Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Literature Search
2.3. Selection Strategy
- Experimental (randomized or non-randomized trial) and observation studies (study design).
- Studies including patients with femoral neck fracture (population).
- Patients receiving robot-assisted screw placement (intervention).
- Studies comparing a robotic approach to a freehand approach (comparison/control group).
- Studies reporting one of our outcomes of interest (listed in the next sub-section).
- Non-original research (i.e., reviews, editorials, perspectives, commentaries, etc.).
- Published protocols without results.
- Studies without a comparison group.
- Validation studies reporting the feasibility of implementing robotics in orthopedic surgery.
- Irrelevant populations (patients without femoral neck fracture or fracture at other sites).
- No clear description of the intervention or comparison groups.
- Duplicates studies.
- Studies with overlapping patients’ data.
2.4. Data Collection and Outcome Measures
2.5. Risk of Bias Assessment
2.6. Statistical Analysis
3. Results
3.1. Literature Search Results
3.2. Baseline Characteristics of Included Studies
3.3. Risk of Bias of Included Studies
3.4. Union Rate
3.5. Union Time (Days)
3.6. Harris Score
3.7. Operative Time (Mins)
3.8. Frequency of Fluoroscopy
3.9. Intraoperative Fluoroscopy Time (s)
3.10. Frequency of Guide Pin Insertion
3.11. Frequency of Intraoperative Drilling
3.12. Frequency of Guide Pin Adjustment
3.13. Intraoperative Blood Loss (mL)
3.14. Postoperative Hospitalization Time (Day)
3.15. Postoperative Complications
4. Discussion
4.1. Accuracy of Screw Placement
4.2. Surgical Time and Efficiency
4.3. Postoperative Complications and Recovery
4.4. Patient Outcomes and Satisfaction
4.5. Long-Term Functional Results
4.6. Determinants of Response Based on Meta-Regression
4.7. Surgeon’s Expertise
4.8. Cost Evaluation
4.9. Implications for Clinical Practice
4.10. Study Limitations
4.11. Recommendations for Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author (YOP) | Country | Robot | Design | FU (mo) | Age | Sample | Gender (M/F) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
RA | FH | RA | FH | Total | RA | FH | |||||
Cao (2017) [33] | China | Universal Robots | RCS | 14.7 | 44.7 | 47.9 | 20 | 36 | 56 | 10/10 | 19/17 |
Chen (2023) [17] | China | TiRobot | RCS | 31.4 | 43.6 (13.7) | 45.7 (12.7) | 32 | 36 | 68 | 18/14 | 17/19 |
Duan (2019) [12] | China | TiRobot | RCS | 13.6 | 61.7 (5.2) | 62.1 (4.1) | 26 | 23 | 49 | 11/15 | 9/14 |
He (2019) [13] | China | Bi-planar robot | RCS | 24 | 56 (11.1) | 56.2 (9.13) | 30 | 30 | 60 | 11/19 | 12/18 |
Huang (2017) [34] | China | Bi-planar robot | RCS | 19.6 | 59.4 (5.6) | 59.1 (4.9) | 32 | 32 | 64 | 10/22 | 12/20 |
Huang (2023) [44] | China | TiRobot Advance | RCS | 22.2 | 48.2 (11.9) | 48.5 (9.8) | 25 | 28 | 53 | 11/14 | 12/16 |
Jiang (2024) [18] | China | TiRobot | RCS | 15.6 | 53.61 (5.45) | 55.23 (4.64) | 16 | 20 | 36 | 7/9 | 8/12 |
Jing (2022) [35] | China | TiRobot | RCS | 7 | 55.2 | 55 | 31 | 43 | 74 | 11/20 | 14/29 |
Lei (2021) [36] | China | TiRobot | RCS | 6 | 51.86 (4.89) | 51.33 (4.3) | 21 | 21 | 42 | 12/9 | 14/7 |
Liao (2022) [37] | China | TiRobot | RCS | 8 | 44.1 (8.7) | 48.8 (8) | 14 | 14 | 28 | 6/8 | 7/7 |
Liu (2015) [38] | China | GD-2000 | RCS | 12.5 | 65.2 (4.2) | 60.5 (5.1) | 21 | 25 | 46 | 8/13 | 11/14 |
Liu (2023) [39] | China | TiRobot | RCS | 24 | 71.3 | 73.2 | 21 | 20 | 41 | 10/11 | 8/12 |
Liu (2024) [19] | China | TiRobot | RCS | 12 | 51.92 (6.41) | 50.08 (8.41) | 26 | 26 | 52 | 17-Sep | 11/15 |
Lou (2024) [20] | China | Tianji Robot | RCS | 13 | 46.2 (9.3) | 48.2 (7.8) | 28 | 32 | 60 | 12/16 | 15/17 |
Nie (2023) [21] | China | TiRobot | RCS | 14.9 | 56 (4.22) | 54.87 (4.81) | 18 | 23 | 41 | 8/10 | 10/13 |
Tong (2016) [14] | China | TiRobot | RCS | 18 | 47.5 | 51.5 | 20 | 18 | 38 | 12/8 | 11/7 |
Wan (2021) [40] | China | Tianji Robot | RCT | 6 | 51.86 (4.89) | 51.33 (4.3) | 21 | 21 | 42 | 12/9 | 14/7 |
Wang (2011) [15] | China | Bi-planar robot | RCS | - | - | - | 6 | 6 | 12 | - | - |
Wang (2019) [16] | China | TiRobot | RCS | 12 | 49.03 (8.23) | 49.8 (7.68) | 63 | 65 | 128 | 30/33 | 31/34 |
Wang (2023) [22] | China | TiRobot | RCS | 12 | 59.5 (8.7) | 60.1 (8.2) | 56 | 56 | 112 | 32/34 | 30/36 |
Wang (2024) [23] | China | TiRobot | RCS | 6 | 41.95 (5.39) | 40.8 (5.08) | 30 | 32 | 62 | - | - |
Yi (2022) [41] | China | TINAVI | RCT | 18 | 58.5 (6.3) | 57.5 (5.3) | 32 | 36 | 68 | 19/13 | 16/20 |
Zhao (2024) [43] | China | TiRobot | RCS | 12 | 53.87 (5.28) | 52.36 (5.05) | 37 | 35 | 72 | 16/21 | 13/22 |
Zhu (2021) [42] | China | TiRobot | RCS | 38.8 | 47.9 (13.5) | 47.7 (12.6) | 50 | 83 | 133 | 26/24 | 47/36 |
Coefficient | SE | Z | P | Low CI | High CI | |
---|---|---|---|---|---|---|
Union rate | ||||||
Universal Robots | −2.54082 | 2.304537 | −1.1 | 0.27 | −7.05764 | 1.975986 |
Tianji Robot | 0.93305 | 1.775284 | 0.53 | 0.599 | −2.54644 | 4.412544 |
Follow-up (month) | 0.043397 | 0.065153 | 0.67 | 0.505 | −0.0843 | 0.171095 |
Sample size | −0.00764 | 0.014116 | −0.54 | 0.588 | −0.03531 | 0.020026 |
Mean age (year) | −0.07405 | 0.089556 | −0.83 | 0.408 | −0.24958 | 0.101473 |
R2 = 0%; I2 = 0% | ||||||
Union Time (days) | ||||||
Bi-planar robot | −0.902 | 12.397 | −0.070 | 0.942 | −25.199 | 23.395 |
Universal Robots | 13.861 | 20.143 | 0.690 | 0.491 | −25.620 | 53.341 |
GD-2000 Robot | −15.649 | 25.399 | −0.620 | 0.538 | −65.430 | 34.132 |
Tianji Robot | 0.446 | 5.416 | 0.080 | 0.934 | −10.169 | 11.062 |
TiRobot Advance | −21.236 | 16.661 | −1.270 | 0.202 | −53.890 | 11.418 |
Follow-up (month) | 1.113 | 0.709 | 1.570 | 0.116 | −0.277 | 2.503 |
Sample size | −0.351 | 0.173 | −2.020 | 0.043 | −0.690 | −0.011 |
Mean age (year) | 0.027 | 0.577 | 0.050 | 0.963 | −1.105 | 1.159 |
R2 = 100%; I2 = 0% | ||||||
Harris Score | ||||||
Bi-planar robot | −2.884 | 2.129 | −1.350 | 0.175 | −7.056 | 1.288 |
Universal Robots | −3.031 | 2.850 | −1.060 | 0.288 | −8.617 | 2.556 |
GD-2000 | −1.587 | 3.096 | −0.510 | 0.608 | −7.654 | 4.481 |
Tianji Robot | 2.153 | 3.778 | 0.570 | 0.569 | −5.252 | 9.558 |
TiRobot Advance | 0.935 | 3.162 | 0.300 | 0.767 | −5.262 | 7.133 |
Follow-up (month) | −0.035 | 0.090 | −0.390 | 0.695 | −0.211 | 0.141 |
Sample size | 0.001 | 0.023 | 0.050 | 0.961 | −0.044 | 0.046 |
Mean age (year) | −0.054 | 0.103 | −0.520 | 0.601 | −0.255 | 0.148 |
R2 = 0%; I2 = 78.86% | ||||||
Operative Time (min) | ||||||
Bi-planar robot | −24.266 | 10.722 | −2.260 | 0.024 | −45.280 | −3.251 |
Universal Robots | 18.863 | 14.914 | 1.260 | 0.206 | −10.367 | 48.094 |
GD-2000 | −1.844 | 15.550 | −0.120 | 0.906 | −32.323 | 28.634 |
Tianji Robot | −8.777 | 14.779 | −0.590 | 0.553 | −37.743 | 20.188 |
TiRobot Advance | −21.372 | 14.532 | −1.470 | 0.141 | −49.856 | 7.111 |
Follow-up (month) | 0.698 | 0.458 | 1.530 | 0.127 | −0.199 | 1.595 |
Sample size | 0.167 | 0.139 | 1.200 | 0.230 | −0.106 | 0.440 |
Mean age (year) | 0.160 | 0.508 | 0.310 | 0.753 | −0.836 | 1.156 |
R2 = 33.43%; I2 = 97.13% | ||||||
Frequency of Fluroscopy | ||||||
Bi-planar robot | −20.472 | 3.989 | −5.130 | 0.000 | −28.290 | −12.655 |
Universal Robots | −32.172 | 3.941 | −8.160 | 0.000 | −39.896 | −24.447 |
GD-2000 | −4.283 | 4.806 | −0.890 | 0.373 | −13.702 | 5.137 |
Tianji Robot | 2.493 | 3.095 | 0.810 | 0.421 | −3.574 | 8.559 |
TiRobot Advance | −0.787 | 3.234 | −0.240 | 0.808 | −7.125 | 5.552 |
Follow-up (month) | 0.014 | 0.113 | 0.130 | 0.898 | −0.207 | 0.236 |
Sample size | 0.060 | 0.033 | 1.830 | 0.068 | −0.004 | 0.124 |
Mean age (year) | −0.450 | 0.175 | −2.570 | 0.010 | −0.792 | −0.107 |
R2 = 94.67%; I2 = 85.30% | ||||||
Frequency of Guide Pin Insertion | ||||||
Bi-planar robot | −3.126 | 3.980 | −0.790 | 0.432 | −10.927 | 4.675 |
Universal Robots | −7.283 | 3.637 | −2.000 | 0.045 | −14.411 | −0.155 |
GD-2000 | 4.888 | 4.797 | 1.020 | 0.308 | −4.515 | 14.291 |
TiRobot Advance | 0.913 | 3.127 | 0.290 | 0.770 | −5.216 | 7.041 |
Follow-up (month) | −0.202 | 0.180 | −1.120 | 0.262 | −0.556 | 0.151 |
Sample size | 0.013 | 0.039 | 0.340 | 0.738 | −0.063 | 0.089 |
Mean age (year) | −0.503 | 0.263 | −1.910 | 0.056 | −1.018 | 0.013 |
R2 = 44.45%; I2 = 92.81% | ||||||
Intraoperative Blood Loss (mL) | ||||||
Bi-planar robot | 21.287 | 14.230 | 1.500 | 0.135 | −6.604 | 49.179 |
Universal Robots | −37.713 | 14.017 | −2.690 | 0.007 | −65.186 | −10.241 |
GD-2000 | 18.924 | 15.664 | 1.210 | 0.227 | −11.777 | 49.625 |
Tianji Robot | 1.532 | 13.189 | 0.120 | 0.908 | −24.318 | 27.381 |
TiRobot Advance | −4.107 | 12.578 | −0.330 | 0.744 | −28.760 | 20.546 |
Follow-up (month) | −0.789 | 0.487 | −1.620 | 0.105 | −1.744 | 0.165 |
Sample size | −0.225 | 0.106 | −2.110 | 0.035 | −0.433 | −0.016 |
Mean age (year) | −2.980 | 0.826 | −3.610 | 0.000 | −4.598 | −1.362 |
R2 = 51.16%; I2 = 96.49% |
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Mert, Ü.; Mahmoud, M.A.; Ghandour, M.; Al Zuabi, A.; Speicher, M.; Kobbe, P.; Horst, K.; Hildebrand, F.; Kabir, K. Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis. J. Clin. Med. 2024, 13, 5072. https://doi.org/10.3390/jcm13175072
Mert Ü, Mahmoud MA, Ghandour M, Al Zuabi A, Speicher M, Kobbe P, Horst K, Hildebrand F, Kabir K. Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2024; 13(17):5072. https://doi.org/10.3390/jcm13175072
Chicago/Turabian StyleMert, Ümit, Mohamad Agha Mahmoud, Maher Ghandour, Ahmad Al Zuabi, Marco Speicher, Philipp Kobbe, Klemens Horst, Frank Hildebrand, and Koroush Kabir. 2024. "Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis" Journal of Clinical Medicine 13, no. 17: 5072. https://doi.org/10.3390/jcm13175072