FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.

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.

Instrumentation of hypoplastic pedicles with patient-specific guides.

PURPOSE: Hypoplastic pedicles of the thoracolumbar spine (<5 mm diameter) are often found in syndromic deformities of the spine and pose a challenge in pedicle screw instrumentation. 3D-printed patient-specific guides might help overcome anatomical difficulties when instrumenting pedicles with screws, thereby reducing the necessity for less effective fixation methods such as hooks or sublaminar wires. In this study, the surgical feasibility and clinical outcome of patients with hypoplastic pedicles following pedicle screw instrumentation with 3D-printed patient-specific guides were assessed. METHODS: Hypoplastic pedicles were identified on preoperative computed tomography (CT) scans in six patients undergoing posterior spinal fusion surgery between 2017 and 2020. Based on these preoperative CT scans, patient-specific guides were produced to help with screw instrumentation of these thin pedicles. Postoperatively, pedicle-screw-related complications or revisions were analyzed. RESULTS: 93/105 (88.6%) pedicle screws placed with patient-specific guides were instrumented. 62/93 (66.7%) of these instrumented pedicles were defined as hypoplastic with a mean width of 3.07 mm (SD ±0.98 mm, 95% CI [2.82-3.32]). Overall, 6 complications in the 62 hypoplastic pedicles (9.7%) were observed and included intraoperatively managed 4 cerebrospinal fluid leaks, 1 pneumothorax and 1 delayed revision due to 2 lumbar screws (2/62, 3.3%) impinging the L3 nerve root causing a painful radiculopathy. The mean follow-up time was 26.7 (SD ±11.7) months. Complications were only noted when the pedicle-width-to-screw-diameter ratio measured less than 0.62. CONCLUSION: Patient-specific 3D-printed guides can aid in challenging instrumentation of hypoplastic pedicles in the thoracolumbar spine, especially if the pedicle-width-to-screw-diameter ratio is greater than 0.62.

Augmented Reality Navigated Sacral-Alar-Iliac Screw Insertion.

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 -10.8° ± 11.77° and MSL -65.29 ± 15 mm vs 55.04 ± 6.76 mm (P = .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.