pedicle

Robotic pedicle screw placement

Robotic pedicle screw placement

Robotic spinal fixation is associated with increased screw placement accuracy and similar operative blood loss, length of stay, and operative duration. These findings support the safety and cost-effectiveness of robotic spinal surgery across the spectrum of robotic systems and screw types 1).

In addition to demonstrating excellent pedicle screw accuracy, early studies have explored the impact of robot-assisted spine surgery on reducing radiation time, length of hospital stay, operative time, and perioperative complications in comparison to conventional freehand technique. The Mazor X Stealth Edition was introduced in 2018. This robotic system integrates Medtronic’s Stealth navigation technology into the Mazor X platform, which was introduced in 2016. It is unclear what the impact of these advancements have made on clinical outcomes.

In a multicenter study, both robot systems achieved excellent screw accuracy and low robot time per screw. However, using Stealth led to significantly less fluoroscopic radiation time, lower robot abandonment rates, and reduced blood transfusion rates than Mazor X. Other factors including length of stay, and 90-day complications were similar 2)

Ha Y. Robot-Assisted Spine Surgery: A Solution for Aging Spine Surgeons. Neurospine. 2018 Sep;15(3):187-188. doi: 10.14245/ns.18edi.003. Epub 2018 Sep 11. PubMed PMID: 30196675.

In three cadavers 12 pedicle screws were implanted in thoraco-lumbar segments with the robotic surgery assistant. 3D-fluoroscopy was performed for preoperative referencing, planning and identification of postoperative screw position. The radiation exposure of fluoroscopy and a CT scanner was compared, measuring the Computed Tomography Dose Index (CTDIw ).

Pedicle screw positioning was graded according to the Gertzbein-Robbins classification: Eleven of 12 pedicle screws showed optimal transpedicular position (Grade 1), one was positioned less than 2 mm outside (Grade 2). No major deviations were observed. Referencing with 3D-fluoroscopy resulted in a CTDIw reduction of 84% in the cervical- and 33% in the lumbar spine.

Robot-guided PS placement, using 3D-fluoroscopy for referencing, is a reliable tool for minimally invasive PS implantation; radiation exposure can be reduced 3).

Menger et al., investigated the cost effectiveness of adding robotic technology in spine surgery to an active neurosurgical practice.

The time of operative procedures, infection rates, revision rates, length of stay, and possible conversion of open to minimally invasive spine surgery (MIS) secondary to robotic image guidance technology were calculated using a combination of institution-specific and national data points. This cost matrix was subsequently applied to 1 year of elective clinical case volume at an academic practice with regard to payor mix, procedural mix, and procedural revenue.

A total of 1,985 elective cases were analyzed over a 1-year period; of these, 557 thoracolumbar cases (28%) were analyzed. Fifty-eight (10.4%) were MIS fusions. Independent review determined an additional ~10% cases (50) to be candidates for MIS fusion. Furthermore, 41.4% patients had governmental insurance, while 58.6% had commercial insurance. The weighted average diagnosis-related group reimbursement for thoracolumbar procedures for the hospital system was calculated to be $25,057 for Medicare and $42,096 for commercial insurance. Time savings averaged 3.4 minutes per 1-level MIS procedure with robotic technology, resulting in annual savings of $5,713. Improved pedicle screw accuracy secondary to robotic technology would have resulted in 9.47 revisions being avoided, with cost savings of $314,661. Under appropriate payor mix components, robotic technology would have converted 31 Medicare and 18 commercial patients from open to MIS. This would have resulted in 140 fewer total hospital admission days ($251,860) and avoided 2.3 infections ($36,312). Robotic surgery resulted in immediate conservative savings estimate of $608,546 during a 1-year period at an academic center performing 557 elective thoracolumbar instrumentation cases.

Application of robotic spine surgery is cost-effective, resulting in lesser revision surgery, lower infection rates, reduced length of stay, and shorter operative time. Further research is warranted, evaluating the financial impact of robotic spine surgery 4).

Several randomized controlled trials (RCTs) and cohort studies involving robotic-assisted (RA) and free-hand with fluoroscopy-guided (FH) and published before January 2017 were searched for using the Cochrane Library, Ovid, Web of Science, PubMed, and EMBASE databases. A total of 55 papers were selected. After the full-text assessment, 45 clinical trials were excluded. The final meta-analysis included 10 articles.

The accuracy of pedicle screw placement within the RA group was significantly greater than the accuracy within the FH group (odds ratio 95%, “perfect accuracy” confidence interval: 1.38-2.07, P < .01; odds ratio 95% “clinically acceptable” Confidence Interval: 1.17-2.08, P < .01).

There are significant differences in accuracy between RA surgery and FH surgery. It was demonstrated that the RA technique is superior to the conventional method in terms of the accuracy of pedicle screw placement 5).

In 2013 a study evaluated the outcomes of robotic-assisted screw placement in a consecutive series of 102 patients.

Data were recorded from technical notes and operative records created immediately following each surgery case, in which the robotic system was used to guide pedicle screw placement. All cases were performed at the same hospital by a single surgeon. The majority of patients had spinal deformity and/or previous spine surgery. Each planned screw placement was classified as: (1) successful/accurately placed screw using robotic guidance; (2) screw malpositioned using robot; (3) use of robot aborted and screw placed manually; (4) planned screw not placed as screw deemed non essential for construct stability. Data from each case were reviewed by two independent researchers to indentify the diagnosis, number of attempted robotic guided screw placements and the outcome of the attempted placement as well as complications or reasons for non-placement.

Robotic-guided screw placement was successfully used in 95 out of 102 patients. In those 95 patients, 949 screws (87.5 % of 1,085 planned screws) were successfully implanted. Eleven screws (1.0 %) placed using the robotic system were misplaced (all presumably due to “skiving” of the drill bit or trocar off the side of the facet). Robotic guidance was aborted and 110 screws (10.1 %) were manually placed, generally due to poor registration and/or technical trajectory issues. Fifteen screws (1.4 %) were not placed after intraoperative determination that the screw was not essential for construct stability. The robot was not used as planned in seven patients, one due to severe deformity, one due to very high body mass index, one due to extremely poor bone quality, one due to registration difficulty caused by previously placed loosened hardware, one due to difficulty with platform mounting and two due to device technical issues.

Of the 960 screws that were implanted using the robot, 949 (98.9 %) were successfully and accurately implanted and 11 (1.1 %) were malpositioned, despite the fact that the majority of patients had significant spinal deformities and/or previous spine surgeries. “Tool skiving” was thought to be the inciting issue with the misplaced screws. Intraoperative anteroposterior and oblique fluoroscopic imaging for registration is critical and was the limiting issue in four of the seven aborted cases 6).

Learning curve

Robotic pedicle screw placement learning curve.

References

1)

Himstead AS, Shahrestani S, Brown NJ, Produturi G, Shlobin NA, Al Jammal O, Choi EH, Ransom SC, Daniel Diaz-Aguilar L, Sahyouni R, Abraham M, Pham MH. Bony fixation in the era of spinal robotics: A systematic review and meta-analysis. J Clin Neurosci. 2022 Jan 19;97:62-74. doi: 10.1016/j.jocn.2022.01.005. Epub ahead of print. PMID: 35065405.
2)

Lee NJ, Zuckerman SL, Buchanan IA, Boddapati V, Mathew J, Leung E, Park PJ, Pham MH, Buchholz AL, Khan A, Pollina J, Mullin JP, Jazini E, Haines C, Schuler TC, Good CR, Lombardi JM, Lehman RA. Is There a Difference Between Navigated and Non-Navigated Robot Cohorts in Robot-Assisted Spine Surgery? A Multicenter, Propensity-Matched Analysis of 2,800 Screws and 372 Patients. Spine J. 2021 May 19:S1529-9430(21)00253-9. doi: 10.1016/j.spinee.2021.05.015. Epub ahead of print. PMID: 34022461.
3)

Spyrantis A, Cattani A, Seifert V, Freiman TM, Setzer M. Minimally invasive percutaneous robotic thoracolumbar pedicle screw implantation combined with three-dimensional-fluoroscopy can reduce radiation: a cadaver and phantom study. Int J Med Robot. 2019 Jun 19:e2022. doi: 10.1002/rcs.2022. [Epub ahead of print] PubMed PMID: 31216120.
4)

Menger RP, Savardekar AR, Farokhi F, Sin A. A Cost-Effectiveness Analysis of the Integration of Robotic Spine Technology in Spine Surgery. Neurospine. 2018 Aug 29. doi: 10.14245/ns.1836082.041. [Epub ahead of print] PubMed PMID: 30157583.
5)

Fan Y, Du JP, Liu JJ, Zhang JN, Qiao HH, Liu SC, Hao DJ. Accuracy of pedicle screw placement comparing robot-assisted technology and the free-hand with fluoroscopy-guided method in spine surgery: An updated meta-analysis. Medicine (Baltimore). 2018 Jun;97(22):e10970. doi: 10.1097/MD.0000000000010970. Review. PubMed PMID: 29851848; PubMed Central PMCID: PMC6392558.
6)

Hu X, Ohnmeiss DD, Lieberman IH. Robotic-assisted pedicle screw placement: lessons learned from the first 102 patients. Eur Spine J. 2013 Mar;22(3):661-6. doi: 10.1007/s00586-012-2499-1. Epub 2012 Sep 14. PubMed PMID: 22975723; PubMed Central PMCID: PMC3585630.

Robotic pedicle screw placement learning curve

Robotic pedicle screw placement learning curve

Siddiqui et al., described the learning curve of pedicle screw placement using Robot-Assisted Spine Surgery (RASS) of an experienced neurosurgeon and two supervised neurosurgical fellows.

The first 120 cases of RASS at the University of Texas Health Science Center at San Antoniowere assessed. Patient variables included age, body mass index (BMI), and indication for surgery. Intra-operative variables included the vertebral level of pedicle screw placement, number of screws placed by each operator, intraoperative blood loss, and operative time. Post-operative variables included Length of stay (LOS), discharge disposition, 30-day readmissions, wound complications, and hardware revisions. Screw accuracy was determined with image overlay analysis comparing planned screw trajectory on the navigation software to the intra-operative CT scan with final screw placement. 2-dimensional accuracy was determined for the tip of the screw, tail of the screw, and angle the screw was placed. The supervising physician and first fellow began utilizing the robot concurrently upon its arrival, while the second fellow began using the robot after the system had been in place for seven months.

Both experienced surgeon and first fellow displayed a learning curve and achieved statistically significant improvement of accuracy after 30 screws. The second fellow had significantly better accuracy than the experienced surgeon in his first 30 screws. There were no complications from hardware placement in either group. There were no returns to the operating room for hardware issues.

RASS is a safe, accurate method of pedicle screw instrumentation. This data shows similar learning adaptation rates for the first fellow and the experienced surgeon. Techniques learned by attending were immediately transferable to a new learner, who was able to achieve a faster learning curve than both first fellow and experienced surgeon 1).

A major peak in screw inaccuracies occurred between cases 10 and 20, and a second, smaller one at about 40 surgeries. One potential explanation could be a transition from decreased supervision (unskilled but aware) to increased confidence of a surgeon (unskilled but unaware) who adopts this new technique prior to mastering it (skilled). Schatloet al., therefore advocate ensuring competent supervision for new surgeons at least during the first 25 procedures of robotic spine surgery to optimise the accuracy of robot-assisted pedicle screws 2).

Between June 2010 and August 2012, the senior surgeon (IHL) performed 174 posterior spinal procedures using pedicle screws, 162 of which were attempted with robotic assistance. The use of the robotic system was aborted in 12 of the 162 procedures due to technical issues (registration failure, software crash, etc). The robotic system was successfully used in the remaining 150 procedures. These were the first procedures performed with the robot by the senior surgeon, and in this study, we divided the early learning curve into five groups: Group 1 (Patients 1-30), Group 2 (Patients 31-60), Group 3 (Patients 61-90), Group 4 (Patients 91-120), and Group 5 (Patients 121-150). One hundred twelve patients (75%) had spinal deformity and 80 patients (53%) had previous spine surgery. The accuracy of screw placement in the groups was assessed based on intraoperative biplanar fluoroscopy and postoperative radiographs. The results from these five groups were compared to determine the effect on the learning curve. The numbers of attempted pedicle screw placements were 359, 312, 349, 359, and 320 in Groups 1 to 5, respectively.

The rates of successfully placed screws using robotic guidance were 82%, 93%, 91%, 95%, and 93% in Groups 1 to 5. The rates of screws converted to manual placement were 17%, 7%, 8%, 4%, and 7%. Of the robotically placed screws, the screw malposition rates were 0.8%, 0.3%, 1.4%, 0.8%, and 0%.

The rate of successfully placed pedicle screws improved with increasing experience. The rate of the screws that were converted to manual placement decreased with increasing experience. The frequency of screw malposition was similar over the learning curve at 0% to 1.4%. Future studies will need to determine whether this finding is generalizable to others 3).

References

1)

Siddiqui MI, Wallace DJ, Salazar LM, Vardiman AB. Robot-assisted pedicle screw placement is safe and accurate in both experienced and two supervised, in-training surgeons. World Neurosurg. 2019 Jun 24. pii: S1878-8750(19)31659-6. doi: 10.1016/j.wneu.2019.06.107. [Epub ahead of print] PubMed PMID: 31247356.
2)

Schatlo B, Martinez R, Alaid A, von Eckardstein K, Akhavan-Sigari R, Hahn A, Stockhammer F, Rohde V. Unskilled unawareness and the learning curve in robotic spine surgery. Acta Neurochir (Wien). 2015 Oct;157(10):1819-23; discussion 1823. doi: 10.1007/s00701-015-2535-0. Epub 2015 Aug 19. PubMed PMID: 26287268.
3)

Hu X, Lieberman IH. What is the learning curve for robotic-assisted pedicle screw placement in spine surgery? Clin Orthop Relat Res. 2014 Jun;472(6):1839-44. doi: 10.1007/s11999-013-3291-1. PubMed PMID: 24048889; PubMed Central PMCID: PMC4016454.

Augmented reality for pedicle screw insertion

Augmented reality for pedicle screw insertion

Cadaveric studies have shown improved accuracy for pedicle screw placement in the thoracic spine using augmented reality with intraoperative 3D imaging, without the need for periprocedural x-ray. In this clinical study, Elmi-Terander et al., used the same system to place pedicle screws in the thoracic and lumbosacral spine of 20 patients.

The study was performed in a hybrid operating room with an integrated augmented realitysystem encompassing a surgical table, a motorized flat detector C-arm with intraoperative 2D/3D capabilities, integrated optical cameras for augmented reality navigation, and noninvasive patient motion tracking. Three independent reviewers assessed screw placement accuracy using the Gertzbein-Robbins classification on 3D scans obtained before wound closure. In addition, the navigation time per screw placement was measured.

One orthopedic spinal surgeon placed 253 lumbosacral and thoracic pedicle screws on 20 consenting patients scheduled for spinal fixation surgery. An overall accuracy of 94.1% of primarily thoracic pedicle screws was achieved. No screws were deemed severely misplaced (Gertzbein grade 3). Fifteen (5.9%) screws had 2 to 4 mm breach (Gertzbein grade 2), occurring in scoliosis patients only. Thirteen of those 15 screws were larger than the pedicle in which they were placed. Two medial breaches were observed and 13 were lateral. Thirteen of the grade 2 breaches were in the thoracic spine. The average screw placement time was 5.2 ± 4.1 minutes. During the study, no device-related adverse event occurred.

Augmented reality can be clinically used to place thoracic and lumbosacral pedicle screws with high accuracy and with acceptable navigation time. Consequently, the risk for revision surgery and complications could be minimized 1).

Molina et al., studied the use of an augmented reality head-mounted display (AR-HMD) in the placement of thoracolumbar pedicle screw spinal instrumentation in cadaver specimens. The AR-HMD system has the potential to reduce important limitations of conventional manual and robotic computer navigation for pedicle screw placement 2).

In 2017 Ma et al., presented a novel augmented reality (AR) surgical navigation system based on ultrasound-assisted registration for pedicle screw placement. This system provides the clinically desired targeting accuracy and reduces radiation exposure.

Ultrasound (US) is used to perform registration between preoperative computed tomography (CT) images and patient, and the registration is performed by least-squares fitting of these two three-dimensional (3D) point sets of anatomical landmarks taken from US and CT images. An integral videography overlay device is calibrated to accurately display naked-eye 3D images for surgical navigation. We use a 3.0-mm Kirschner wire (K-wire) instead of a pedicle screw in this study, and the K-wire is calibrated to obtain its orientation and tip location. Based on the above registration and calibration, naked-eye 3D images of the planning path and the spine are superimposed onto patient in situ using our AR navigation system. Simultaneously, a 3D image of the K-wire is overlaid accurately on the real one to guide the insertion procedure. The targeting accuracy is evaluated postoperatively by performing a CT scan.

An agar phantom experiment was performed. Eight K-wires were inserted successfully after US-assisted registration, and the mean targeting error and angle error were 3.35 mm and [Formula: see text], respectively. Furthermore, an additional sheep cadaver experiment was performed. Four K-wires were inserted successfully. The mean targeting error was 3.79 mm and the mean angle error was [Formula: see text], and US-assisted registration yielded better targeting results than skin markers-based registration (targeting errors: 2.41 vs. 5.18 mm, angle errors: [Formula: see text] vs. [Formula: see text].

Experimental outcomes demonstrated that the proposed navigation system has acceptable targeting accuracy. In particular, the proposed navigation method reduces repeated radiation exposure to the patient and surgeons. Therefore, it has promising prospects for clinical use 3).

References

1)

Elmi-Terander A, Burström G, Nachabe R, Skulason H, Pedersen K, Fagerlund M, Ståhl F, Charalampidis A, Söderman M, Holmin S, Babic D, Jenniskens I, Edström E, Gerdhem P. Pedicle Screw Placement Using Augmented Reality Surgical Navigation With Intraoperative 3D Imaging: A First In-Human Prospective Cohort Study. Spine (Phila Pa 1976). 2019 Apr 1;44(7):517-525. doi: 10.1097/BRS.0000000000002876. PubMed PMID: 30234816.
2)

Molina CA, Theodore N, Ahmed AK, Westbroek EM, Mirovsky Y, Harel R, Orru’ E, Khan M, Witham T, Sciubba DM. Augmented reality-assisted pedicle screw insertion: a cadaveric proof-of-concept study. J Neurosurg Spine. 2019 Mar 29:1-8. doi: 10.3171/2018.12.SPINE181142. [Epub ahead of print] PubMed PMID: 30925479.
3)

Ma L, Zhao Z, Chen F, Zhang B, Fu L, Liao H. Augmented reality surgical navigation with ultrasound-assisted registration for pedicle screw placement: a pilot study. Int J Comput Assist Radiol Surg. 2017 Dec;12(12):2205-2215. doi: 10.1007/s11548-017-1652-z. Epub 2017 Aug 5. PubMed PMID: 28779275.