Background: Sacral-alar-iliac (SAI) screws are increasingly used for lumbo-pelvic fixation procedures. Insertion of SAI screws is technically challenging, and surgeons often rely on costly and time-consuming navigation systems. We investigated the accuracy and precision of an augmented reality (AR)-based and commercially available head-mounted device requiring minimal infrastructure.Methods: A pelvic sawbone model served to drill pilot holes of 80 SAI screw trajectories by 2 surgeons, randomly either freehand (FH) without any kind of navigation or with AR navigation. The number of primary pilot hole perforations, simulated screw perforation, minimal axis/outer cortical wall distance, true sagittal cranio-caudal inclination angle (tSCCIA), true axial medio-lateral angle, and maximal screw length (MSL) were measured and compared to predefined optimal values.Results: In total, 1/40 (2.5%) of AR-navigated screw hole trajectories showed a perforation before passing the inferior gluteal line compared to 24/40 (60%) of FH screw hole trajectories (P , .05). The differences between FH-and AR-guided holes compared to optimal values were significant for tSCCIA with À10.88 6 11.778 and MSL À65.29 6 15 mm vs 55.04 6 6.76 mm (P ¼ .001).Conclusions: In this study, the additional anatomical information provided by the AR headset and the superimposed operative plan improved the precision of drilling pilot holes for SAI screws in a laboratory setting compared to the conventional FH technique. Further technical development and validation studies are currently being performed to investigate potential clinical benefits of the AR-based navigation approach described here.Level of Evidence: 4.
Background Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. Methods Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. Results Models were developed and integrated into a web-app (https://neurosurgery.shinyapps.io/fuseml/) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59–0.74], back pain (0.72, 95%CI: 0.64–0.79), and leg pain (0.64, 95%CI: 0.54–0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. Conclusions Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk–benefit estimation, truly impacting clinical practice in the era of “personalized medicine” necessitates more robust tools in this patient population.
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