Robot-Guided Transforaminal Versus Robot-Guided Posterior Lumbar Interbody Fusion for Lumbar Degenerative Disease

Victor E. Staartjes,Bianca Battilana,Marc L. Schröder 대한척추신경외과학회 2021 Neurospine Vol.18 No.1

Objective: There have been no clinical studies comparing different robotic techniques. We compare minimally invasive, robot-guided transforaminal lumbar interbody fusion (RG-TLIF) and mini-open robot-guided posterior lumbar interbody fusion (RG-PLIF). Methods: Using data from a prospective institutional registry, we identified 38 patients who underwent RG-PLIF. Propensity score matching using a nearest-neighbor algorithm was implemented to select RG-TLIF controls. Twelve-month patient-reported outcome measures are presented. A reduction of ≥30% from baseline was defined as the minimum clinically important difference (MCID). Results: Among the 76 included patients, there was no difference between RG-TLIF and RG-PLIF in surgical time (132.3±29.4 minutes vs. 156.5±53.0 minutes, p=0.162), length of stay (55.9±20.0 hours vs. 57.2±18.8 hours, p=0.683), and radiation dose area product (310.6±126.1 mGy×cm2 vs. 287.9±90.3 mGy×cm2, p=0.370). However, while there was no difference among the 2 groups in terms of raw postoperative patient-reported outcome measures scores (all p>0.05), MCID in leg pain was greater for RG-PLIF (55.3% vs. 78.9%, p=0.028), and MCID in Oswestry Disability Index was greater for RG-TLIF (92.1% vs. 68.4%, p=0.009). There was no difference concerning back pain (81.6% vs. 68.4%, p=0.185). Conclusion: Our findings suggest that both RG-TLIF and RG-PLIF are viable and equally effective techniques in robotic spine surgery.

Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

Victor E. Staartjes,Martin N. Stienen 대한척추신경외과학회 2019 Neurospine Vol.16 No.4

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Five-Repetition Sit-to-Stand Test Performance in Healthy Individuals: Reference Values and Predictors From 2 Prospective Cohorts

Anita M. Klukowska,Victor E. Staartjes,W. Peter Vandertop,Marc L. Schröder 대한척추신경외과학회 2021 Neurospine Vol.18 No.4

Objective: The 5-repetition-sit-to-stand (5R-STS) test is an objective test of functional impairment- commonly used in various diseases, including lumbar degenerative disc diseases. It is used to measure the severity of disease and to monitor recovery. We aimed to evaluate reference values for the test, as well as factors predicting 5R-STS performance in healthy adults. Methods: Healthy adults (>18 years of age) were recruited, and their 5R-STS time was measured. Their age, sex, weight, height, body mass index (BMI), smoking status, education level, work situation and EuroQOL-5D Healthy & Anxiety category were recorded. Linear regression analysis was employed to identify predictors of 5R-STS performance. Results: We included 172 individuals with mean age of 39.4±14.1 years and mean BMI of 24.0±4.0 kg/m2. Females constituted 57%. Average 5R-STS time was 6.21±1.92 seconds, with an upper limit of normal of 12.39 seconds. In a multivariable model, age (regression coefficient [RC], 0.07; 95% confidence interval [CI], 0.05/0.09; p<0.001), male sex (RC, -0.87; 95% CI, -1.50 to -0.23; p=0.008), BMI (RC, 0.40; 95% CI, 0.10–0.71; p=0.010), height (RC, 0.13; 95% CI, 0.04–0.22; p=0.006), and houseworker status (RC, -1.62; 95% CI, -2.93 to -0.32; p=0.016) were significantly associated with 5R-STS time. Anxiety and depression did not influence performance significantly (RC, 0.82; 95% CI, -0.14 to 1.77; p=0.097). Conclusion: The presented reference values can be applied as normative data for 5R-STS in healthy adults, and are necessary to judge what constitutes abnormal performance. We identified several significant factors associated with 5R-STS performance that may be used to calculate individualized expected test times. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT03303300 and NCT03321357

Predicting Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery: A Step Towards True “Precision” Medicine?: Commentary on “Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal

Lara M. Höbner,Alexandra Grob,Victor E. Staartjes 대한척추신경외과학회 2023 Neurospine Vol.20 No.4

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Timing of Surgery in Tubular Microdiscectomy for Lumbar Disc Herniation and Its Effect on Functional Impairment Outcomes

Alessandro Siccoli,Marlies P. de Wispelaere,Marc L. Schröder,Victor E. Staartjes 대한척추신경외과학회 2020 Neurospine Vol.17 No.1

Objective: While it has been established that surgery for lumbar disc herniation, excluding emergent indications, should only be performed after weeks of conservative treatment, it has also been established that late surgery is associated with poorer outscomes in terms of leg pain. However, nothing is known concerning the timinig and functional outcome. We quantify the association of time to surgery (TTS) with functional impairment outcome and identify a maximum TTS cutoff. Methods: A consecutive series of patients who underwent tubular microdiscectomy for lumbar disc herniation was included. A reduction of ≥30% in the Oswestry Disability Index from baseline to 12 months was defined as the minimum clinically important difference (MCID). TTS was defined as time of symptom onset to surgery in weeks. The maximum TTS cutoffs were derived both quantitatively by an area under the curve (AUC) analysis, as well as qualitatively based on cutoff-specific MCID rates. Results: Inclusion was met by 372 patients, among which 327 (87.9%) achieved MCID. MCID achievement was associated with lower TTS (hazard ratio, 0.725; 95% confidence interval, 0.557–0.944; p=0.014). The optimum maximum TTS based on AUC was 21.5 weeks. The qualitative analysis showed a continuous drop of MCID rates with increasing TTS, with values >80% until week 14. Conclusion: Our findings suggest that longer TTS is associated with a poorer patient-reported outcome in terms of functional impairment, and that—depending on the calculation method and according to the literature—a maximum TTS of between 14 to 22 weeks should likely be aimed for.

TomoRay: Generating Synthetic Computed Tomography of the Spine From Biplanar Radiographs

Olivier Zanier,Sven Theiler,Raffaele Da Mutten,유승준,Luca Regli,Carlo Serra,Victor E. Staartjes 대한척추신경외과학회 2024 Neurospine Vol.21 No.1

Objective: Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively. Methods: A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS). Results: At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively. Conclusion: Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.

Whole Spine Segmentation Using Object Detection and Semantic Segmentation

Raffaele Da Mutten,Olivier Zanier,Sven Theiler,유승준,Luca Regli,Carlo Serra,Victor E. Staartjes 대한척추신경외과학회 2024 Neurospine Vol.21 No.1

Objective: Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis. Methods: Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets. Results: Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively. Conclusion: We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.