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Our Initial Experience of First 50 Cases of Robotic-Arm-Assisted Total Knee Arthroplasty

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Journal of Clinical Orthopaedics | Vol 9 | Issue 2 |  July-December 2024 | page: 47-51 | Chandan Mehta, Mohan Madhav Desai, Swapnil Chitnavis, Kushagra Jain, Urvil Shah

DOI: https://doi.org/10.13107/jcorth.2024.v09i02.662

Open Access License: CC BY-NC 4.0

Copyright Statement: Copyright © 2024; The Author(s).

Submitted Date: 09 Aug 2024, Review Date: 26 Aug 2024, Accepted Date: 17 Sep 2024 & Published Date: 10 Dec 2024

Author: Chandan Mehta [1], Mohan Madhav Desai [1], Swapnil Chitnavis [1], Kushagra Jain [1], Urvil Shah [1]

[1] Department of Orthopaedics, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, India

Address of Correspondence

Dr. Chandan Mehta,

Department of Orthopaedics, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, India.
E-mail: drchandanmehta01@gmail.com

Abstract

Purpose: Robotic-arm-assisted total knee arthroplasty (RA-TKA) has been criticized for an increased operative time, longer incision, the extra incision for insertion of pins and various other potential complications. We want to describe our initial experience of the first 50 cases of RA-TKA (of fully automatic robot) regarding the learning curve for operative time, accuracy of implant positioning, and the accuracy of achieving a well-balanced knee through the assessment of gaps.
Materials and Methods: Retrospective analysis of the first 50 patients was done who underwent RA-TKA, all of which were performed by a senior surgeon experienced in conventional manual jig-based TKA. Operative time, accuracy of implant positing, restoration of limb alignment, and intraoperative gap balancing were assessed. Linear regression analysis and cumulative sum (CUSUM) sequential analysis were used to assess the learning curve for the operative time.
Results: In our experience, the learning curve for operative time in RA-TKA is around 25 cases as per CUSUM sequential analysis. The linear regression analysis showed a gradual decrease in the operative time as the number of RA-TKA performed cases increased (cases 1–10 = 76.8 ± 16 min, cases 11–20 = 72.5 ± 13 min, cases 21–30 = 63.6 ± 7 min, cases 31–40 = 61.3 ± 6 min, and cases 41–50 = 57.3 ± 10 min) – statically significant (P < 0.05) after 20 cases. There is no learning curve for the accuracy of achieving the planned implant position (P = n.s.) and limb alignment (P = n.s.). Only three cases were outliers, HKA angle <174° for varus phenotype, and HKA >183° for valgus phenotype. Forty-six cases (out of 50) had all the gaps within 3 mm of each other (sensitivity of the robot is <1 mm).
Conclusion: Implementation of RA-TKA into the surgical workflow is associated with a learning curve for the operative times, which eventually decreases but this does not lead to any compromise in the accuracy of implant positioning or overall limb alignment. The RA-TKA has shown improved accuracy in implant positioning, improved limb alignment, thereby reducing outliers, and improved gap balancing. All this translates to better clinical outcomes and patient satisfaction.
Keywords: Robotic arm assisted Total Knee Arthroplasty, Learning Curve, Operative time, Implant Positioning, Gap Balancing

References

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How to Cite this article: Mehta C, Desai MM, Chitnavis S, Jain K, Shah U. Our Initial Experience of First 50 Cases of Robotic-Arm Assisted Total Knee Arthroplasty. Journal of Clinical Orthopaedics July-December 2024;9(2):47-51.

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MIS-TLIF: Technical Note, Learning Goals behind Case Selection during Early Part of Learning Curve and Clinical Outcomes in First 150 Cases

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Journal of Clinical Orthopaedics | Vol 7 | Issue 1 |  Jan-Jun 2022 | page: 85-93 | Umesh Srikantha, Parichay Perikal, Akshay Hari, Yadhu Lokanath, Deepak Somasundaram, Nirmala Subramaniam, Ravi Gopal Varma

Author: Umesh Srikantha [1], Parichay Perikal [1], Akshay Hari [1], Yadhu Lokanath [1], Deepak Somasundaram [1], Nirmala Subramaniam [1], Ravi Gopal Varma [1]

[1] Department of Neurosurgery, Aster CMI Hospital, Bengaluru, Karnataka, India

[2] Department of Neurosurgery, Ramaiah Medical College and Hospitals, Bengaluru, Karnataka, India

 

Address of Correspondence
Dr. Umesh Srikantha,
Department of Neurosurgery, Aster CMI Hospital, Bengaluru, Karnataka, India.
E-mail: umeshsrikantha@gmail.com

Abstract

Introduction: Minimally Invasive Transforaminal Lumbar Interbody Fusion (MIS-TLIF) has been shown to offer several advantages over conventional (open) TLIF and is being increasingly employed by young surgeons early in their careers. It is important to know the appropriate technique and the correct cases to be selected in the early phase to achieve good outcomes during the learning curve. A detailed and illustrative technical note along with a guide for case selection at different phases of experience has been presented in this article.

Methods: The first consecutive single surgeon series of 150 MIS-TLIF cases done over 4 years between 2012 and 2015 were considered for analysis. Demographic and peri-operative data and previously documented follow-up were collected from case records. Telephonic questionnaire and consultation were done to collect latest status, any procedures/surgeries done elsewhere for issues related to index procedure. Results were stratified as Group 1 – first 25 cases; Group 2 – 26–75 cases; Group 3 – 76–150 cases.

Results: The major indication for surgery in group 1 was either Grade 1 spondylolisthesis or lumbar canal stenosis with concomitant axial symptoms. The incidence of relatively complex cases (Grade 2 or 3 listhesis; Revision cases; Multilevel cases) increased with each successive group. As expected, the operative time (calculated for only single-level cases) improved with time. The overall rate of peri-operative complications was higher in group 2 as compared to groups 1 and 3, predominantly due to an increased incidence of intra-operative dural tears in group 2. Symptomatic screw malposition was detected in five screws, all were managed conservatively. The median duration of follow-up for the entire group was 39 months (Range – 1–119 months). Eighty-two (55%) patients had follow-up of more than 1 year while 31 (20.6%) patients had follow-up of more than 7 years. Around 80–85% of patients at each point of follow-up assessment had a successful outcome (McNab 4 and 5). The re-operation rate for index level problems or adjacent segment was 2.6%, only one of which was done at the author’s center.

Conclusions: Minimally invasive TLIF is a safe and effective procedure with favorable long-term results and acceptable complication rates. Though technically challenging in initial phases, a good understanding of the technique and principles of minimally invasive spine surgery along with fulfilling helpful pre-requisites and appropriate case selection as mentioned in this article, will help to smoothen the learning curve and avoid unfavorable outcomes in early stages.

Keywords: Minimally Invasive, transforaminal lumbar interbody fusion, learning curve, long-term outcome, case selection, minimally invasive transforaminal lumbar interbody fusion

References

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How to Cite this article: Srikantha U, Perikal P, Hari A, Lokanath Y, Somasundaram D, Subramaniam N, Varma RG. MIS-TLIF: Technical Note, Learning Goals behind Case Selection during Early Part of Learning Curve and Clinical Outcomes in First 150 Cases. Journal of Clinical Orthopaedics Jan-Jun 2022;7(1):85-93.

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