Asian Spine Journal最新文献

Study design: This was a retrospective case-control study using 8 years of data from a nationwide database of surgical outcomes in the United States.

Purpose: This study aimed to improve our understanding of the risk factors associated with a length of stay (LOS) >1 day and aid in reducing postoperative hospitalization and complications.

Overview of literature: Despite the proven safety of transforaminal lumbar interbody fusion (TLIF), some patients face prolonged postoperative hospitalization.

Methods: Data were collected from the American College of Surgeons National Surgical Quality Improvement Program dataset from 2011 to 2018. The cohort was divided into patients with LOS up to 1 day (LOS ≤1 day), defined as same day or next-morning discharge, and patients with LOS >1 day (LOS >1 day). Univariable and multivariable regression analyses were performed to evaluate predictors of LOS >1 day. Propensity-score matching was performed to compare pre- and postdischarge complication rates.

Results: A total of 12,664 eligible patients with TLIF were identified, of which 14.8% had LOS ≤1 day and 85.2% had LOS >1 day. LOS >1 day was positively associated with female sex, Hispanic ethnicity, diagnosis of spondylolisthesis, American Society of Anesthesiologists classification 3, and operation length of >150 minutes. Patients with LOS >1 day were more likely to undergo intraoperative/postoperative blood transfusion (0.3% vs. 4.5%, p<0.001) and reoperation (0.1% vs. 0.6%, p=0.004). No significant differences in the rates of postdischarge complications were found between the matched groups.

Conclusions: Patients with worsened preoperative status, preoperative diagnosis of spondylolisthesis, and prolonged operative time are more likely to require prolonged hospitalization and blood transfusions and undergo unplanned reoperation. To reduce the risk of prolonged hospitalization and associated complications, patients indicated for TLIF should be carefully selected.

Study design: Retrospective cohort study.

Purpose: To investigate the long-term clinical and radiological outcomes of selective fusion for rotatory olisthesis (RO) in degenerative lumbar scoliosis (DLS).

Overview of literature: DLS is often associated with RO, and selective fusion of RO is a common surgical treatment option. However, the clinical and radiological outcomes remain controversial.

Methods: A cohort of 54 consecutive patients with DLS and RO was included in the study. All the included patients underwent selective RO fusion and at least 2 years of follow-up. They were divided into two groups: group 1 with a curve <30° and group 2 with a curve ≥30°. The clinical outcomes were evaluated by the Oswestry Disability Index (ODI) and Numerical Rating Scale. The radiological assessment included RO location, offset and subluxated-disc orientation, Cobb angle, and coronal as well as sagittal alignments.

Results: The offset value was greater in group 2 than in group 1 (13.4±4.7 mm vs. 9.3±3.5 mm, p<0.001). The subluxated disc was mainly oriented to the concave side in group 2 (15/21) but to the convex side in group 1 (20/33) (p =0.022). Group 2 had a higher rate of postoperative adjacent RO than group 1 (14/21 vs. 1/33, p<0.001). The ODI was comparable between both groups preoperatively but higher at the final follow-up in group 2 (34.9±9.5) than in group 1 (24.4±6.2). In the multiple logistic regression analysis, the thoracolumbar/lumbar curve was identified as the risk factor for postoperative adjacent RO (odds ratio, 1.400; p=0.007). The receiver operating characteristic analysis verified it with an area under the curve of 0.960 (p<0.001).

Conclusions: The clinical and radiological outcomes were maintained well in group 1 but not in group 2. Selective RO fusion in DLS with a lumbar curve <30° is a rational option. However, it should be avoided in those with a lumbar curve >30° because of a higher complication rate and a worse clinical outcome at the final follow-up.

Study design: Feasibility study.

Purpose: A phantom model was used to evaluate the accuracy of a novel augmented reality (AR) system for cervical screw placement.

Overview of literature: The use of navigation systems is becoming increasingly common in spine procedures. However, numerous factors limit the feasibility of regular and widespread use of navigation tools during spine surgery. AR is a new technology that has already demonstrated utility as a navigation tool during spine surgery. However, advancements in AR technology are needed to increase its adoption by the medical community.

Methods: AR technology that uses a fiducial-less registration system was tested in a preclinical cervical spine phantom model study for accuracy during spinal screw placement. A three-dimensional reconstruction of the spine along with trajectory lines was superimposed onto the phantom model using an AR headset. Participants used the AR system to guide screw placement, and post-instrumentation scans were compared for accuracy assessment.

Results: Twelve cervical screws were placed under AR guidance. All screws were placed in an acceptable anatomic position. The average distance error for the insertion point was 2.73±0.55 mm, whereas that for the endpoint was 2.71±0.69 mm. The average trajectory angle error for all insertions was 2.69°±0.59°.

Conclusions: This feasibility study describes a novel registration approach that superimposes spinal anatomy and trajectories onto the surgeon's real-world view of the spine. These results demonstrate reasonable accuracy in the preclinical model. The results of this study demonstrate that this technology can assist with accurate screw placement. Further investigation using cadaveric and clinical models is warranted.

Study design: A retrospective study.

Purpose: To investigate the correlation between Hounsfield unit (HU) values measured by chest computed tomography (CT) and dual-energy Xray absorptiometry (DXA) T-scores. HU-based thoracolumbar (T11 and T12) cutoff thresholds were calculated for a cohort of Chinese patients.

Overview of literature: For patients with osteoporosis, the incidence of fractures in the thoracolumbar segment is significantly higher than that in other sites. However, most current clinical studies have focused on L1.

Methods: This retrospective study analyzed patients who underwent chest CT and DXA at our hospital between August 2021 and August 2022. Thoracic thoracolumbar segment HU values, lumbar T-scores, and hip T-scores were computed for comparison, and thoracic thoracolumbar segment HU thresholds suggestive of potential bone density abnormalities were established using receiver operating characteristic curves.

Results: In total, 470 patients (72.4% women; mean age, 65.5±12.3 years) were included in this study. DXA revealed that of the 470 patients, 90 (19%) had osteoporosis, 180 (38%) had reduced osteopenia, and 200 (43%) had normal bone mineral density (BMD). To differentiate osteoporosis from osteopenia, the HU threshold was established as 105.1 (sensitivity, 54.4%; specificity, 72.2%) for T11 and 85.7 (sensitivity, 69.4%; specificity, 61.1%) for T12. To differentiate between osteopenia and normal BMD, the HU threshold was 146.7 for T11 (sensitivity, 57.5%; specificity, 84.4%) and 135.7 for T12 (sensitivity, 59.5%; specificity, 80%).

Conclusions: This study supports the significance of HU values from chest CT for BMD assessment. Chest CT provides a new method for clinical opportunistic screening of osteoporosis. When the T11 HU is >146.7 or the T12 HU is >135.7, additional osteoporosis testing is not needed unless a vertebral fracture is detected. If the T11 HU is <105.1 or the T12 HU is <85.7, further DXA testing is strongly advised. In addition, vertebral HU values that fall faster than those of the T11 and L1 vertebrae may explain the high incidence of T12 vertebral fractures.

Study design: A retrospective multicenter case series was conducted.

Purpose: This study aimed to investigate survival and prognostic factors after surgery for a metastatic spinal tumor.

Overview of literature: Prognostic factors after spinal metastasis surgery remain controversial.

Methods: A retrospective multicenter study was conducted. The study participants included 345 patients who underwent surgery for spinal metastases from 2010 to 2020 at nine referral spine centers in Japan. Data for each patient were extracted from medical records. To identify the factors predicting survival prognosis after surgery, univariate analyses were performed using a Cox proportional hazards model.

Results: The mean age was 65.9 years. Common primary tumors were lung (n=72), prostate (n=61), and breast (n=39), and 67.8% (n=234) presented with osteolytic lesions. The epidural spinal cord compression scale score 2 or 3 was recognized in 79.0% (n=271). Frankel grade A paralysis accounted for 1.4% (n=5), and 73.3% (n=253) were categorized as intermediate or high risk according to the new Katagiri score. The overall survival rates were -71.0% at 6 months, 57.4% at 12, and 43.3% at 24. In the univariate analysis, Frankel grade A (hazard ratio [HR], 3.59; 95% confidence interval [CI], 1.23-10.50; p<0.05), intermediate risk (HR, 3.34; 95% CI, 2.10-5.32; p<0.01), and high risk (HR, 7.77; 95% CI, 4.72-12.8; p<0.01) in the new Katagiri score were significantly associated with poor survival. On the contrary, postoperative chemotherapy (HR, 0.23; 95% CI, 0.15-0.36; p<0.01), radiation therapy (HR, 0.43; 95% CI, 0.26-0.70; p<0.01), and both adjuvant therapy (HR, 0.21; 95% CI, 0.14-0.32; p<0.01) were suggested to improve survival.

Conclusions: Surgical indications for patients with Frankel grade A or intermediate or high risk in the new Katagiri score should be carefully considered because of poor survival. Chemotherapy or radiation therapy should be considered after surgery for better survival.

Study design: An experimental study.

Purpose: This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging.

Overview of literature: In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%-15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.

Methods: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME's graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.

Results: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model's accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.

Conclusions: The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.

Study design: A retrospective cohort study.

Purpose: This study aimed to assess the reliability of quantitative computed tomography (QCT) in measuring bone mineral density (BMD) of instrumented vertebrae and investigate the effect of less paraspinal muscle damage on BMD changes after lumbar interbody fusion.

Overview of literature: Patients always experience a decrease in vertebral BMD after lumbar interbody fusion. However, to the best of our knowledge, no study has analyzed the effect of paraspinal muscles on BMD changes.

Methods: This retrospective analysis included a total of 155 patients who underwent single-level lumbar fusion, with 81 patients in the traditional group and 74 patients in the Wiltse group (less paraspinal muscle damage). QCT was used to measure the volumetric BMD (vBMD), Hounsfield unit value, and cross-sectional area of the paraspinal muscles at the upper instrumented vertebrae (UIV), vertebrae one segment above the UIV (UIV+1), and the vertebrae one segment above the UIV+1 (UIV+2). Statistical analyses were performed.

Results: No significant differences in general data were observed between the two groups (p>0.05). Strong correlations were noted between the preoperative and 1-week postoperative vBMD of each segment (p<0.01), with no significant difference between the two time points in both groups (p>0.05). Vertebral BMD loss was significantly higher in UIV+1 and UIV+2 in the traditional group than in the Wiltse group (-13.6%±19.1% vs. -4.2%±16.5%, -10.8%±20.3% vs. -0.9%±37.0%; p<0.05). However, no statistically significant difference was observed in the percent vBMD changes in the UIV segment between the two groups (37.7%±70.1% vs. 36.1%±78.7%, p>0.05).

Conclusions: QCT can reliably determine BMD in the instrumented spine after lumbar interbody fusion. With QCT, we found that reducing paraspinal muscle destruction through the Wiltse approach during surgery can help preserve the adjacent vertebral BMD; however, it does not help increase the BMD in the instrumented vertebrae.

Study design: Retrospective study.

Purpose: To compare and correlate technetium-99m methylene diphosphonate uptake between benign and metastatic bone lesions using semiquantitative analysis of maximum standard uptake value (SUVmax) and mean Hounsfield unit (HU) in single-photon emission computed tomography-computed tomography (SPECT-CT).

Overview of literature: Qualitative interpretation of metastatic bone lesions in breast cancer on bone scintigraphy is often complicated by coexisting benign lesions.

Methods: In total, 185 lesions were identified on bone and SPECT-CT scans from 32 patients. Lesions were classified as metastatic (109 sclerotic lesions) and benign (76 lesions) morphologically on low-dose CT. Semiquantitative analysis using SUVmax and mean HU was performed on the lesions and compared. To discriminate benign and metastatic lesions, the correlation between SUVmax and mean HU was determined using the intraclass correlation coefficients.

Results: The SUVmax was higher in metastatic lesions (20.66±14.36) but lower in benign lesions (10.18±12.79) (p<0.001). The mean HU was lower in metastatic lesions (166.62±202.02) but higher in benign lesions (517.65±192.8) (p<0.001). A weak negative correlation was found between the SUVmax and the mean HU for benign lesions, and a weak positive correlation was noted between the SUVmax and the mean HU on malignant lesions with no statistical significance (p=0.394 and 0.312, respectively). The cutoff values obtained were 10.8 for SUVmax (82.6% sensitivity and 84.2% specificity) and 240.86 for the mean HU (98.7% sensitivity and 88.1% specificity) in differentiating benign from malignant bone lesions.

Conclusions: Semiquantitative assessment using SUVmax and HU can complement qualitative analysis. Metastatic lesions had higher SUVmax but lower mean HU than benign lesions, whereas benign lesions demonstrated higher mean HU but lower SUVmax. A weak correlation was found between the SUVmax and the mean HU on malignant and benign lesions. Cutoff values of 10.8 for the SUVmax and 240.86 for the mean HU may differentiate bone metastases from benign lesions.

Study design: A retrospective study.

Purpose: This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis.

Overview of literature: This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care.

Methods: A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC).

Results: A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery.

Conclusions: These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.