Spine最新文献

Study design: Retrospective cohort study.

Objective: To identify factors associated with heterotopic ossification (HO) formation following cervical disc arthroplasty (CDA), including postoperative non-steroidal anti-inflammatory drug (NSAID) use.

Summary of background data: CDA preserves segmental motion in treating cervical degenerative disc disease but is susceptible to HO formation, which may compromise surgical outcomes. While NSAID prophylaxis is well-established in total hip arthroplasty to reduce HO risk, its role in CDA remains underexplored.

Methods: A retrospective review was conducted at a single academic center using a maintained surgical registry. Patients undergoing CDA with at least 1-2 years of radiographic follow-up were included. Demographic variables, BMI, implant type, operative levels, and NSAID use (any reason vs. specifically for HO prophylaxis) within 48 hours postoperatively were collected. Radiographs were graded for HO severity using the McAfee classification. Two logistic regression analyses assessed associations between variables and HO formation. Patient-reported outcome measures (PROMs) and rates of complications and reoperations were compared between HO-positive and HO-negative groups.

Results: Among 140 patients, 43.6% developed HO. HO presence was associated with higher age (P=0.025), higher BMI (P=0.002), and lower NSAID use both overall (P=0.018) and specifically for HO prophylaxis (P=0.005). Logistic regression confirmed that higher BMI was associated with increased HO risk, while postoperative NSAID use was associated with reduced risk. Clinical outcomes and PROMs improved significantly over time in both HO+ and HO- groups, with no significant differences in outcomes, complications, or reoperations.

Conclusion: Following multivariate analysis, higher BMI is associated with increased risk of HO following CDA, while early postoperative NSAID use is associated with a lower incidence. Clinical outcomes were similar between HO presence and absence groups. These findings support the potential role of NSAID prophylaxis in reducing HO development and guiding postoperative management following CDA.

Level of evidence: 3.

Study design: Anatomical and histological study of human cadaveric specimens.

Objective: To clarify the detailed anatomy of the cervical ligamentum flavum (LF), evaluate its presence at the craniocervical junction, and describe novel cervicodural ligaments with potential clinical implications.

Summary of background data: The cervical ligamentum flavum is clinically important yet remains anatomically controversial, particularly regarding its presence and morphology at C1.

Methods: Twelve adult cadaveric necks were examined (six gross dissections, six histological analyses). Specimens were sectioned coronally, sagittally, and axially. Masson's trichrome staining was used to identify ligamentous structures and their relationships with adjacent tissues.

Results: A distinct LF was consistently present between C2 and C7 vertebrae, attaching to adjacent laminae, blending laterally with the capsular ligament, and posteriorly with the interspinous ligament. No LF was identified at C0-C1. Instead, fibrous connections extended from the posterior arch of C1 and the lamina of C2 to the dura, forming previously undescribed atlantodural and axiodural ligaments. These cervicodural ligaments created a thickened dural region at C1-C2 and contained muscle fibers corresponding to the myodural bridge. A midline gap was observed between the right and left LF, traversed by vascular structures supplying the posterior cervical elements.

Conclusions: The cervical LF is absent at C0-C1 (i.e. posterior atlanto-occipital membrane), where novel cervicodural ligaments connect C1-C2 to the dura. These findings refine the surgical anatomy of the craniocervical junction and may provide an anatomical basis for cervicogenic headache.

Study design: A prospective cohort study (Level 3).

Objective: To develop and validate a clinical scoring system (Osteoporotic Vertebral Fracture Conservative Treatment Prognosis Score, OVF-CTPS) for predicting the prognosis of conservative treatment in patients with osteoporotic vertebral fractures (OVFs), addressing clinical uncertainty in treatment selection.

Summary of background data: OVFs face uncertainty in choosing conservative vs. surgical management. 10-40% of patients have conservative treatment failure (e.g., non-union, collapse). Existing classification systems lack prognostic value, highlighting the need for a practical predictive tool.

Methods: 201 patients with acute OVFs undergoing conservative treatment were prospectively followed for 6 months. Baseline assessments included demographics, pain, quality of life measures, and multimodal imaging (X-ray, CT, MRI). The primary outcome was conservative treatment failure. Independent predictors were identified using multivariate logistic regression and weighted to create the OVF-CTPS, which was validated using receiver operating characteristic (ROC) analysis.

Results: The conservative treatment failure rate was 29.9%. Six independent predictors were identified: Sugita "bow-shaped" or "concave" type, standing vertebral collapse degree <80%, middle column/posterior wall injury, T2WI "diffuse low" signal, STIR linear black signal, and basivertebral foramen involvement. The OVF-CTPS (range 0-13) demonstrated excellent predictive performance (AUC=0.918). At an optimal cutoff score of 6, the sensitivity was 91.3% and specificity was 84.0%. The low-risk group (score <6) had a 96.3% treatment success rate, while the high-risk group (score ≥6) had a success rate of 32.3.

Conclusion: The OVF-CTPS is a validated prognostic tool that integrates fracture morphology, injury severity, and MRI-based perfusion markers. It accurately stratifies patients based on their risk of conservative treatment failure, enabling clinicians to identify low-risk patients suitable for conservative care and high-risk patients who may benefit from early surgical evaluation.

Study design: Retrospective cohort study from a single academic institution.

Objective: To identify clinical and radiographic predictors for sacral extension (SE) during revision lumbar fusion.

Background: Lumbar fusion is common, with revision rates up to 25.9% within two years. When planning a revision of lumbar fusion, surgeons may extend constructs from L5 to the sacrum to improve stability, decompression, or alignment, but sacral extension alters biomechanics and increases risks such as pseudoarthrosis, adjacent segment disease, and proximal junctional kyphosis. Predictors for sacral extension during revision remain poorly defined.

Methods: Adult patients undergoing anterior or transforaminal lumbar interbody fusion (ALIF or TLIF) between 2017-2022 at a single academic institution, and those referred for revision with sacral extension, were reviewed. Eligible patients had an index fusion spanning L1-L4 to L5 or above. Sacral extension was defined as instrumentation to S1 or the pelvis within two years. Demographics, frailty indices, radiographic parameters, and complications were collected. Operative notes were reviewed to identify indications. Analyses included t-tests, chi-square, and multivariable logistic regression.

Results: Of 181 patients, 50 (27.6%) underwent SE and 131 (72.4%) remained fused between L1-L5. SE patients had higher frailty scores (MFI-5, P=0.018) and lower L4-L5 lordosis (P=0.020). Independent predictors included increased frailty (OR 7.015, P=0.032), greater fusion length (OR 1.796, P=0.012), and reduced L4-S1 lordosis (OR 1.137, P=0.007). Closer alignment of L1PA to ideal was protective (OR 0.81 per degree, P=0.009). Common indications were distal junctional degeneration (58%), foraminal stenosis (40%), and pseudoarthrosis (38%).

Conclusion: Frailty, longer constructs, and inadequate caudal lordosis independently predicted sacral extension during revision, while optimal L1PA alignment was protective. The most common indications were distal junctional degeneration, pseudoarthrosis, foraminal stenosis, and spondylolisthesis. These findings may aid preoperative risk stratification and surgical planning.

Study design: Prospective questionnaire development and validation study.

Objective: To develop and validate a new "Forgotten Spine Surgery Score for Cervical Spine Surgery" (FS3-C) to assess patient outcomes after CDR beyond the traditional measures: the ability to forget the presence of the implanted disc in daily life.

Summary of background data: The Forgotten Joint Score (FJS) has demonstrated superior discrimination in high-functioning total joint arthroplasty patients due to low ceiling and floor effects compared to legacy patient-reported outcome measures (PROMs). To date, there is no similar outcome measure to assess "forgottenness" following spine surgery. Such measures may be critical for evaluating subtle differences in high-performance surgeries like cervical disc replacement (CDR).

Methods: A 20-item pilot questionnaire was developed based on published patient expectations and expert opinion, utilizing a 5-point Likert scale. This was administered to 41 patients who underwent primary one- or two-level CDR (minimum 3-month follow-up, 2016-2023) for item selection and internal validity assessment. The final 12-item FS3-C was validated in 97 patients and correlated with the neck disability index (NDI) to determine convergent validity.

Results: In the pilot cohort (mean age 44.7±7.9 y), four items were excluded due to high missing responses or ceiling effects. In the pilot cohort (mean age 44.7±7.9 y), four items were excluded due to high missing responses or ceiling effects. The remaining 16 items demonstrated high internal consistency (Cronbach's alpha 0.95-0.96). Pairwise correlation analysis reduced the questionnaire to 12 items. In the validation cohort (mean age 44.3±9.0 y, 56.7±24.2 mo post-surgery), FS3-C demonstrated high internal consistency with minimal ceiling effects. Mean FS3-C and NDI scores were 86.4±18.9 and 9.8±12.6, respectively, with strong correlation (r=-0.606, P<0.001).

Conclusion: The FS3-C demonstrates high internal consistency, low ceiling effects, and strong convergent validity with NDI, enabling spine surgeons to evaluate CDR success beyond traditional symptom improvement measures.

Level of evidence: II.

Study design: Retrospective database study.

Objective: To characterize national trends in advanced practice provider (APP) utilization, reimbursement, and procedural roles in spine care, and assess regional variation relative to provider shortage areas.

Summary of background data: Advanced practice providers (APPs), including physician assistants (PAs) and nurse practitioners (NPs), are increasingly integral to surgical care delivery in the United States. With rising demand for spine services and persistent physician shortages-particularly in underserved regions-APPs have expanded their clinical and procedural responsibilities. However, national patterns in APP participation and financial sustainability within spine care remain underexplored.

Methods: Medicare claims from 2005-2016 were retrospectively analyzed using the PearlDiver database. Spine procedures involving APPs were identified through CPT codes and modifier flags, categorized as assistant-at-surgery, imaging, or injections. Claims were aggregated by year, region, and modifier type. Geographic analyses incorporated Health Professional Shortage Area (HPSA) scores to evaluate workforce disparities. Financial trends were assessed through total charges, reimbursements, and reimbursement-to-charge ratios. Utilization trajectories were projected through 2030.

Results: APP-managed spine claims increased 7.8% annually and are projected to rise 74% by 2030. Growth occurred across diagnostic (73%), therapeutic (74%), and perioperative (103%) service categories. Geographic variation was substantial, with over half of states demonstrating composite HPSA-adjusted utilization scores below 0.4, concentrated in the South and Southeast. Despite rising utilization, reimbursement-to-charge ratios declined for most services-falling 54% for imaging and 44% for injections-while assistant-at-surgery reimbursements increased 14%.

Conclusion: APPs are assuming a rapidly expanding role in spine care, particularly in regions with significant provider shortages. Yet declining reimbursement relative to charges threatens long-term financial sustainability. Strategic scope-of-practice optimization, equitable reimbursement reform, and targeted workforce deployment are needed to ensure continued access and equity in spine care delivery.

Level of evidence: III.

Study design: A national retrospective observational cohort study was conducted using the Merative MarketScan database.

Objective: To evaluate whether preoperative ED use was predictive of postoperative readmission, reoperation, and increased overall cost in patients undergoing transforaminal lumbar interbody fusion (TLIF).

Summary of background data: Emergency department (ED) visits and unplanned readmissions after spine surgery are increasingly financially relevant, as the Centers for Medicare and Medicaid Services levies financial penalties for unplanned 30-day hospital readmission.

Methods: The dataset was queried for transforaminal lumbar interbody fusion (TLIF) performed between July 1, 2018 and June 30, 2023. The primary outcome was any postoperative ED visit. Secondary outcomes included 30-day readmissions, 90-day readmissions, major medical complications, any reoperation, and 30-day episode of care costs.

Results: A total of 25,203 patients underwent TLIF during the study period. Multivariate logistic regression demonstrated that at least one preoperative ED visit was predictive for any postoperative ED visit and was also predictive of 30-day complication, 90-day readmission, and 90-day reoperation. A diagnosis of CKD was the strongest predictor for 30-day complication. Multivariate linear regression for total 30-day episode-of-care cost demonstrated that a preoperative ED visit was associated with a $2,806.71 increase in total cost.

Conclusions: We found that any preoperative ED visit was associated with higher 30-day episode of care costs, increased postoperative ED visits, and higher 90-day readmissions and reoperations following TLIF. Preoperative ED visits may serve as an indicator for unnecessary postoperative 30-day-episode of care utilization. This represents an opportunity for preoperative counseling and intervention to close care gaps and decrease unnecessary healthcare expenditures.

Level of evidence: III.

Study design: A retrospective multicenter study.

Objective: To identify independent risk factors for Spinal Epidural Lipomatosis (SEL) and to develop and validate an interpretable machine learning-based predictive model.

Summary of background data: SEL is an underdiagnosed yet clinically significant cause of debilitating lumbar spinal stenosis. Robust tools for early identification and risk stratification of at-risk patients are currently lacking.

Methods: Using data from 774 patients with low back and leg pain who underwent lumbar MRI at five institutions, we applied LASSO regression for variable selection and developed a clinically accessible nomogram. The cohort was randomly divided into training (70%) and validation (30%) sets. Four machine learning models were constructed and evaluated based on discrimination (AUC), calibration, and clinical utility (decision curve analysis).

Results: Seven independent predictors were identified: elevated random blood glucose, blood type B, atherosclerosis index, body mass index, uric acid, obstructive sleep apnea, and age. The XGBoost model demonstrated superior predictive performance in the validation set (AUC: 0.726; 95% CI: 0.547-0.904), with satisfactory calibration and positive net clinical benefit. Interpretability analysis confirmed glucose, age, and uric acid as the most consistent contributors to individualized risk predictions.

Conclusions: We developed and validated an interpretable prediction model that integrates clinical risk factors with an XGBoost algorithm and provides an actionable nomogram. This tool demonstrates strong potential to assist clinicians in early SEL detection and risk-stratified management, potentially enabling more targeted interventions for this underdiagnosed condition.

Study design: Large multicenter prospective study.

Objective: We aimed to develop and validate a novel machine learning-based prognostic scoring system for spinal metastases.

Summary of background data: Spinal metastases, common complications in patients with advanced cancer, significantly affect neurological function, pain, and quality of life. Although surgery plays a crucial role in selected cases, the accurate prediction of patient prognosis remains challenging. Traditional scoring systems, developed for older treatment paradigms, do not fully reflect the impact of modern oncologic therapies.

Methods: This multicenter prospective study, conducted by the Japan Association of Spine Surgeons with Ambition, included 401 patients who underwent surgery for spinal metastases at 35 medical centers between 2018 and 2021. Patient demographics, tumor burden, performance status, and treatment history data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression was used to identify significant predictors of one-year survival, followed by stepwise variable selection. The model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and calibration plots.

Results: Among the 401 patients, 67.1% survived for one year, whereas 32.9% did not. Survivors had better performance status, lower tumor burden, and lower opioid use than non-survivors. LASSO regression identified five key predictors of one-year survival: age ≥75 years, poor performance status (≥3), presence of other bone metastases, preoperative opioid use, and lower preoperative Vitality Index. The final model demonstrated a strong predictive performance (AUROC=0.762). Based on the key prognostic factors, a simplified risk stratification system was developed to classify patients into low- (one-year survival 82.2%), intermediate- (67.2%), and high-risk (34.2%) groups.

Conclusion: We developed a clinically applicable prognostic scoring system for patients with spinal metastases using machine learning techniques to enhance predictive accuracy. This model provides a practical risk assessment tool to aid surgical decision-making and optimize postoperative management.

Level of evidence: 2.