A retrospective study to evaluate the clinical results of patients with osteoporosis and various spinal diseases treated surgically with polymethylmethacrylate (PMMA) augmented pedicle screw. To report a novel technique using PMMA for pedicle screw augmentation in osteoporotic spinal surgery. Many studies have proved that the stiffness and strength of pedicle screw fixation can be significantly increased when the pedicle screw is augmented with various cements. However, most of those studies were experimental. Clinical reports using those materials for pedicle screw augmentation are rare and a practical and reliable technique for primary pedicle screw augmentation with cement has not yet been established. Forty-one patients [23 female, 18 male, mean age 75.1 (50-90) years] with osteoporosis and various spinal diseases underwent spinal decompression and instrumentation with PMMA augmentation of pedicle screw. Pre-and postoperative scores for visual analogue scale for pain and Oswestry disability index questionnaire were analyzed. The screw migration, which is the distance from the screw tip to the anterior cortex and upper endplate of vertebra, was also evaluated immediately after the operation and at the mean 22.3 months final follow-up. RESULTS.: Totally 291 of 300 screws were augmented with PMMA. There was neither neurologic deterioration nor symptomatic cement leakage after surgery. The mean visual analogue scale pain score of these patients improved from 9.2 to 1.5 (P < 0.01) and the functional Oswestry disability index score improved from 77.5% to 44.2% (P < 0.01). Kyphotic deformity was improved from average 23.2 degrees to 11.9 degrees after surgery, and to 14.9 degrees at final follow-up (P < 0.01). The average loss of kyphosis correction was 3 degrees. There was no significant screw migration when the screws distances just after operation and at the final follow-up were compared (P > 0.01). The presented technique of PMMA for augmentation of pedicle screw is a safe, reliable, and practical technique for osteoporotic patients who also had various spinal diseases and need spinal instrumentation.

Study Design.: A retrospective cohort study.Objective.: To determine the risk factors of cage subsidence in patients undergoing minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) and its correlation with patient-reported outcomes.Summary of Background Data.: Cage subsidence is among the cage-related complications after TLIF and may lead to poor outcomes. Few studies have addressed the incidence of cage subsidence in MI-TLIF.Methods.: This retrospective study of a prospectively collected database was conducted from October 2015 to October 2017. All patients received MI-TLIF with a minimum of 2-year follow-up. All levels were separated into the cage subsidence (CS group) and no cage subsidence (non-CS group) groups. Cage subsidence was evaluated using lateral radiographs and defined as more than 2 mm migration of the cage into the endplate of adjacent vertebral body. Patient demographics, perioperative details, and radiographic parameters were recorded. Cage-related parameters were cage height, cage insertion level, and cage position. Cage position was recorded using central point ration (CPR). Patient-reported outcome was analyzed using the Oswestry Disability Index (ODI) questionnaire and Visual Analog Scale (VAS) preoperatively and at 2 years postoperatively.Results.: Ninety-three patients (126 levels) were included. Mean age was 66.5 years with an average follow-up of 36.9 months. Overall incidence of cage subsidence was 34.1%. The CS group had significantly higher body mass index, less bone mineral density (BMD), shorter disc height, and higher CPR than the non-CS group. BMD, disc height, and CPR were significantly negatively correlated with depth of cage subsidence. ODI improvement was significantly lesser in the CS group than in the non-CS group. Fusion rate and complications were unrelated to cage subsidence.Conclusion.: The BMD, disc height, and cage position were the most significant risk factors that were negatively correlated with depth of cage subsidence. Placing a TLIF cage anteriorly if possible may reduce the risk of cage subsidence.Level of Evidence:3

We retrospectively compared magnetic resonance images (MRIs) of tuberculous (TB) spondylitis and pyogenic spondylitis. To identify differences between the diseases. Clinical and imaging findings of the 2 diseases are hard to distinguish. MRI may show important differences and aid in early diagnosis and treatment. We compared 22 MRI parameters in 33 patients with TB spondylitis (average age, 66 years) and in 33 patients with pyogenic spondylitis (average age, 65 years). Seventeen parameters significantly differed between the groups. The most important were local and heterogeneous enhancement of the vertebral body in all patients with TB spondylitis, diffuse and homogeneous enhancement of the vertebral body in 94% of patients with pyogenic spondylitis, vertebral intraosseous abscess with rim enhancement (TB vs. pyogenic, 79% vs. 0%), disc abscess with rim enhancement (9% vs. 64%), and well-defined paraspinal abnormal signal intensity (82% vs. 18%). Most parameters occurred in both diseases. Distinctive findings were a pattern of bone destruction with relative disc preservation and heterogeneous enhancement for TB spondylitis and a diskitis pattern (disc destruction) with peridiscal bone destruction and homogeneous enhancement for pyogenic spondylitis. These MRI parameters may facilitate the differential diagnosis of these diseases.

Background: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. Questions/purposes: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients’ clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? Methods: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. Results: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. Conclusion: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. Level of Evidence: Level II, diagnostic study.

Study Design. A retrospective review and analysis of consecutive patients who underwent single-level vertebroplasty at our institute between March 2002 and March 2006. Objective. To analyze the risk factors for subsequent fractures after vertebroplasty and to predict the postoperative fracture-free time and rate. The effect of bone cement volume injected was also evaluated. Summary of Background Data. Previous studies of subsequent fractures after vertebroplasty showed conflicting conclusions about risk factors. The frequency of refracture also varied, ranging from 12% to 52%. Most new fractures occurred at adjacent levels, with different risk factors identified. No data were available on the effect of injected bone cement volume, and no consensus had been reached as to the optimal cement volume. Methods. All enrolled patients were treated with single-level vertebroplasty and followed a standardized postoperative care protocol. Data from medical records and radiographs were collected and analyzed. Variables included patient constitutional factors, radiographic parameters, and volume of injected bone cement. Results. A total of 166 patients (76 men, 90 women) with a mean age of 73.4 years were enrolled in this study. The mean follow-up time was 15.3 months. The overall refracture rate was 38%, with a mean fracture-free interval of 32 months. Both a greater volume of bone cement injected and a greater degree of vertebral height restored contributed significantly to the risk of subsequent adjacent fracture. No risk factor for subsequent remote fracture was identified. A greater volume of bone cement injected was positively correlated with deformity correction after vertebroplasty. Conclusion. Most subsequent fractures occurred at the adjacent level within the first 3 months. Patient preoperative condition did not help predict refracture. Although a greater volume of bone cement injected when performing vertebroplasty contributed to the risk of subsequent adjacent fracture, it resulted in a greater improvement of kyphosis.