Introduction
Methods
Search strategy
Outcomes of interest
Eligibility criteria
Data extraction and items
Qualitative assessment (risk of bias)
Statistical analysis
Results
Study selection
Quality assessment [risk of bias and level of evidence (LoE)]
Study | Design, LoE | Country | Population | No. of patients | Graft type (strands) | Graft source |
---|---|---|---|---|---|---|
2007 Brown [6] | Cohort, II | USA | Adult | 414 | BTB | Allograft |
2007 Tuman [7] | Cohort, II | USA | Adult | 106 | HT | Autograft |
2008 Treme [5] | Cohort, II | USA | Adult | 50 | HT | Autograft |
2012 Chan [30] | Series, IV | USA | Adult | 20 | HT | Autograft |
2012 Reboonlap [31] | Cross-sectional, III | Thailand | Adult | 74 | HT | Autograft |
2012 Stergios [32] | Retrospective, III | Greece | Adult | 61 | HT | Autograft |
2012 Xie [33] | Cohort, II | China | Adult | 235 | HT | Autograft |
2013 Celiktas [34] | Cohort, II | Turkey | Adult | 164 | HT | Autograft |
2013 Challa [4] | Cohort, II | India | Adult | 41 | HT | Autograft |
2013 Park [35] | Series, IV | South Korea | Adult | 296 | HT | Autograft |
2013 Thomas [9] | Cohort, II | UK | Adult | 121 | HT | Autograft |
2014 Schwartzberg [10] | Cohort, II | USA | Adult | 100 | HT | Autograft |
2015 Nuelle [36] | Series, IV | USA | Adult | 60 | HT | Autograft |
2016 Asif [37] | Retrospective, III | India | Adult | 46 | HT | Autograft |
2016 Atbasi [38] | Retrospective, III | Turkey | Adult | 126 | HT | Autograft |
2016 Goyal [39] | Cohort, II | India | Adult | 160 | HT | Autograft |
2016 Ho [40] | Series, IV | Singapore | Adult | 169 | HT | Autograft |
2016 Kivi [41] | Cross-sectional, III | Iran | Adult | 178 | HT | Autograft |
2016 Pereira [11] | Retrospective, III | Brazil | Adult | 64 | HT | Autograft |
2016 Sundararajan [42] | Cohort, II | India | Adult | 108 | HT | Autograft |
2017 Chiba [43] | Cross-sectional, III | Japan | Adult | 200 | HT | Autograft |
2017 Gupta [44] | Cohort, II | India | Adult | 123 | HT | Autograft |
2017 Leiter [12] | Retrospective, III | Canada | Adult | 109 | HT | Autograft |
2017 Vincent V.G. An [45] | Retrospective, III | Australia | NR | 108 | HT | |
2018 Ramkumar [46] | Cross-sectional, III | USA | Adult | 1681 | HT | Autograft |
2018 Song [47] | Retrospective, III | China | Adult | 156 | PLT | Autograft |
2019 Heijboer [48] | Cohort, II | Netherlands | Adult | 53 | HT | Autograft |
2019 Moghamis [8] | Mixed, III | Qatar | Adult | 50 | HT | Autograft |
2019 Sakti [49] | Cohort, II | Indonesia | Adult | 60 | HT | Autograft |
2020 Du-Hyun Ro [50] | Retrospective, III | Korea | Adult | 54 | HT | Autograft and allograft |
2020 Goyal [51] | Cohort, II | India | Adult | 95 | QUAD | Autograft |
2020 Jagadeesh [52] | Cohort, II | India | Adult | 128 | HT | Autograft |
2020 Sakti [53] | Cohort, II | Indonesia | Adult | 20 | PLT | Autograft |
2020 Thwin [54] | Cohort, II | Singapore | Adult | 141 | HT | Autograft |
2021 Ertilav [55] | Retrospective, III | Turkey | Adult | 53 | PLT | Autograft |
2021 Khan [56] | Retrospective, III | India | Adult | 52 | PLT | Autograft |
2021 Kumar [57] | Retrospective, III | India | Adult | 73 | HT | Autograft |
2021 Singhal [58] | Cohort, II | India | Adult | 280 | HT | Autograft |
2022 Harshith [59] | Cohort, II | India | Adult | 35 | HT | Autograft |
2022 Huang [60] | Cohort, II | China | Adult | 24 | HT | Autograft |
2022 Mishra [61] | Series, IV | India | NR | 256 | HT | Autograft |
2023 Movahedinia [62] | Cohort, II | Iran | Adult | 42 | HT | Autograft |
Study | Age (Y) | Height (cm) | Weight (Kg) | BMI (kg/m2) | Gender (M/F) | Thigh length (cm) | Thigh circumference (cm) | Sports/Activity level |
---|---|---|---|---|---|---|---|---|
2007 Brown [6] | 45.8 ± 17.4 | 172 ± 11.4 | 74 ± 15.4 | NR | 1.10 | NR | NR | NR |
2007 Tuman [7] | 32.9 ± 14.1 | 172.4 ± 9.4 | 75.4 ± 14.9 | 25.4 ± 4.8 | 0.92 | NR | NR | NR |
2008 Treme [5] | 31.6 ± 13.6 | 170.9 ± 10.5 | 78 ± 18.4 | 28.4 ± 4.7 | 1.37 | 51.8 ± 4.9 | 47.0 ± 4.9 | Tegner score 6.4 ± 2.0 |
2012 Chan [30] | 28.14 | 172.1 | 75.0 | 24.54 | 1.50 | NR | NR | NR |
2012 Reboonlap [31] | 29.2 ± 9.0 | 171.9 ± 6.9 | 71.2 ± 10.4 | 24.0 ± 2.8 | 0.00 | 52.7 ± 3.8 | 47.4 ± 3.8 | NR |
2012 Stergios [32] | 27.0 ± 7.7 | 176.2 ± 8.3 | 77.8 ± 14.1 | 24.9 ± 3.5 | 2.81 | NR | NR | NR |
2012 Xie [33] | 28.1 ± 10 | 171.9 ± 7.9 | 71.0 ± 13.7 | 23.9 ± 3.5 | 2.45 | NR | NR | Tegner score 6.15 ± 0.8 |
2013 Celiktas [34] | 29.23 | 179.2 ± 5.3 | 82.5 ± 8.8 | 25.7 ± 2.3 | 0.00 | NR | 51.0 ± 4.7 | NR |
2013 Challa [4] | 27.9 ± 8.9 | 170.8 ± 5.3 | 66.5 ± 7.1 | 22.7 ± 2.8 | 4.85 | NR | NR | NR |
2013 Park [35] | 29.8 ± 10.7 | 171.3 ± 7.6 | 72.1 ± 12.2 | 24.5 ± 3.3 | 3.84 | NR | NR | 11% Athletes |
2013 Thomas [9] | 31.9 | 177 | 84.90 | 26.90 | 8.31 | NR | NR | NR |
2014 Schwartzberg [10] | NR | NR | NR | NR | NR | NR | NR | NR |
2015 Nuelle [36] | 25.3 ± 8.9 | 176.4 ± 10.6 | 79.4 ± 16.7 | 25.3 ± 3.9 | 1.5 | NR | NR | All
athletes |
2016 Asif [37] | 29.4 ± 10.2 | 172.6 ± 4.6 | 70.9 ± 11.5 | 23.8 ± 3.7 | 22.00 | NR | 47.1 ± 5.0 | NR |
2016 Atbasi [38] | 24.2 ± 4.6 | 176.3 ± 5.4 | 77.9 ± 8.1 | 25.1 ± 2.3 | 0.00 | NR | NR | NR |
2016 Goyal [39] | NR | 169.1 ± 6.9 | 69.2 ± 11.7 | 24.1 ± 3.5 | NR | 51.5 ± 3.5 | NR | NR |
2016 Ho [40] | 25.5 | 171.3 | 73.54 | 25.25 | 5.03 | NR | NR | NR |
2016 Kivi [41] | 29.8 ± 9.9 | 174.8 ± 7.8 | 76.4 ± 12.7 | 24.9 ± 3.5 | 1.96 | NR | NR | NR |
2016 Pereira [11] | 31.8 ± 8.2 | 177 ± 8.0 | 82.4 ± 12.9 | 26.1 ± 3.7 | 15.00 | NR | NR | NR |
2016 Sundararajan [42] | 33.0 ± 9.5 | 167.7 ± 9.9 | 72.4 ± 12.4 | 25.7 ± 3.6 | 4.40 | 51.5 ± 4.1 | NR | NR |
2017 Chiba [43] | 25.6 ± 13 | 165.6 ± 8 | 63.5 ± 11.9 | 23.1 ± 3.5 | 0.77 | NR | NR | Tegner score 6.4 ± 1.9 |
2017 Gupta [44] | 28.4 ± 8.8 | 173.3 ± 7.3 | 75.0 ± 11.3 | NR | 7.20 | 49.4 ± 3.6 | 48.2 ± 3.8 | NR |
2017 Leiter [12] | 27.8 ± 11.4 | 173.0 ± 12.0 | 80.6 ± 19.6 | 26.9 ± 5.7 | 1.82 | NR | NR | NR |
2017 Vincent V.G. An [45] | 30.7 ± 13.9 | 172.9 ± 9.6 | NR | NR | 1.47 | NR | NR | NR |
2018 Ramkumar [46] | 28.7 ± 11.8 | 172.7 ± 10.0 | 80.1 ± 18.6 | 26.8 ± 5.1 | 1.45 | NR | NR | NR |
2018 Song [47] | 29.5 ± 8.1 | 174.1 ± 8.6 | 76.2 ± 13.2 | 25.0 ± 3.4 | 1.44 | NR | NR | NR |
2019 Heijboer [48] | 25 | 178.0 ± 8.9 | 78.2 ± 14.0 | NR | 3.10 | NR | 46 ± 3.8 | Tegner score 9(7.3–9) |
2019 Moghamis [8] | 29 ± 7 | 174.0 ± 8.0 | 82.2 ± 11.2 | 27.0 ± 3.5 | 0.00 | 46.6 ± 2.7 | 50.7 ± 3.8 | NR |
2019 Sakti [49] | 27.2 ± 7.5 | 167.7 ± 7.1 | 71.9 ± 15.7 | 25.4 ± 4.7 | 5.66 | 38.8 ± 3.8 | 45.8 ± 6.9 | NR |
2020 Du-Hyun Ro [50] | 28.2 ± 9.2 | 169.8 | 66.8 | 23.57 | 1.45 | NR | NR | NR |
2020 Goyal [51] | 30.2 ± 8.7 | 168.1 ± 7.3 | 72.2 ± 11.2 | 25.6 ± 3.7 | NR | 46.9 ± 4.1 | 47.5 ± 5.9 | Tegner score 4 |
2020 Jagadeesh [52] | 30.8 ± 10.1 | 167.4 ± 6.3 | 66.5 ± 7.9 | 23.7 ± 2.6 | 0.00 | 50.0 ± 2.4 | NR | NR |
2020 Sakti [53] | 29.8 | 168.1 ± 8.2 | 71.2 ± 13.1 | 25.0 ± 3.1 | 5.66 | NR | NR | NR |
2020 Thwin [54] | 24.77 | 171.1 | 72.78 | 24.69 | 4.42 | NR | NR | NR |
2021 Ertilav [55] | 29.2 ± 7.7 | 170.0 ± 10.0 | 76.0 ± 12.6 | 25.9 ± 2.6 | 2.00 | NR | NR | NR |
2021 Khan [56] | 28.2 ± 7.4 | 172.7 ± 2.8 | 75.6 ± 3.4 | 25.3 ± 0.9 | 7.66 | NR | NR | NR |
2021 Kumar [57] | 33.7 ± 11.2 | 173.1 ± 5.3 | 71.2 ± 13.1 | 23.7 ± 3.9 | 0.00 | NR | 50.4 ± 6.8 | NR |
2021 Singhal[58] | 28.6 ± 8.7 | 1.69 ± 0.1 | 75.2 ± 14.2 | 26.3 ± 4.6 | 4.18 | NR | NR | NR |
2022 Harshith [59] | 33.2 ± 6.9 | 166.4 ± 9.6 | 70.1 ± 9.4 | 25.1 ± 4.5 | 6.00 | 49.3 ± 4.6 | 44.2 ± 5.0 | NR |
2022 Huang [60] | 33.7 ± 8.4 | NR | NR | NR | 1.18 | NR | NR | NR |
2022 Mishra [61] | NR | NR | NR | NR | NR | NR | NR | NR |
2023 Movahedinia [62] | 32.8 ± 5.1 | 173.8 ± 5.6 | 77.1 ± 7.3 | 25.4 ± 2.0 | 3.2 | NR | NR | NR |
Pooled study characteristics
Correlations between graft diameter and anthropometric measures
Subgroup analysis per graft type and region
Outcome | No. studies | No. patients | Correlation (r) | 95% CI | Heterogenity I2 (%) | p-value |
---|---|---|---|---|---|---|
Age (Fig. 2) | 26 | 4322 | 0.016 | − 0.03–0.06 | 32.1 | 0.461 |
Gender (Fig. 3) | 10 | 2791 | − 0.173 | − 0.36–− 0.03 | 94.2 | 0.096 |
Height (Fig. 4) | 42 | 6385 | 0.494 | 0.41–0.56 | 94.0 | < 0.001 |
Weight (Fig. 5) | 38 | 4128 | 0.383 | 0.31–0.44 | 76.3 | < 0.001 |
BMI (Fig. 6) | 33 | 5084 | 0.168 | 0.11–0.23 | 68.8 | < 0.001 |
Thigh length (Fig. 7) | 9 | 833 | 0.351 | 0.18–0.50 | 86.1 | < 0.001 |
Thigh circumference (Fig. 8) | 11 | 752 | 0.403 | 0.19–0.58 | 89.1 | < 0.001 |
Outcome | No. studies | No. patients | Correlation (r) | Heterogeneity I2 (%) |
---|---|---|---|---|
HT | ||||
Age | 22 | 4042 | 0.14 (− 0.03–0.06) | 31.9 |
Height | 36 | 5596 | 0.45 (0.36–0.53) | 92.6 |
Weight | 33 | 3753 | 0.35 (0.29–0.41) | 69.1 |
BMI | 29 | 4804 | 0.15 (0.09–0.21) | 67.6 |
PLT | ||||
Age | 4 | 280 | 0.07 (− 0.12–0.25) | 49.7 |
Height | 4 | 280 | 0.75 (0.53–0.88) | 89.3 |
Weight | 4 | 280 | 0.64 (0.31–0.83) | 90.8 |
BMI | 4 | 280 | 0.32 (0.10–0.51) | 60.2 |
Heterogeneity and publication bias
Outcome | No. studies | No. patients | Correlation (r) | Heterogeneity I2 (%) |
---|---|---|---|---|
North America | ||||
Age | 3 | 1837 | 0.01 (− 0.14–0.16) | 60.1 |
Height | 8 | 2540 | 0.41 (0.22–0.56) | 97.3 |
Weight | 6 | 445 | 0.43 (0.29–0.54) | 55.0 |
BMI | 5 | 2000 | 0.28 (0.09–0.45) | 84.7 |
Asia | ||||
Age | 17 | 2060 | 0.002 (− 0.05–0.05) | 14.5 |
Height | 23 | 2826 | 0.52 (0.40–0.62) | 90.3 |
Weight | 22 | 2772 | 0.36 (0.29–0.42) | 66.1 |
BMI | 20 | 2352 | 0.14 (0.07–0.19) | 52.2 |
Europe | ||||
Age | 3 | 269 | 0.05 (− 0.09–0.18) | 3.6 |
Height | 6 | 577 | 0.54 (0.22–0.75) | 92.8 |
Weight | 6 | 577 | 0.51 (0.23–0.71) | 91.0 |
BMI | 4 | 398 | 0.26 (− 0.00–0.49) | 83.0 |
Middle East | ||||
Age | 2 | 92 | 0.17 (− 0.34–0.60) | 83.8 |
Height | 3 | 270 | 0.43 (0.18–0.63) | 68.7 |
Weight | 3 | 270 | 0.25 (− 0.17–0.60) | 90.7 |
BMI | 3 | 270 | 0.11 (− 0.21–0.42) | 84.0 |