss, and the incidence of postoperative dysphagia were significantly lower in the ROI-C group than in the traditional group (P < .05). There were no significant differences in the JOA score, NDI score, cervical Cobb angle, intervertebral space height, the incidence of postoperative axial symptoms, and adjacent segment degeneration between the 2 groups (P > .05). However, both groups showed significant improvement in the JOA score, NDI score, cervical Cobb angle, and intervertebral space height compared with before surgery (P < .05). The ROI-C zero-profile internal fixation system and traditional titanium plates with fusion cages can achieve satisfactory clinical treatment results for CSM patients. However, ROI-C has advantages of a shorter operative time, less blood loss, and less postoperative dysphagia. Therefore, the ROI-C zero-profile internal fixation system can be safely and effectively used to treat patients with CSM.

Degenerative cervical myelopathy: Where have we been? Where are we now? Where are we going?

Degenerative cervical myelopathy (DCM), a recently coined term, encompasses a group of age-related and genetically associated pathologies that affect the cervical spine, including cervical spondylotic myelopathy and ossification of the posterior longitudinal ligament (OPLL). Given the significant contribution of DCM to global disease and disability, there are worldwide efforts to promote research and innovation in this area. An AO Spine effort termed 'RECODE-DCM' was initiated to create an international multistakeholder consensus group, involving patients, caregivers, physicians and researchers, to focus on launching actionable discourse on DCM. In order to improve the management, treatment and results for DCM, the RECODE-DCM consensus group recently identified ten priority areas for translational research. The current article summarizes recent advancements in the field of DCM. We first discuss the comprehensive definition recently refined by the RECODE-DCM group, including steps taken to arrive at this definition and the supporting rationale. We then provide an overview of the recent advancements in our understanding of the pathophysiology of DCM and modalities to clinically assess and diagnose DCM. A focus will be set on advanced imaging techniques that may offer the opportunity to improve characterization and diagnosis of DCM. A summary of treatment modalities, including surgical and nonoperative options, is then provided along with future neuroprotective and neuroregenerative strategies. This review concludes with final remarks pertaining to the genetics involved in DCM and the opportunity to leverage this knowledge toward a personalized medicine approach.

Factors Associated With the Rate of Recovery After Cervical Decompression Surgery for Degenerative Cervical Myelopathy: A Retrospective Analysis

Introduction Degenerative cervical myelopathy (DCM) is a debilitating spinal condition with a wide variety of symptoms that can differ greatly among individuals. Common symptoms include numbness, extremity weakness, loss of balance, and gait instability. Decompression surgeries are commonly indicated for the treatment of DCM with varying outcomes reported in the literature. However, there is little evidence on the rate of recovery defined as the time until improvement in symptoms such as numbness, balance, and strength after surgery for DCM. The purpose of this study was to determine the rate of neurological recovery after surgery for DCM and its subsequent association with various risk factors to guide clinicians while providing care and improve patient education. Methods This study was a retrospective case series (n=180 patients) examining patients who underwent cervical decompression surgery for DCM. All patients had a clinical presentation of DCM, were diagnosed with DCM, had radiographic degenerative changes and cervical stenosis, and received surgical management from 2010 to 2020 in a tertiary hospital system. Data recorded included age, smoking status, duration of pre-operative symptoms, preoperative and postoperative pain, and postoperative rate of recovery (days until improvement) in numbness, upper extremity strength, and balance. Results Patients (n=180) had an average age of 65.7 years (SD ±9.2 years, range 43-93 years). The mean ± standard deviation for the rate of recovery (days until improvement) in numbness, upper extremity strength, and balance was 84.5 ± 94.4 days, 50.6 ± 42.8 days, and 60.4 ± 69.9 days, respectively. There was only a marginally significant association between the rate of recovery for numbness after surgery and patient age (p=0.053). The average rate of recovery in numbness for patients older than 60 years was significantly longer than those younger than 60 years (99.3 versus 60.2 days). Preoperative smoking status was significantly associated with persistent moderate to severe pain (p=0.032) within the six-month postoperative period. No significant correlations were seen between the rate of recovery for balance or strength and patient age or preoperative duration of symptoms. Conclusion There was great variability in the rate of recovery for postoperative symptoms after surgery for DCM. A longer time for improvement in postoperative numbness was only marginally correlated with the increased patient age after surgery for DCM. There was no correlation found between strength or balance recovery times and patient age. Smoking status was associated with moderate to severe postoperative pain after surgery for DCM. Furthermore, the duration of preoperative symptoms was not associated with improvement in postoperative symptoms after surgery for DCM. More research is needed to determine factors impacting the rate of recovery after surgery for DCM.