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.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

Comprehensive assessment of atypical and anaplastic pediatric meningiomas utilizing national cancer database: a retrospective cohort study.

PURPOSE: To examine demographic and clinical characteristics and their association with survival in grade 2 and 3 pediatric meningiomas in a large cohort using the National Cancer Database (NCDB). METHODS: We conducted a comprehensive analysis using data from NCDB between 2004 to 2018. Tumor-specific data included tumor grade and size. Treatment details, including surgical resection, extent of resection, and radiotherapy, were gathered. Our analytic approach incorporated logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: Among the included 239 patients aged 0-21 years, age category distribution was significantly different between grade 2 and grade 3 tumors (p = 0.018). For grade 2 meningiomas, 51.5% of patients were female, and 76.7% were white. 85.3% of patients with grade 2 meningiomas underwent surgical resection, of which 67% underwent gross total resection. Overall survival (OS) was significantly different between resected and non-resected patients (p = 0.048). Uninsured patients were over seven times as likely to have prolonged length of stay (LOS) versus those with private insurance (OR = 7.663, p = 0.014). For grade 3 meningiomas, 51.4% of patients were male, and 82.9% were white. 91.4% of patients with grade 3 meningiomas underwent surgical resection, of which 53.3% underwent subtotal resection. OS was not significantly different between resected and non-resected patients (p = 0.659). CONCLUSION: In summary, there were significant differences in age, maximum tumor dimension, unplanned readmission, radiotherapy, and treatment combinations between grade 2 and 3 meningiomas. These findings highlight the intricacies of managing pediatric meningiomas and emphasize the necessity for tailored therapeutic approaches to enhance outcomes in the future.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

STUDY DESIGN: Retrospective, population-based cohort study. OBJECTIVE: This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. SUMMARY OF BACKGROUND DATA: Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. METHODS: Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. RESULTS: From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. CONCLUSIONS: This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research.

Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.

Scoping Review with Topic Modeling on the Diagnostic Criteria for Degenerative Cervical Myelopathy.

STUDY DESIGN: This study is a scoping review. OBJECTIVE: There is a broad variability in the definition of degenerative cervical myelopathy (DCM) and no standardized set of diagnostic criteria to date. METHODS: We interrogated the Myelopathy.org database, a hand-indexed database of primary clinical studies conducted exclusively on DCM in humans between 2005-2021. The DCM inclusion criteria used in these studies were inputted into 3 topic modeling algorithms: Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), and BERtopic. The emerging topics were subjected to manual labeling and interpretation. RESULTS: Of 1676 reports, 120 papers (7.16%) had well-defined inclusion criteria and were subjected to topic modeling. Four topics emerged from the HDP model: disturbance from extremity weakness and motor signs; fine-motor and sensory disturbance of upper extremity; a combination of imaging and clinical findings is required for the diagnosis; and "reinforcing" (or modifying) factors that can aid in the diagnosis in borderline cases. The LDA model showed the following topics: disturbance to the patient is required for the diagnosis; reinforcing factors can aid in the diagnosis in borderline cases; clinical findings from the extremities; and a combination of imaging and clinical findings is required for the diagnosis. BERTopic identified the following topics: imaging abnormality, typical clinical features, range of objective criteria, and presence of clinical findings. CONCLUSIONS: This review provides quantifiable data that only a minority of past studies in DCM provided meaningful inclusion criteria. The items and patterns found here are very useful for the development of diagnostic criteria for DCM.

Ultra-Early (<5 Hours) Decompression for Thoracolumbar Spinal Cord Injury: A Case Series.

Early surgical decompression within 24 hours for traumatic spinal cord injury (SCI) is associated with improved neurological recovery. However, the ideal timing of decompression is still up for debate. The objective of this study was to utilize our retrospective single-institution series of ultra-early (<5 hours) decompression to determine if ultra-early decompression led to improved neurological outcomes and was a feasible target over previously defined early decompression targets. Retrospective data on patients with SCI who underwent ultra-early (<5 hours) decompression at a level one metropolitan trauma center were extracted and collected from 2015-2018. American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade improvement was the primary outcome, with ASIA Motor score improvement and complication rate as secondary outcomes. Four individuals met the criteria for inclusion in this case series. All four suffered thoracolumbar SCI. All patients improved neurologically by AIS grade, and there were no complications directly related to ultra-early surgery. Given the small sample size, there was no statistically significant difference in outcomes compared to a control group who underwent early (5-24 hour) decompression in the same period. Ultra-early decompression is a feasible and safe target for thoracolumbar SCI and may lead to improved neurological outcomes without increased risk of complications. This case series can help create the foundation for future, larger studies that may definitively show the benefit of ultra-early decompression.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models. METHODS: Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented. RESULTS: A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome. CONCLUSIONS: Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients.

BACKGROUND CONTEXT: Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes. OBJECTIVE: The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability. STUDY DESIGN: This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models. PATIENT SAMPLE: The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019. OUTCOME MEASURES: The outcomes of interest were survival outcomes at three specific time points postdiagnosis: 1, 3, and 5 years. These outcomes were formed by combining the "Vital Status" and "Last Contact or Death (Months from Diagnosis)" variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precision-recall curves (PRCs), and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score. METHODS: We employed five ML algorithms-TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest-along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models. RESULTS: For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models. CONCLUSIONS: This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians (https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NCDB-SCG). Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability. This robust, interpretable methodology aligns with the goals of precision medicine, establishing a foundation for continued research leveraging ML's predictive power to enhance patient counseling.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

Cervical Spine Stenosis Causing Diaphragmatic Paralysis: A Case Study and Narrative Review of Clinical Presentations and Outcomes.

STUDY DESIGN: Case report and narrative review. OBJECTIVE: To explore the therapeutic role of surgical and nonsurgical treatment of diaphragmatic paralysis secondary to spinal cord and nerve root compression. SUMMARY OF BACKGROUND DATA: Phrenic nerve dysfunction due to central or neuroforaminal stenosis is a rare yet unappreciated etiology of diaphragmatic paralysis and chronic dyspnea. Surgical spine decompression, diaphragmatic pacing, and intensive physiotherapy are potential treatment options with varying degrees of evidence. METHODS: The case of a 70-year-old male with progressive dyspnea, reduced hemi-diaphragmatic excursion, and C3-C7 stenosis, who underwent a microscopic foraminotomy is discussed. Literature review (MEDLINE, PubMed, Google Scholar) identified 19 similar reports and discussed alternative treatments and outcomes. RESULTS AND CONCLUSIONS: Phrenic nerve root decompression and improvement in neuromonitoring signals were observed intraoperatively. The patient's postoperative course was uncomplicated, and after 15 months, he experienced significant symptomatic improvement and minor improvement in hemi-diaphragmatic paralysis and pulmonary function tests. All case reports of patients treated with spinal decompression showed symptomatic and/or functional improvement, while one of the 2 patients treated with physiotherapy showed improvement. More studies are needed to further describe the course and outcomes of these interventions, but early identification and spinal decompression can be an effective treatment. OCEBM LEVEL OF EVIDENCE: Level-4.

Trends in stroke-related journals: Examination of publication patterns using topic modeling.

OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. MATERIALS AND METHODS: Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. RESULTS: Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. CONCLUSIONS: The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.

Exploring the differences in radiologic and clinical outcomes of transforaminal lumbar interbody fusion with single- and bi-planar expandable cages: a systematic review and meta-analysis.

Transforaminal lumbar interbody fusion (TLIF) is a universal surgical technique used to achieve lumbar fusion. Traditionally static cages have been used to restore the disc space after discectomy. However, newer technological advancements have brought up uniplanar expandable cages (UECs) and more recently bi-planar expandable cages (BECs), the latter with the hope of reducing the events of intra- or postoperative subsidence compared to UECs. However, since BECs are relatively new, there has been no comparison to UECs. In this PRISMA-compliant systematic review, we sought to identify all Medline and Embase reports that used UECs and/or BECs for TLIF or posterior lumbar interbody fusion. Primary outcomes included subsidence and fusion rates. Secondary outcomes included VAS back pain score, VAS leg pain score, ODI, and other complications. A meta-analysis of proportions was the main method used to evaluate the extracted data. Bias was assessed using the ROBINS-I tool. A total of 15 studies were pooled in the analysis, 3 of which described BECs. There were no studies directly comparing the UECs to BECs. A statistically significant difference in fusion rates was found between UECs and BECs (p = 0.04). Due to lack of direct comparative literature, definitive conclusions cannot be made about differences between UECs and BECs. The analysis showed a statistically higher fusion rate for BECs versus UECs, but this should be interpreted cautiously. No other statistically significant differences were found. As more direct comparative studies emerge, future meta-analyses may clarify potential differences between these cage types.

Comparative Effectiveness of Open Versus Minimally Invasive Transforaminal Lumbar Interbody Fusion: An Umbrella Review of Meta-Analyses.

STUDY DESIGN: Umbrella review of meta-analyses. OBJECTIVE: To compile existing meta-analyses to provide analysis of the multiple postoperative outcomes in a comparison of open-transforaminal lumbar interbody fusions (O-TLIFs) versus minimally invasive transforaminal interbody fusions (MI-TLIFs). SUMMARY OF BACKGROUND DATA: TLIF is the standard surgical intervention for spinal fusion in degenerative spinal diseases. The comparative effectiveness of MI-TLIFs and O-TLIFs remains controversial. METHODS: A literature search was conducted in the PubMed, Scopus, and Web of Science databases. Titles and abstracts were initially screened, followed by a full-text review based on the inclusion criteria. Twenty articles were deemed eligible for the umbrella review. Data extraction and quality assessment using A Measurement Tool to Assess Systematic Reviews were performed. Effect sizes of the outcomes of interest from primary studies included in the meta-analyses were repooled. Repooling and stratification of the credibility of the evidence were performed using the R package metaumbrella. The pooled effect sizes were compared and interpreted using equivalent Hedges' g values. RESULTS: When the meta-analyses were pooled, MI-TLIF was found to have a shorter length of stay, less blood loss, and a higher radiation exposure time, with a highly suggestive level of evidence. Data regarding less postoperative drainage, infections, and Oswestry disability index for MI-TLIF were supported by weak evidence. Conversely, data regarding other postoperative outcomes were nonsignificant to draw any conclusions. CONCLUSION: Our umbrella review provides a comprehensive overview of the relevant strengths and weaknesses of each surgical technique. This overview revealed that MI-TLIF had better outcomes in terms of length of stay, blood loss, postoperative drainage, infections, and Oswestry disability index when compared with those of O-TLIF. However, O-TLIF had a better outcome for radiation exposure when compared with MI-TLIF.

Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study.

BACKGROUND: Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging. Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined. OBJECTIVE: This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features. METHODS: A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics. RESULTS: After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: χ22=159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM. CONCLUSIONS: Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition. However, the challenge lies in creating quality data sets necessary to derive benefit from such approaches.

Prognosis at Your Fingertips: A Machine Learning-Based Web Application for Outcome Prediction in Acute Traumatic Epidural Hematoma.

Traumatic brain injury (TBI) affects 69 million people worldwide each year, and acute traumatic epidural hematoma (atEDH) is a frequent and severe consequence of TBI. The aim of the study is to use machine learning (ML) algorithms to predict in-hospital death, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with atEDH and incorporate the resulting ML models into a user-friendly web application for use in the clinical settings. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with atEDH. Four ML algorithms (XGBoost, LightGBM, CatBoost, and Random Forest) were utilized, and the best performing models were incorporated into an open-access web application to predict the outcomes of interest. The study found that the ML algorithms had high area under the receiver operating characteristic curve (AUROC) values in predicting outcomes for patients with atEDH. In particular, the algorithms had an AUROC value range of between 0.874 to 0.956 for in-hospital mortality, 0.776 to 0.798 for non-home discharges, 0.737 to 0.758 for prolonged LOS, 0.712 to 0.774 for prolonged ICU-LOS, and 0.674 to 0.733 for major complications. The following link will take users to the open-access web application designed to generate predictions for individual patients based on their characteristics: huggingface.co/spaces/MSHS-Neurosurgery-Research/TQP-atEDH. This study aimed to improve the prognostication of patients with atEDH using ML algorithms and developed a web application for easy integration in clinical practice. It found that ML algorithms can aid in risk stratification and have significant potential for predicting in-hospital outcomes. Results demonstrated excellent performance for predicting in-hospital death and fair performance for non-home discharges, prolonged LOS and ICU-LOS, and poor performance for major complications.

Personalized Prognosis with Machine Learning Models for Predicting In-Hospital Outcomes Following Intracranial Meningioma Resections.

BACKGROUND: Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent intracranial meningioma resections from 2014 to 2020. We focused on 5 outcomes: prolonged LOS, nonhome discharges, 30-day readmissions, unplanned reoperations, and major complications. Six machine learning algorithms, including TabPFN, TabNet, XGBoost, LightGBM, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations were used to evaluate the importance of predictor variables. RESULTS: Our analysis included 7000 patients. Of these patients, 1658 (23.7%) had prolonged LOS, 1266 (18.1%) had nonhome discharges, 573 (8.2%) had 30-day readmission, 253 (3.6%) had unplanned reoperation, and 888 (12.7%) had major complications. Performance evaluation indicated that the top-performing models for each outcome were the models built with LightGBM and Random Forest algorithms. The LightGBM models yielded AUROCs of 0.842 and 0.846 in predicting prolonged LOS and nonhome discharges, respectively. The Random Forest models yielded AUROCs of 0.717, 0.76, and 0.805 in predicting 30-day readmissions, unplanned reoperations, and major complications, respectively. CONCLUSIONS: The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients.

Predicting 30-Day Non-Seizure Outcomes Following Temporal Lobectomy with Personalized Machine Learning Models.

BACKGROUND: Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. RESULTS: Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. CONCLUSIONS: Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm.

The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries.

OBJECTIVES: The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. METHODS: The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS: A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. CONCLUSIONS: ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.

Natural language processing reveals research trends and topics in The Spine Journal over two decades: a topic modeling study.

BACKGROUND CONTEXT: The field of spine research is rapidly evolving, with new research topics continually emerging. Analyzing topics and trends in the literature can provide insights into the shifting research landscape. PURPOSE: This study aimed to elucidate prevalent and emerging research topics and trends within The Spine Journal using a natural language processing technique called topic modeling. METHODS: We utilized BERTopic, a topic modeling technique rooted in natural language processing (NLP), to examine articles from The Spine Journal. Through this approach, we discerned topics from distinct keyword clusters and representative documents that represented the main concepts of each topic. We then used linear regression models on these topic likelihoods to trace trends over time, pinpointing both "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. Additionally, we conducted an in-depth review of the trending topics in the present decade. RESULTS: Our analysis led to the categorization of 3358 documents into 30 distinct topics. These topics spanned a wide range of themes, with the most commonly identified topics being "Outcome Measures," "Scoliosis," and "Intradural Lesions." Throughout the history of the journal, the three hottest topics were "Degenerative Cervical Myelopathy," "Osteoporosis," and "Opioid Use." Conversely, the coldest topics were "Intradural Lesions," "Extradural Tumors," and "Vertebral Augmentation." Within the current decade, the hottest topics were "Screw Biomechanics," "Paraspinal Muscles," and "Biologics for Fusion," whereas the cold topics were "Intraoperative Blood Loss," "Construct Biomechanics," and "Material Science." CONCLUSIONS: This study accentuates the dynamic nature of spine research and the changing focus within the field. The insights gleaned from our analysis can steer future research directions, inform policy decisions, and spotlight emerging areas of interest. The implementation of NLP to synthesize and analyze vast amounts of academic literature exhibits the potential of advanced analytical techniques in comprehending the research landscape, setting a precedent for similar analyses across other medical disciplines.