Neurosurgical focus最新文献

{"title":"Does alignment alone predict mechanical complications after adult spinal deformity surgery? A machine learning comparison of alignment, bone quality, and soft tissue.","authors":"Sameer Sundrani, Derek J Doss, Graham W Johnson, Harsh Jain, Omar Zakieh, Adam M Wegner, Julian G Lugo-Pico, Amir M Abtahi, Byron F Stephens, Scott L Zuckerman","doi":"10.3171/2025.4.FOCUS25245","DOIUrl":"https://doi.org/10.3171/2025.4.FOCUS25245","url":null,"abstract":"<p><strong>Objective: </strong>Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors sought to determine which combination of inputs produced the most sensitive and specific machine learning model to predict mechanical complications using postoperative alignment, bone quality, and soft tissue data.</p><p><strong>Methods: </strong>A retrospective cohort study was performed in patients undergoing ASD surgery from 2009 to 2021. Inclusion criteria were a fusion ≥ 5 levels, sagittal/coronal deformity, and at least 2 years of follow-up. The primary exposure variables were 1) alignment, evaluated in both the sagittal and coronal planes using the L1-pelvic angle ± 3°, L4-S1 lordosis, sagittal vertical axis, pelvic tilt, and coronal vertical axis; 2) bone quality, evaluated by the T-score from a dual-energy x-ray absorptiometry scan; and 3) soft tissue, evaluated by the paraspinal muscle-to-vertebral body ratio and fatty infiltration. The primary outcome was mechanical complications. Alongside demographic data in each model, 7 machine learning models with all combinations of domains (alignment, bone quality, and soft tissue) were trained. The positive predictive value (PPV) was calculated for each model.</p><p><strong>Results: </strong>Of 231 patients (24% male) undergoing ASD surgery with a mean age of 64 ± 17 years, 147 (64%) developed at least one mechanical complication. The model with alignment alone performed poorly, with a PPV of 0.85. However, the model with alignment, bone quality, and soft tissue achieved a high PPV of 0.90, sensitivity of 0.67, and specificity of 0.84. Moreover, the model with alignment alone failed to predict 15 complications of 100, whereas the model with all three domains only failed to predict 10 of 100.</p><p><strong>Conclusions: </strong>These results support the notion that not every mechanical failure is explained by alignment alone. The authors found that a combination of alignment, bone quality, and soft tissue provided the most accurate prediction of mechanical complications after ASD surgery. While achieving optimal alignment is essential, additional data including bone and soft tissue are necessary to minimize mechanical complications.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"59 1","pages":"E15"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.","authors":"Mathijs de Boer, Jesse A M van Doormaal, Mare H Köllen, Lambertus W Bartels, Pierre A J T Robe, Tristan P C van Doormaal","doi":"10.3171/2025.4.FOCUS25163","DOIUrl":"https://doi.org/10.3171/2025.4.FOCUS25163","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.</p><p><strong>Methods: </strong>The authors used 125 preoperative CT and 137 contrast-enhanced T1-weighted MRI scans to generate 3D surface meshes of patients' skin and ventricular systems. Seven anatomical landmarks were manually annotated to train a neural network for automatic landmark localization. The model's accuracy was assessed by calculating the mean Euclidian distance of predicted landmarks to the ground truth. Kocher's point and EVD trajectories were automatically calculated with the foramen of Monro as the target. Performance was evaluated using Kakarla grades, as assessed by 3 clinicians. Interobserver agreement was measured with Pearson correlation, and scores were aggregated using majority voting. Ordinal linear regressions were used to assess whether modality or placement side had an effect on Kakarla grades. The impact of landmark localization error on the final EVD plan was also evaluated.</p><p><strong>Results: </strong>The automated landmark localization model achieved a mean error of 4.0 mm (SD 2.6 mm). Trajectory planning generated a trajectory for all patients, with a Kakarla grade of 1 in 92.9% of cases. Statistical analyses indicated a strong interobserver agreement and no significant differences between modalities (CT vs MRI) or EVD placement sides. The location of Kocher's point and the target point were significantly correlated to nasion landmark localization error, with median drifts of 9.38 mm (95% CI 1.94-19.16 mm) and 3.91 mm (95% CI 0.18-26.76 mm) for Kocher's point and the target point, respectively.</p><p><strong>Conclusions: </strong>The presented method was efficient and robust for landmark localization and accurate EVD trajectory planning. The short processing time thereby also provides a base for use in emergency settings.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"59 1","pages":"E14"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.","authors":"Thara Tunthanathip, Thakul Oearsakul, Chin Taweesomboonyat, Nuttha Sanghan, Rakkrit Duangsoithong","doi":"10.3171/2025.4.FOCUS24940","DOIUrl":"https://doi.org/10.3171/2025.4.FOCUS24940","url":null,"abstract":"<p><strong>Objective: </strong>Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Various anatomical structures must be identified during the operation, particularly the sella turcica and internal carotid artery (ICA) bilaterally. In the present retrospective cohort study, authors aimed to evaluate the performance of a deep learning (DL) model in detecting the sella turcica and ICA bilaterally in EETS video footage, with the goal of recognizing crucial landmarks and preventing potentially fatal injury.</p><p><strong>Methods: </strong>The authors collected images from the endoscopic video footage of 98 patients who had undergone EETS from January 2015 to June 2024. The ICAs and sella turcica were labeled by neurosurgeons, and the entire dataset was divided into training, validation, and test datasets at a ratio of 7:2:1. The model for ICA and sella turcica detection was trained using the YOLOv5s object detection architecture, and precision, recall, mean average precision (mAP)@0.5, and mAP@0.5:0.95 were reported during the validation process. Moreover, the confusion matrix and area under the receiver operating characteristic curve (AUC) were assessed from the model using unseen images from the test dataset.</p><p><strong>Results: </strong>The DL model had precision, recall, mAP@0.5, and mAP@0.5:0.95 of 0.942, 0.955, 0.969, and 0.617, respectively, for all objects in the training processes with validation. For testing the model with unseen images, the AUC was 0.97 (95% CI 0.95-0.98), whereas average precision was 0.99 (95% CI 0.99-1.00). For ICA detection with a multiclass approach, the AUCs were 0.98 (95% CI 0.97-0.99) for the absence of any ICA, 0.93 (95% CI 0.91-0.95) for 1 ICA in the images, and 0.95 (95% CI 0.93-0.96) for both ICAs in the image. Additionally, accuracy for the ICA and sella turcica was 0.958 and 0.965, respectively.</p><p><strong>Conclusions: </strong>Complex anatomical landmarks should be recognized during EETS. The computer vision model was effective in detecting the sella turcica and ICA bilaterally, as well as in identifying and avoiding fatal complications. For the model to generalize with reliability, it requires novel, unseen data from various settings to refine it and facilitate transfer learning.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"59 1","pages":"E11"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinematic analysis of lumbar pedicle screw placement using an artificial intelligence framework.","authors":"Christian J Quinones, Deepak Kumbhare, Matthew Palfreeman, Udaysinh Rathod, Devesh Sarda, Subhajit Chakrabarty, Bharat Guthikonda, Stanley Hoang","doi":"10.3171/2025.4.FOCUS25157","DOIUrl":"https://doi.org/10.3171/2025.4.FOCUS25157","url":null,"abstract":"<p><strong>Objective: </strong>Robotics and artificial intelligence (AI) are being increasingly integrated in spine surgery. One emerging application of AI is in hand motion detection to assess surgical skill. However, no standardized framework currently exists for evaluating trainee proficiency in spine surgery. This proof-of-concept study applied AI-based motion analysis and the machine learning (ML) pipeline to evaluate hand movements during lumbar pedicle screw placement, aiming to generate objective metrics for skill assessment.</p><p><strong>Methods: </strong>AI-based motion tracking was used to analyze hand movements during pedicle screw placement on a lumbar spine sawbone model. Video recordings of hand movements during freehand (FH) and robot-assisted (RB) pedicle screw placement were analyzed to extract metrics including distance, displacement, speed, velocity, acceleration, jerk, and normalized jerk index. Due to the limited number of participants, data augmentation techniques were used to generate synthetic data to expand the dataset. Extracted and derived kinematic metrics were then evaluated for their ability to predict training level and surgical technique.</p><p><strong>Results: </strong>In general, procedure time and movement distance appeared to decrease with increasing trainee experience, with more pronounced improvements in FH procedures. Kinematic analysis trended toward a reduction in speed, displacement, and jerk variability across training years. RB procedures were associated with reduced movement variability as extremes in velocity, acceleration, and jerk were limited. ML models were able to classify augmented data by training level and procedure type with acceptable accuracy.</p><p><strong>Conclusions: </strong>This proof-of-concept study presents a data processing pipeline capable of analyzing metrics to quantify surgical proficiency during spinal procedures. The methods described demonstrate the feasibility of using AI-driven video analysis to assess hand motion. It also highlights specific motion-based metrics that can distinguish between FH and RB techniques and correlate with surgical training level. These findings lay the groundwork for developing a standardized, objective framework for proficiency assessment in spine surgery.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"59 1","pages":"E9"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.","authors":"Shane Shahrestani, Catherine Garcia, Andrew M Miller, Robin Babadjouni, Andre E Boyke, Miguel Quintero-Consuegra, Rohin Singh, Alexander Tuchman, Corey T Walker","doi":"10.3171/2025.3.FOCUS2532","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS2532","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and neural network (NN), for perioperative outcomes in adult spinal deformity (ASD) surgery. By evaluating these models, the authors sought to explore how linear and nonlinear interactions unique to each outcome influence predictive accuracy, emphasizing the need for outcome-specific model selection.</p><p><strong>Methods: </strong>A retrospective cohort of 7430 patients (mean age of 67 years) who underwent multilevel thoracolumbar deformity correction was identified using the Nationwide Readmissions Database (2016-2019). Predictor variables included demographic data, frailty status, comorbidity indices, nutritional status, and hospital characteristics. Outcomes assessed were prolonged hospital length of stay (LOS), nonroutine discharge, top-quartile all-payer cost, 30-day readmission, and posthemorrhagic anemia. Models were trained on 75% of the dataset and tested on the remaining 25%. LR served as the baseline parametric model, while RF and GBM employed ensemble methods to handle nonlinear interactions, and NN used hidden layers optimized via backpropagation. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values, and DeLong's test was used for statistical comparisons.</p><p><strong>Results: </strong>RF achieved the highest AUC for LOS (0.713), while GBM excelled for posthemorrhagic anemia (AUC = 0.717). LR provided consistent moderate accuracy across all outcomes (AUC range 0.556-0.690). NN underperformed (AUC range 0.540-0.665), likely due to dataset size limitations. Significant differences were observed between models for prediction of LOS and posthemorrhagic anemia (p < 0.05), with RF and GBM performing the best as they capture nonlinear interactions effectively.</p><p><strong>Conclusions: </strong>The results highlight that no single algorithm universally outperforms others across all perioperative outcomes, as each model captures different linear and nonlinear heterogeneities. Careful consideration of the outcome's unique characteristics is essential when selecting a predictive model for ASD surgery. These findings support the integration of tailored machine learning approaches to optimize patient-specific risk stratification and perioperative care.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E12"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Removal of painful pelvic screws following spine fusion surgery: outcomes and complications.","authors":"Anthony L Mikula, Zach Pennington, Nikita Lakomkin, Michael L Martini, Abdelrahman M Hamouda, Ahmad Nassr, Brett Freedman, Arjun S Sebastian, William W Cross, Christopher P Ames, Benjamin D Elder, Jeremy L Fogelson","doi":"10.3171/2025.3.FOCUS2510","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS2510","url":null,"abstract":"<p><strong>Objective: </strong>The purpose of this study was to evaluate the risks and benefits of removing painful pelvic/iliac screws in spine fusion surgery patients.</p><p><strong>Methods: </strong>A retrospective review identified patients who had traditional iliac and S2-alar-iliac (S2AI) screws removed for pain. The minimum follow-up was 24 months.</p><p><strong>Results: </strong>Fifty-two patients (75% women) were included with a mean age of 63 years, BMI of 28, and follow-up of 65 months. Most of the removed screws were S2AI (83%) compared with traditional iliac screws (17%). Forty-three patients (83%) had improvement in their pelvic screw related-pain following removal. Eight patients (15%) experienced lumbosacral mechanical complications following pelvic screw removal including sacral fracture (n = 3, 6%) and/or L4-5 or L5-S1 rod fracture (n = 7, 13%). On multivariable analysis, risk factors for mechanical complications following pelvic screw removal included a longer fusion construct (OR 1.34, p = 0.035), greater postoperative L4-S1 lordosis (OR 1.14, p = 0.04, ideal cutoff > 40°), and lack of bone morphogenetic protein (BMP; OR 0.03, p = 0.02). Ten patients (19%) underwent subsequent SI joint fusion following pelvic screw removal, and higher standing pelvic incidence (OR 1.10, p = 0.03) was the only independent predictor of SI fusion.</p><p><strong>Conclusions: </strong>Removal of painful pelvic screws resulted in a high rate of postoperative pain relief, albeit with a risk of lumbosacral mechanical complications and subsequent SI joint fusion. Patients at risk for lumbosacral mechanical complications following pelvic screw removal included those with longer fusion constructs, more lordosis from L4 to S1 (> 40°), and lack of BMP. Patients at risk for receiving an instrumented SI joint fusion following pelvic screw removal included those with a higher pelvic incidence.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E15"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in short-term and delayed unplanned readmission in patients with adult spinal deformity undergoing posterior spinal fusion.","authors":"Aladine A Elsamadicy, Sumaiya Sayeed, Sina Sadeghzadeh, Paul Serrato, Shaila D Ghanekar, Sheng-Fu Larry Lo, Daniel M Sciubba","doi":"10.3171/2025.3.FOCUS2513","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS2513","url":null,"abstract":"<p><strong>Objective: </strong>Adult spinal deformity (ASD) affects many people in the US, often causing significant back pain and disability and disrupting activities of daily living. As a result, surgical intervention for deformity correction can help improve quality of life. Unplanned readmissions after surgery can significantly impact patients and the value of care. The aim of this study was to assess the trends in short-term and delayed unplanned readmissions following treatment for ASD.</p><p><strong>Methods: </strong>A retrospective cohort study was performed using the 2016-2019 Nationwide Readmissions Database. All adult patients undergoing thoracic/thoracolumbar posterior spinal fusion (PSF) for ASD were identified using International Classification of Diseases, 10th Revision coding. Patients were stratified into 7-day readmission (7-R), 30-day readmission (30-R), 90-day readmission (90-R), and no readmission (NonR) cohorts. Patient demographics, comorbidities, adverse events (AEs), and clinical outcomes were assessed.</p><p><strong>Results: </strong>Of the 3628 ASD patients identified, 550 (15.2%) experienced unplanned readmission (7-R: 131 [3.6%], 30-R: 252 [6.9%], 90-R: 167 [4.6%], NonR: 3078 [84.8%]). Patients in the readmission cohorts had higher rates of Medicare coverage, while the NonR cohort had the highest proportion of private insurance (p = 0.004). The 30-R cohort had the highest frailty score, followed by the 90-R, 7-R, and NonR cohorts (p < 0.001), respectively. The 7-R and 30-R cohorts had the highest prevalence of hypertension (p = 0.002), complicated diabetes (p = 0.002), and chronic pulmonary disease (p = 0.011). In addition, the 7-R and 30-R cohorts had a higher frequency of three or more comorbidities (p = 0.002) and two or more AEs (p < 0.001). On initial admission, the 7-R cohort had the longest mean length of stay (LOS) (p < 0.001), while the 30-R cohort had the greatest rate of nonroutine discharge (p < 0.001) and the highest mean cost of index admission (p = 0.039). On readmission, the 7-R cohort experienced the longest mean LOS (p < 0.001) and highest rate of nonroutine discharge (p = 0.025), with no significant differences in costs between cohorts.</p><p><strong>Conclusions: </strong>Our study suggests that increased comorbidities, AEs, LOS, nonroutine discharge, and hospital expenditures are associated with short-term (7 and 30 days) unplanned readmissions following PSF for ASD patients. Future studies should further investigate these observed trends and work to optimize patient care while minimizing unplanned readmissions and healthcare expenditures.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E16"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation and clinical application of the ΔC2 pelvic angle - ΔC2 tilt = Δpelvic tilt equation for predicting pelvic tilt in spinal deformity surgery.","authors":"Karan Joseph, Tim Bui, Alexander T Yahanda, Salim Yakdan, Samuel Vogl, Miguel Ruiz Cardozo, Jeffrey T Galla, Zachariah Leatherman, Noah D Poulin, Sundeep Chakladar, Samuel Brehm, Braeden Benedict, Munish Gupta, Nicholas Pallotta, Jeffrey Hills, Michael P Kelly, Jacob K Greenberg, Brian J Neuman, Wilson Z Ray, Camilo A Molina","doi":"10.3171/2025.3.FOCUS2554","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS2554","url":null,"abstract":"<p><strong>Objective: </strong>Notably, studies have established a consistent link between global sagittal alignment and pelvic tilt (PT) using C2 tilt (C2T) and C2 pelvic angle (C2PA), described by the following equation: C2PA = PT + C2T. The present study aimed to validate the proposed relationship (predicted ΔPT = ΔC2PA - ΔC2T) based on the assumption that patients aim to maintain a neutral C2T. Additionally, this study sought to evaluate the accuracy of intraoperative C2PA measurements for predicting postoperative PT.</p><p><strong>Methods: </strong>The medical records of patients > 21 years of age undergoing spinal fusion were retrospectively reviewed. Inclusion criteria were spinal instrumentation and fusion extending to the sacrum with upper instrumented vertebrae at or above L3. Patients without complete preoperative and 6-week postoperative anteroposterior and lateral scoliosis radiographs were excluded. Patients were stratified into short (fusion at or below T10) and long thoracolumbar fusions. Pre- and postoperative measurements included the C2-7 Cobb angle (CA), C6-T4 CA, T5-12 CA, T4 pelvic angle, L1 pelvic angle, pelvic incidence, and lumbar lordosis. A subset of patients with intraoperative radiographs visualizing a visible C2 vertebral body and bilateral femoral heads were analyzed for intraoperative prediction accuracy. ΔC2PA was defined as postoperative C2PA - preoperative C2PA, and predicted ΔC2T was defined as 0 - preoperative C2T. The mean absolute error (MAE) was calculated as the mean absolute difference between the predicted and actual postoperative PT values.</p><p><strong>Results: </strong>In total, 298 patients (mean age 65.4 ± 11.4 years, 71.8% female) met the inclusion criteria; 126 (42.3%) underwent short thoracolumbar fusions, and 172 (57.7%) underwent long thoracolumbar fusions. Preoperatively aligned patients had a mean postoperative C2T of -2.43° ± 2.48°, and preoperatively malaligned patients had a mean postoperative C2T of 0.72° ± 5.32°. The equation demonstrated excellent accuracy in the full cohort, with an MAE of 3.56° and an R2 value of 0.77. Of the total cohort, 69 patients (23.2%) met criteria for intraoperative measurements. Intraoperatively, the equation retained clinical utility (MAE = 5.75°, R2 = 0.576) and maintained high accuracy across stratified analyses by fusion length (MAE in long fusion = 5.89°, R2 = 0.595; MAE in short fusion = 5.31°, R2 = 0.603).</p><p><strong>Conclusions: </strong>This study validates the equation (predicted ΔPT = ΔC2PA - ΔC2T) as a reliable tool for predicting PT in spinal deformity surgery. The equation's dual functionality as a preoperative planning tool and intraoperative predictive guide underscores its clinical utility.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E6"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial. Tilted foundations, leaning towers: the L4 factor in adult spinal deformity.","authors":"Daniel Schneider, Daniel M Sciubba","doi":"10.3171/2025.3.FOCUS25295","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS25295","url":null,"abstract":"","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E5"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pain catastrophizing and frailty in adult spinal deformity patients with cognitive impairment.","authors":"Alexa Semonche, Justin K Scheer, Austin Lui, John F Burke, Chloe Jedwood, Albert Wang, Elaina J Wang, Tony Catalan, Diana Chang, Bethany Belfield, Isabelle Thapar, Michael M Safaee, Darryl Lau, Marissa Fury, Thomas Wozny, Anthony L Mikula, David Mazur-Hart, Alekos A Theologis, Aaron J Clark, Christopher P Ames","doi":"10.3171/2025.3.FOCUS2538","DOIUrl":"https://doi.org/10.3171/2025.3.FOCUS2538","url":null,"abstract":"<p><strong>Objective: </strong>Cognitive impairment and pain catastrophizing are both associated with worse surgical outcomes. The aim of this study was to define the prevalence of cognitive impairment in patients with adult spinal deformity (ASD) and the relationships between cognitive impairment, pain catastrophizing, patient-reported outcome measures (PROMs), and frailty in the preoperative setting.</p><p><strong>Methods: </strong>This cross-sectional study included patients undergoing evaluation for ASD correction at a single tertiary care center from January 2017 to October 2024. Patients were administered the Montreal Cognitive Assessment (MoCA), Pain Catastrophizing Scale (PCS), Scoliosis Research Society 22-item revised (SRS-22r) questionnaire, the Oswestry Disability Index (ODI), and the Edmonton Frail Scale (EFS). Median survey responses were compared between patients with any cognitive impairment (MoCA score < 26) and no cognitive impairment (MoCA score ≥ 26) using the Mann-Whitney U-test. Associations between survey responses were tested using Spearman's rank correlation analysis. Multivariate logistic regression analysis was performed to identify predictors of severe pain catastrophizing (PCS score ≥ 30).</p><p><strong>Results: </strong>A total of 210 patients (61.4% female, median age 66.5 years) were included in the study. Of these, 123 (58.6%) had normal cognition and 87 (41.4%) had mild or moderate cognitive impairment. Patients with cognitive impairment had greater median PCS scores compared with patients with normal cognition (total PCS score 25.0 vs 19.0, p = 0.01). Lower MoCA scores were significantly correlated with higher PCS (ρ = -0.23, p = 0.0007) and EFS (ρ = -0.21, p = 0.0074) scores, but not ODI and total SRS-22r scores. In the multivariate logistic regression analysis, lower MoCA and SRS-22r scores were associated with greater odds of having severe pain catastrophizing (MoCA: OR 0.82 [95% CI 0.68-0.98], p = 0.03; SRS-22r: OR 0.05 [95% CI 0.01-0.19], p < 0.0001), while ODI score, EFS score, age, and sex were not associated.</p><p><strong>Conclusions: </strong>There was a high prevalence (41.4%) of cognitive impairment in patients with ASD. In both the correlation and multivariate logistic regression analyses, cognitive impairment was associated with pain catastrophizing and thus might contribute to pain perception and frailty in a way that is not consistently captured by traditional PROMs.</p>","PeriodicalId":19187,"journal":{"name":"Neurosurgical focus","volume":"58 6","pages":"E2"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}