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Journal of Orthopaedic Surgery and Research logoLink to Journal of Orthopaedic Surgery and Research
. 2020 Sep 3;15:377. doi: 10.1186/s13018-020-01904-2

Prevalence and influencing factors of nonunion in patients with tibial fracture: systematic review and meta-analysis

Ruifeng Tian 1, Fang Zheng 1,2, Wei Zhao 1,3, Yuhui Zhang 4, Jinping Yuan 4, Bowen Zhang 1, Liangman Li 1,✉
PMCID: PMC7469357  PMID: 32883313

Abstract

Objective

The aim of this study is to assess the prevalence of nonunion in patients with tibia fracture and the association between influencing factors and tibia fracture nonunion.

Method

A database searches of PubMed, the Cochrane Library, EMBASE, China National Knowledge Infrastructure (CNKI), Weipu database, and Wanfang database from inception until June 2019 was conducted. The pooled prevalence, odds ratio (OR), and 95% confidence intervals (CI) were calculated with Stata software.

Results

In this study, 111 studies involving 41,429 subjects were included. In the study of the relationship between influencing factors and tibia fracture nonunion, 15 factors significantly influenced the fracture union, including > 60 years old, male, tobacco smoker, body mass index > 40, diabetes, nonsteroidal anti-inflammatory drugs (NSAIDs) user, opioids user, fracture of middle and distal tibia, high-energy fracture, open fracture, Gustilo-Anderson grade IIIB or IIIC, Müller AO Classification of Fractures C, open reduction, fixation model, and infection.

Conclusion

The prevalence of nonunion in patients with tibia fracture was 0.068 and 15 potential factors were associated with the prevalence. Closed reduction and minimally invasive percutaneous plate osteosynthesis (MIPPO) have the low risks of nonunion for the treatment of tibial fractures.

Keywords: Tibia fracture, Nonunion, Prevalence, Influencing factors, Systematic review

Introduction

Fracture is a common disease that has a great impact on patients’ lives. Take Canada as an example, fractures and dislocations of the lower limb make up 38% of all injury admissions [1]. It is estimated that the disability from traffic accidents (the major cause of fractures) will rank the top three of all causes of disability by 2020 [2].

Fracture nonunion is one of the most common complications of fracture. The rate of fracture nonunion varies greatly in different anatomical locations of the fracture [3], with an average incidence rate of 4.93% [4]. Fracture nonunion is a chronic condition in terms of pain, and functional and psychosocial disability [5]. Nonunion of some fractures can reduce the quality of life and even increase the risk of death [3]. The cost of treatment for fracture nonunion was much more than that of fracture union [6, 7]. Other economic burdens caused by prolonged disability and downtime of job are more difficult to quantify but must be considered [8].

Good blood supply is an important condition for fracture union [1, 9]. Compared to other long bones with abundant blood vessels and soft tissue, the tibia with a longer subcutaneous boundary normally has a poorer blood supply [10]. Therefore, tibial fracture has a higher risk of nonunion due to its special structure and blood supply. The definition of tibia fracture nonunion was no sign of union 9 months after surgical operation or no possibility of union if no further intervention was given assessed by surgeon [11].

Doctors need to know how to predict the risk of fracture nonunion and set up a plan to reduce the rate of fracture nonunion [8, 12]. In 2007, the “diamond concept” was introduced by Giannoudis et al., aiming to define what is required to achieve adequate fracture healing. This concept highlights the importance of three biological factors (osteogenic cells, osteoconductive scaffolds, growth factors) and a fourth factor known as mechanical stabilization. If one or more of these factors are altered, adequate fracture healing will be threatened [9, 13, 14].

Clinical and experimental studies have identified a number of potential factors that may help to predict fracture nonunion [15–18]. These factors include uncontrollable factors (for example, gender, age, underlying diseases, the way of injury) and controllable factors (for example, treatment method) [19, 20]. The uncontrollable factors of tibial nonunion may be similar to those of other anatomic sites. But there are too many influencing factors and even the same influencing factor may lead to different consequences in different anatomical positions [21]. For controllable influencing factors, the treatment of tibial fracture is also controversial [22]. Some doctors believe that intramedullary nailing (IMN) is the gold standard for the treatment of tibial fractures [23, 24]; however, most doctors consider that different treatment options have different advantages [25–28]. The use of non-steroidal anti-inflammatory drugs (NSAIDs) and the fixation of fibular fractures have also been considered as controversial factors for many years [29, 30].

Herein, we conducted a systematic review to explore the prevalence of nonunion in patients with tibia fracture and evaluate the association between influencing factors and tibia fracture nonunion. The study would provide valuable information for future prevention and treatment of tibia fracture nonunion.

Methods

Search strategy

The PubMed, Cochrane Library, EMBASE, CNKI (China National Knowledge Infrastructure), Wanfang database, and Weipu database were systematically searched, from inception to June 2019. The search keywords were “tibia” AND "fracture” AND “union OR nonunion OR disunion.” The manual search was performed through checking the reference lists of key studies and review articles to identify additional studies.

Study selection

An overall literature search was performed and relevant studies were screened independently by two reviewers (Ruifeng Tian, Fang Zheng). Initially, all the titles and abstracts which were identified based on the keywords were screened. Secondly, full texts of articles which were selected from the first phase were reviewed. Finally, the articles which had contents suitable for data extraction were included in the systematic review. Disagreements between the two reviewers were resolved by a third reviewer (Wei Zhao) via discussion and consensus.

Exclusion criteria

Exclusion criteria were as follows: neither English nor Chinese; animal model experiment; patients at the age of < 18; the cases of patients being lower than 10; insufficient information; duplicate publication; and obscure definition, such as delay union or mixed-descriptions of delay union and nonunion.

Data extraction

Relevant data were extracted independently by two reviewers (Ruifeng Tian and Yuhui Zhang). Each of the following information was entered into a pre-designed form: first author’s name, publication year, basic information of patients (including history of medication, unhealthy habits and basic diseases), fracture type, operative information, the number of all tibia fracture patients, and the number of tibia fracture nonunion patients. The information of 19 potentially influencing factors were also exacted for comparison analyses, including age, gender, tobacco smoke, drink, body mass index (BMI), diabetes, nonsteroidal anti-inflammatory drugs (NSAIDs) user, opioids user, osteofascial compartment syndrome, fracture site, injury energy (low or high energy that causes tibia fracture), open fracture, Gustilo-Anderson grade, Müller AO Classification of Fractures (AO), debride time (the time from injury to debride), open reduction, fibula fixation, infection, and fixation models. Disagreements between the two reviewers were resolved by a third reviewer (Jinping Yuan) via discussion and consensus.

Data analysis

Stata software (v12.0, Stata Corp, College Station, TX, USA) was used to assess all statistical analyses and a p < 0.05 was considered statistically significant. First, for exploring the prevalence of nonunion in patients with tibia fracture, the pooled prevalence and its 95% confidence intervals (CI) were calculated by using a random-effect model (p < 0.05, I2 > 50%), otherwise, or a fixed-effect model was selected (p > 0.05, I2 < 50%). When the prevalence rate in the included study was zero, double arcsine was used to deal with the data in case of data exclusion. Second, in the study of the association between potentially influencing factors and nonunion, the odds ratio (OR) and its 95% CI were calculated. To assess sources of heterogeneity, subgroup analyses were conducted, stratified by above 19 potentially influencing factors. Sensitivity analysis was performed by eliminating individual studies one by one. Publication biases were assessed by using the Begg’s test and Egger’s test.

Results

Characteristics of included studies

A total of 3846 studies (2195 English and 1651 Chinese) were searched. Following selection process (Fig. 1), 111 studies were included in this systematic review and meta-analysis [6, 15, 16, 19, 31–136].

Fig. 1.

Fig. 1

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Flow diagram of the study selection process

These studies were published between 1997 and 2019 from USA, China, Australia, Belarus, Canada, Egypt, France, India, Iran, Italy, Japan, Malaysia, Singapore, Turkey, and UK. There were 46 studies written in English and 65 studies in Chinese. The number of patients with tibia fracture ranged from 30 to 14638, and the prevalence of tibia fracture nonunion ranged from 0 to 42.7%. The basic information in all included studies were listed in Table 1.

Table 1.

The basic information and prevalence of tibia fracture nonunion in each included study

Author Year Country Age Male Female Number of tibia fracture Number of nonunion Prevalence
Su CA [31] 2018 USA 40.4 225 102 284 19 0.067
Mehta D [32] 2018 USA 35.2 29 11 40 4 0.100
Milenkovic S [33] 2018 USA 43.5 20 12 32 6 0.188
Chang BS [34] 2018 China 23-57 38 26 60 7 0.117
Liu BQ [35] 2018 China 36.1 46 5 51 3 0.059
Zhang JS [36] 2018 China 49.4 60 34 94 5 0.053
Zhang QL [37] 2018 China 35 50 36 86 0 0.000
Yu JQ [38] 2018 China 42.4 65 39 94 5 0.053
Jin PF [39] 2018 China 57.6 90 107 197 26 0.132
Ge Y [40] 2018 China 39.3 50 42 92 2 0.022
Fang YS [41] 2018 China 45.2 49 13 62 1 0.016
Li J [42] 2018 China 35.5 46 39 70 2 0.029
Xu DY [43] 2018 China 40.9 38 26 64 3 0.047
Li ZT [44] 2018 China 52.4 48 42 90 1 0.011
Dailey HL [45] 2018 UK 739 264 1003 121 0.121
Singh A [46] 2018 Singapore 38.2 101 2 103 44 0.427
Galal S [47] 2018 Egypt 37.2 52 8 60 2 0.033
Javdan M[48] 2017 USA 231 12 0.052
Auston DA [49] 2017 USA 42 184 131 315 17 0.054
Zura R [50] 2017 USA 18-63 6273 6535 12808 944 0.074
Thakore RV [15] 2017 USA 36 364 102 486 56 0.115
Chan DS [51] 2017 USA 44 82 32 114 24 0.211
Xiong SR [52] 2017 China 42.5 82 66 148 8 0.054
Javdan M [48] 2017 Iran 35.9 45 4 49 3 0.061
BeytemürÔ [53] 2017 Turkey 40.6 52 21 73 1 0.014
Daolagupu AK [54] 2017 India 37.14 32 10 42 3 0.071
Garg S [55] 2017 India 38.9 5 31 36 4 0.111
Mukherjee S [56] 2017 India 40.3 26 14 40 3 0.075
Blair JA [57] 2016 USA 42.2 156 28 184 16 0.087
Burrus MT [16] 2016 USA 8132 6506 14,638 1758 0.120
Avilucea FR [58] 2016 USA 40.6 162 54 216 29 0.134
O'Halloran K [19] 2016 USA 39.3 93 289 382 56 0.147
Barcakë [59] 2016 USA 64 5 0.078
Shen J [60] 2016 China 45 54 71 125 0 0.000
Fang JH [61] 2016 China 36.8 40 16 56 2 0.036
Hao LS [62] 2016 China 19-67 67 15 82 2 0.024
Hu H [63] 2016 China 36.7 30 22 52 1 0.019
Liu JQ [64] 2016 China 43.2 44 16 60 1 0.017
Rao HR [65] 2016 China 35.7 35 15 50 2 0.040
Bai T [66] 2016 China 36.8 43 17 60 4 0.067
Zhao KP [67] 2016 China 35.6 41 17 58 1 0.017
Uchiyama Y [68] 2016 Japan 41.9 77 8 85 3 0.035
Gupta P [69] 2016 India 42.7 22 8 30 1 0.033
Piątkowski K [70] 2015 USA 49.5 24 17 45 12 0.267
Sun KF [71] 2015 China 43.1 32 20 115 7 0.061
Sun JQ [72] 2015 China 48 35 21 56 7 0.125
Ma N [73] 2015 China 45.4 334 246 580 82 0.141
Huang H [74] 2015 China 17-65 52 44 96 5 0.052
Huang PZ [75] 2015 China 32 43 13 56 1 0.018
Zhang YH [76] 2015 China 36.5 49 21 70 2 0.029
Luo BX [77] 2015 China 38.5 47 31 78 1 0.013
Wang B [78] 2015 China 41.2 39 33 72 2 0.028
Cui LH [79] 2015 China 37.5 53 21 74 2 0.027
Meng YH [80] 2015 China 31.6 19 35 54 1 0.019
Gong Y [81] 2015 China 16-39 38 32 70 11 0.157
Lian HK [82] 2015 China 35.1 51 43 94 4 0.043
Meena RC [83] 2015 India 37.5 32 12 44 2 0.045
Sathiyakumar V [84] 2014 USA 37.5 63 30 93 17 0.183
Li Y [85] 2014 China. 43.3 116 5 121 2 0.017
Dai QH [86] 2014 China 34.5 23 19 42 0 0.000
Wu ZH [87] 2014 China 48.5 32 18 50 1 0.020
Li ZZ [88] 2014 China 43.8 76 44 60 5 0.083
Ren Y [89] 2014 China 34.7 49 21 70 4 0.057
Luan HX [90] 2014 China 37.1 78 20 98 6 0.061
Zhang WJ [91] 2014 China 44 43 25 68 3 0.044
Heng WX [92] 2014 China 18-79 45 23 68 4 0.059
Yavuz U [93] 2014 Turkey 42 32 23 55 3 0.055
Lack WD [94] 2014 USA 45 92 71 163 13 0.080
Berlusconi M [95] 2014 Italy 45 42 18 60 5 0.083
Antonovaë [6] 2013 USA 52.5 378 475 853 99 0.116
Huang Q [96] 2013 China 36.9 80 40 120 3 0.025
Gong M [97] 2013 China 40.3 41 11 52 2 0.038
Lv YM [98] 2013 China 39.1 77 34 111 6 0.054
Xu YD [99] 2013 China 39 105 58 163 2 0.012
Clement ND [100] 2013 UK 77.9 63 170 233 23 0.099
Sitnik AA [101] 2013 Belarus 43 54 26 80 7 0.088
Yusof NM [102] 2013 Malaysia 24.5 52 6 58 10 0.172
Bishop JA [103] 2012 USA 32 1 0.031
Lin ZF [104] 2012 China 36.6 222 194 416 33 0.079
Zhang H [105] 2012 China 39.6 58 38 96 1 0.010
Jia QT [106] 2012 China 36 61 27 88 4 0.045
Zhou JL [107] 2012 China 53 43 9 52 10 0.192
Rouhani A [108] 2012 Iran 26.4 45 8 54 3 0.056
Vallier HA [109] 2011 USA 38.3 85 19 114 6 0.053
Zhu DK [110] 2011 China 18-76 53 31 84 3 0.036
Zhao DL [111] 2011 China 37.8 54 26 80 1 0.013
Liu F [112] 2011 China 32.6 32 14 46 4 0.087
Enninghorst N [113] 2011 Australia 42.4 66 23 89 26 0.292
Xu JQ [114] 2009 China 36.3 121 49 170 8 0.047
Li ZG [115] 2009 China 35.8 71 56 127 3 0.024
Mahmudi N [116] 2009 China 37 34 10 44 3 0.068
Deng HP [117] 2009 China 40.3 51 34 85 4 0.047
Dong JH [118] 2009 China 18-74 77 51 128 2 0.016
Fu KL [119] 2009 China 112 11 0.098
Zhou L [120] 2009 China 37.9 52 41 93 5 0.054
Lang ZY [121] 2009 China 33.6 51 16 67 2 0.030
Wu C [122] 2009 China 19-71 25 12 37 2 0.054
Li QM [123] 2009 China 37.6 168 51 219 6 0.027
Yokoyama K [124] 2008 Japan 34.6 70 14 84 17 0.202
Aderinto J [125] 2008 UK 54 3 0.056
Lu HY [126] 2007 China 34.5 158 98 256 9 0.035
Hu GZ [127] 2007 China 33.4 301 116 396 11 0.028
Zeng CJ [128] 2006 China 30.7 390 264 541 14 0.026
Zhang YL [129] 2006 China 35 73 25 98 9 0.092
Zhao XZ [130] 2006 China 43.8 52 26 78 5 0.064
Zhu GH [131] 2005 China 34 55 23 78 5 0.064
Harris I [132] 2005 Australia 34 124 39 163 13 0.080
Cole PA [133] 2004 USA 89 2 0.022
Bonnevialle P [134] 2003 France 40.8 34 15 49 8 0.163
Harvey EJ [135] 2002 Canada 110 13 0.118
Keating J [136] 1997 USA 112 9 0.080
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Pooled results, sensitive analysis, publication bias of the prevalence of tibia fracture nonunion

Based on the results of random-effects method (p < 0.05, I2 > 50%), the prevalence of nonunion from tibia fracture patient was 0.068 (95% CI 0.060–0.077) (Fig. 2, Table 2). The sensitive analysis demonstrated that there was no individual studies significantly affected the pooled results. The publication bias were found in pooled results (t = 3.19, p = 0.002) (Fig. 3).

Fig. 2.

Fig. 2

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The forest plot of prevalence of tibia fracture nonunion

Table 2.

The pooled results and subgroup analysis of prevalence of nonunion from tibia fracture patient

Number of study N n Prevalence rate Heterogeneity Model
effect size lower limit upper limit I2 p
Total 111 41429 3817 0.068 0.060 0.077 86.60% < 0.01 Random
1. Age (year) < 60 3 545 60 0.125 0.060 0.189 77.50% 0.012 Random
> 60 3 316 65 0.204 0.160 0.249 0.00% 0.689 Fixed
2. Gender Male 11 8186 790 0.131 0.104 0.159 77.80% < 0.01 Random
Female 11 8123 618 0.118 0.085 0.150 84.50% < 0.01 Random
3. Tobacco smoker Yes 8 2263 299 0.173 0.119 0.226 91.80% < 0.01 Random
No 8 12177 888 0.111 0.072 0.150 87.30% < 0.01 Random
4. Drink Yes 2 348 42 0.136 0.036 0.235 82.50% 0.017 Random
No 2 12842 958 0.098 0.043 0.152 86.90% 0.006 Random
5. Body mass index < 30 2 24466 2257 0.091 0.049 0.133 99.30% < 0.01 Random
> 30 2 3790 451 0.119 0.109 0.129 0.00% 0.557 Fixed
30–40 2 2507 236 0.094 0.083 0.105 0.00% 0.441 Fixed
< 40 2 26973 2493 0.091 0.053 0.128 99.20% < 0.01 Random
> 40 2 1283 215 0.160 0.020 0.218 87.80% 0.004 Random
6. Diabetes Yes 4 347 73 0.221 0.178 0.267 8.50% 0.335 Fixed
No 4 984 103 0.102 0.065 0.139 67.50% 0.046 Random
Yes 3 371 58 0.153 0.116 0.189 0.00% 0.420 Fixed
No 3 1197 144 0.117 0.099 0.135 59.90% 0.083 Random
8. Opioids user Yes 3 1035 145 0.140 0.118 0.161 0.00% 0.694 Fixed
No 3 522 58 0.097 0.031 0.164 78.40% 0.010 Random
9. Fracture site Proximal 7 586 30 0.043 0.027 0.06 26.50% 0.254 Fixed
Middle 7 724 115 0.146 0.080 0.211 84.60% < 0.01 Random
Distal 7 614 88 0.139 0.104 0.178 24.10% 0.253 Fixed
10. Injury energy High 4 710 105 0.149 0.083 0.241 83.60% < 0.01 Random
Low 4 298 22 0.065 0.007 0.175 87.30% < 0.01 Random
11.Open fracture Yes 10 14037 916 0.062 0.049 0.074 56.20% 0.015 Random
On 10 1985 390 0.197 0.145 0.294 84.80% < 0.01 Random
12. Gustilo-Anderson gradea I or II 9 680 57 0.070 0.051 0.089 31.30% 0.168 Fixed
IIIA 9 394 55 0.130 0.097 0.163 0.00% 0.686 Fixed
IIIB or IIIC 9 220 89 0.382 0.198 0.566 88.90% < 0.01 random
13.Müller AO Classification of Fractures (AO) classificationb A 7 1039 69 0.059 0.027 0.090 68.90% 0.004 Random
B 7 600 103 0.140 0.086 0.204 65.90% 0.007 Random
C 7 285 54 0.158 0.078 0.260 74.50% 0.001 Random
14. Debride time < 6 h 2 138 41 0.302 0.074 0.530 89.10% 0.002 Random
> 6 h 2 49 20 0.405 0.268 0.541 0.00% 0.411 Fixed
15. Open reduction Yes 9 573 48 0.075 0.043 0.107 52.40% 0.032 Random
No 9 606 26 0.043 0.028 0.060 42.10% 0.086 Fixed
16. Fixation modec ORIF 41 6216 703 0.081 0.058 0.107 82.10% < 0.01 Random
IMN 51 12642 1326 0.054 0.040 0.070 77.30% < 0.01 Random
MIPPO 25 988 18 0.023 0.015 0.032 0.00% 0.835 Fixed
External fixation 680 33 0.055 0.023 0.098 76.90% < 0.01 Random
Conservative treatment 4 116 22 0.134 0.003 0.409 92.10% < 0.01 Random
17. Fibula fixed Yes 7 166 11 0.073 0.027 0.140 53.20% 0.046 Random
No 7 538 69 0.122 0.094 0.149 < 0.01 0.611 Fixed
18. Osteofascial compartment syndrome Yes 3 210 31 0.134 0.088 0.179 61.90% 0.072 Fixed
No 3 1359 162 0.105 0.058 0.151 85.40% 0.001 Random
19. Infection Yes 2 217 84 0.510 0.155 0.866 93.80% < 0.01 Random
No 2 1366 119 0.076 0.022 0.129 92.80% < 0.01 Random
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aGustilo-Anderson classification: grade I: clean wound < 1 cm in length; grade II: wound 1–10 cm in length without extensive soft-tissue damage, flaps or avulsions; grade III: extensive soft-tissue laceration (>10 cm) or tissue loss/damage or an open segmental fracture; grade IIIa: adequate periosteal coverage of the fracture bone despite the extensive soft-tissue laceration or damage; grade IIIb: extensive soft-tissue loss, periosteal stripping and bone damage, usually associated with massive contamination; grade IIIc: associated with an arterial injury requiring repair, irrespective of degree of soft-tissue injury

bAO classification of tibia fractures with designations of A: simple, B: wedge, C: complex

cORIF open reduction and internal fixation, IMN intramedullary nailing, MIPPO minimally invasive plate osteosynthesis

Fig. 3.

Fig. 3

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The publication bias of prevalence of tibia fracture nonunion

Subgroup analysis of prevalence of tibia fracture nonunion and comparison results

The prevalence of tibia fracture nonunion in different countries were of various (Tables 2, 3, and 4), for example, USA was 0.094 (95% CI 0.075–0.114), China was 0.047 (95% CI 0.039–0.057), etc.

Table 3.

The comparison results stratified by 19 influencing factors

Study Comparison results Heterogeneity Model
p OR lower limit upper limit I2 p
1. Age (year) > 60 vs. < 60 3 < 0.05 2.602 1.686 4.016 48.70% 0.142 Fixed
2. Gender Male vs. Female 11 < 0.05 1.256 1.122 1.407 14.00% 0.311 Fixed
3. Tobacco smoker Yes vs. No 8 < 0.05 1.692 1.458 1.964 49.30% 0.055 Fixed
4. Drink Yes vs. No 2 0.083 1.367 0.960 1.947 0.00% 0.518 Fixed
5. Body mass index (BMI) 30 < BMI < 40 vs. BMI < 30 2 0.801 1.085 0.575 2.050 93.70% < 0.05 Random
BMI > 40 vs. BMI < 30 2 < 0.05 1.874 1.607 2.185 0.00% 0.660 Fixed
BMI > 30 vs. BMI < 30 2 0.189 1.351 0.862 2.119 93.00% < 0.05 Random
BMI > 40 vs. 30 < BMI < 40 2 0.045 1.773 1.014 3.102 84.30% 0.012 Random
BMI > 40 vs. BMI < 40 2 < 0.05 1.899 1.630 2.212 0.00% 0.892 Fixed
6. Diabetes Yes vs. No 3 < 0.05 2.731 1.857 4.014 32.20% 0.229 Fixed
7. Nonsteroidal anti-inflammatory drugs user Yes vs. No 3 0.018 1.536 1.076 2.194 0.00% 0.384 Fixed
8. Opioids user Yes vs. No 3 0.012 2.010 1.166 3.468 0.00% 0.370 Fixed
9. Fracture site Middle vs. Proximal 7 < 0.05 3.152 2.019 4.922 0.00% 0.788 Fixed
Distal vs. Proximal 7 < 0.05 2.877 1.822 4.543 0.00% 0.911 Fixed
Distal vs. Middle 7 0.670 0.932 0.673 1.290 0.00% 0.650 Fixed
10. Injury energy High vs. Low 4 0.001 2.602 1.484 4.562 35.90% 0.182 Fixed
11. Open fracture Yes vs. No 9 < 0.05 2.846 1.700 4.202 16.50% 0.296 Fixed
12. Gustilo-Anderson gradea IIIA vs. I or II 9 0.005 1.831 1.204 2.784 0.00% 0.847 Fixed
IIIB or IIIC vs. I or II 9 < 0.05 7.202 4.781 10.848 4.60% 0.394 Fixed
IIIB or IIIC vs. IIIA 9 < 0.05 3.695 2.422 5.639 32.60% 0.168 Fixed
13. Müller AO Classification of Fractures (AO) classificationb B vs. A 7 0.010 2.522 1.249 5.930 54.20% 0.041 Random
C vs. A 7 < 0.05 3.685 2.405 5.648 37.00% 0.160 Fixed
C vs. B 7 < 0.05 3.569 2.428 5.325 39.60% 0.142 Fixed
14. Debride time < 6 h vs. > 6 h 2 0.631 1.190 0.585 2.419 0.00% 0.520 Fixed
15. Open reduction Yes vs. No 9 < 0.05 2.887 1.715 4.861 26.20% 0.220 Fixed
16. Fixation modec IMN vs. MIPPO 15 0.003 2.681 1.397 5.146 0.00% 0.980 Fixed
IMN vs. ORIF 28 0.020 1.127 1.019 1.247 54.10% <0.05 Random
ORIF vs. MIPPO 7 0.010 3.495 1.351 9.045 0.00% 0.859 Fixed
External vs. ORIF 10 0.115 0.506 0.217 1.182 54.00% 0.016 Random
Conservative vs. ORIF 4 0.264 1.496 0.737 3.035 64.10% 0.062 Fixed
External vs. IMN 10 0.993 1.006 0.266 3.806 55.40% 0.022 Random
17. Fibula fixed Yes vs. No 7 0.435 1.317 0.659 2.634 47.60% 0.075 Random
18. Osteofascial compartment syndrome Yes vs. No 3 0.106 1.420 0.968 2.173 80.30% 0.006 Fixed
19. Infection Yes vs. No 2 < 0.05 11.877 7.461 18.906 52.10% 0.149 Fixed
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aGustilo-Anderson classification: grade I: clean wound < 1 cm in length; grade II: wound 1–10 cm in length without extensive soft-tissue damage, flaps or avulsions; grade III: extensive soft-tissue laceration (> 10 cm) or tissue loss/damage or an open segmental fracture; grade IIIa: adequate periosteal coverage of the fracture bone despite the extensive soft-tissue laceration or damage; grade IIIb: extensive soft-tissue loss, periosteal stripping and bone damage, usually associated with massive contamination; grade IIIc: associated with an arterial injury requiring repair, irrespective of degree of soft-tissue injury

bAO classification of tibia fractures with designations of A: simple, B: wedge, C: complex

cORIF open reduction and internal fixation, IMN intramedullary nailing, MIPPO minimally invasive plate osteosynthesis

Table 4.

Prevalence of nonunion from tibia fracture in different countries

Number of study N n Prevalence rate Heterogeneity Model
Effect size Lower limit Upper limit I2 p
USA 19 30167 3083 0.094 0.075 0.114 93.40% < 0.01 Random
China 68 7550 396 0.047 0.039 0.057 69.50% < 0.01 Random
Australia 2 252 39 0.182 0.026 0.389 93.90% < 0.01 Random
Belarus 1 80 7 0.088 – – – – –
Canada 1 110 13 0.118 – – – – –
Charlotte 1 163 13 0.08 – – – – –
Egypt 1 60 2 0.033 – – – – –
France 1 49 8 0.162 – – – – –
India 5 150 10 0.059 0.026 0.092 0 0.73 Fixed
Iran 3 152 9 0.059 0.022 0.097 0 0.99 Fixed
Italy 1 60 5 0.083 – – – – –
Japan 2 169 20 0.114 0.049 0.278 91.70% 0.001 Random
Malaysia 1 58 10 0.172 – – – – –
Singapore 1 103 44 0.427 – – – – –
Turkey 1 73 1 0.014 – – – – –
UK 4 1042 156 0.108 0.092 0.124 47.60% 0.126 Fixed
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In the following comparisons of influencing factors (Table 3), each of the former prevalence of tibia fracture nonunion was significantly higher than the latter one (p < 0.05), i.e., > 60 years old (0.204) vs. < 60 years old (0.125), male (0.131) vs. female (0.118), tobacco smoker (0.173) vs. non-smoking (0.111), BMI > 40 (0.160) vs. BMI < 40 (0.091), diabetes (0.221) vs. no diabetes (0.102), NSAIDs user (0.153) vs. none NSAIDs user (0.117), opioids user (0.140) vs. none opioids user (0.097), fracture of middle segment (0.146) vs. proximal segment (0.043), fracture of distal segment (0.139) vs. proximal segment (0.043), high-energy injury (0.149) vs. low-energy injury (0.065), open fracture (0.197) vs. close fracture (0.062), Gustilo-Anderson grade I or II (0.070) vs. IIIA (0.130) vs. IIIB and IIIC (0.382), AO Classification A (0.059) vs. B (0.140) vs. C (0.158), open reduction (0.075) vs. close reduction (0.043), infection (0.510) vs. without infection (0.076). No significant difference was found between other comparisons (p > 0.05).

There were 5 fixation models of tibial fractures available, including open reduction and internal fixation (ORIF), intramedullary nailing (IMN), minimally invasive percutaneous plate osteosynthesis (MIPPO), external fixation, and conservative treatment. Significant difference was found between each other comparison of the following 3 fixation models, ORIF (0.081) vs. IMN (0.054) vs. MIPPO (0.023) (p < 0.05) (Fig. 4). No significant difference was found between external and ORIF, conservative and ORIF, or external and IMN (p > 0.05).

Fig. 4.

Fig. 4

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The comparison of MIPO with IMN

Discussion

To our knowledge, this is the first systematic review and meta-analysis to estimate the prevalence of nonunion in patients with tibia fracture and the relationship between different influence factors and tibia fracture nonunion. The pooled prevalence of tibial fracture nonunion was 0.068. Different countries were in variety of prevalence, indicating a heredity disparity. The lowest prevalence was seen in Turkey (0.014) and next was Egypt (0.033); however, the numbers of included studies were so small that the conclusions were not so robust. There were 68 studies that were conducted in China involving 7550 tibia fracture patients and the prevalence of nonunion was 0.047. However, one study in Singapore, a country that has lots of Chinese population, presented a very high prevalence of tibia fracture nonunion 0.427, indicating other influencing factors other than heredity. In calendar year 2011, an inception cohort study in a large payer database of patients with fracture in the USA was conducted using patient-level health claims for medical and drug expenses compiled for approximately 12,808 patients, and the prevalence of tibial fracture nonunion was reported to be 0.074 [137]. In contrast, the present systematic review involved 30,167 patients in a total of 19 studies conducted in the USA and the prevalence was 0.094. The pooled results enabled a larger sample size and accessed more to the real conclusion.

Some influencing factors contributed to the nonunion of tibial fractures. In 2016, O'Halloran K et al. created a Nonunion Risk Determination Score (NURDS) to predict nonunion risk, based on 7 influencing factors (p < 0.05, OR > 2), including flaps, compartment syndrome, chronic condition(s), open fractures, male gender, grade of American Society of Anesthesiologists Physical Status, and percent cortical contact. While another 2 factors including spiral fractures and low-energy injuries can be predictive of union [19]. In our study, we found more influencing factors, including age > 60 years old, diabetes, opioids user, middle and distal fracture, high-energy injury, open fracture, Gustilo-Anderson grade IIIB and IIIC, and AO Classification C met above criteria (p < 0.05, OR > 2) and can be regarded as predictive indicators. Still, there were some other influencing factors, including male, tobacco smoker, BMI > 40, and NSAIDs user, partially predicated the risks (p < 0.05, OR < 2).

The present study showed that BMI > 40 and diabetes were the influencing factors of nonunion of tibia fractures. With the improvement of quality of life, the negative impact of obesity has gradually become a hot issue of concern. Obesity can lead to vitamin D deficiency, and whether there is a causal relationship between fracture nonunion and vitamin D deficiency is the focus of discussion [138, 139]. But we cannot ignore the fact that diabetes mellitus is closely related to obesity. In our study, the use of NSAIDs was also associated with fracture nonunion. Some experiments have proved that NSAIDs can temporarily inhibit the process of fracture union [140, 141]; however, other studies considered that the pain caused by fracture nonunion of patients led to their resorting to NSAIDs [142].

Our comparison showed that open reduction had a higher rate of fracture nonunion than closed reduction. In surgery, although open reduction can bring good fracture repair, but closed reduction can better protect blood supply and soft tissue. In addition, our study did not find a relationship between fibular fixation and nonunion rates of tibial fractures. However, Strauss EJ and Kumar A’ experiments on cadavers showed that fibular fixation can increase the stability of tibial fractures after surgery [143–145]. So whether it is necessary to fix the fibula for the treatment of tibial fracture accompanied by fibular fracture should be further determined.

The choice of fixation mode is a way to control the nonunion rate of tibial fracture artificially [146, 147]. We compared 5 fixation modes available. The nonunion rate of conservative treatment was the highest one compared with that of surgical treatment. This is obviously different from the lowest rate reported by Li H et al. [148]. This may be related to the insufficient number of articles in conservative treatment. Compared with traditional ORIF, IMN and MIPPO have lower fracture nonunion rate. No significant difference was found between external fixation and ORIF. Ebraheim NA et al. reported that IMN can achieve better healing effect in the treatment of tibial fractures, comparing to ORIF and external fixation [149]. MIPPO had the lowest nonunion rate of all fixation modes. It was proved that MIPPO can maximize the protection of soft tissue and bone marrow around the fracture site [150]. The above 5 fixation modes destroy the necessary conditions of fracture healing to varying degrees. However, it is worth mentioning that different options have different advantages in the treatment of tibial fractures [151, 152]. For example, in distal tibial fractures, more comminuted fractures would rather require open reduction than “simple” type A fractures. So it is unreasonable to only consider the nonunion rate of fracture of operation [148].

The systematic review and meta-analysis had made strict inclusion and exclusion criteria, but still had some limitations and bias which may be unavoidable. Firstly, due to different attentions of individual studies, the influencing factors were only extracted from partial studies with available data and some other influencing factors such as hemoglobin and bone defect were not mentioned. Secondly, different doctors and different hospitals had a variety of surgical technologies and conditions, which may cause unavoidable bias. Thirdly, the number of included studies and the data for meta-analysis were limited which may affect the final results to a certain degree. Fourthly, publication bias was found in the study. Therefore, the data from literature in other languages, more areas, and ongoing studies are required to reflect a more accurate and wide variation. Finally, non-randomized controlled trials (nRCTs) were involved in this systematic review. As a result, subjective factors may affect the result. More rigorous designs and large RCTs are required to make further verification.

In conclusion, the prevalence of nonunion in patients with tibia fracture was 0.068 and 15 potential factors were associated with the prevalence. Closed reduction and MIPPO have low risks of nonunion for the treatment of tibial fractures. A series of factors shed the light which may affect the union rate of tibial fracture for doctors’ reference, and provide the probability of nonunion of tibial fracture under different treatment schemes. The authors hope to help doctors assess the risk of nonunion and propose the most suitable treatment for patients with tibial fractures under different conditions.

Acknowledgements

Not applicable.

Abbreviations

CNKI

China National Knowledge Infrastructure

OR

Odds ratio

CI

Confidence intervals

NSAIDs

Nonsteroidal anti-inflammatory drugs

AO

Müller AO Classification of Fractures

MIPPO

Minimally invasive percutaneous plate osteosynthesis

IMN

Intramedullary nailing

NRCTs

Non-randomized controlled trials

Authors’ contributions

An overall literature search was performed and relevant studies were screened by RT; extracted the relevant data. An overall literature search was performed and relevant studies were screened independently by FZ. Disagreements of data were resolved by WZ via discussion and consensus. YZ extracted the relevant data. BZ extracted the relevant data. Disagreements of data extraction were resolved by JY via discussion and consensus. LL: Technical guidance of the writing process. All authors read and approved the final manuscript.

Funding

The study was supported by Liaoning Provincial Natural Science Fund (code 201602837).

Availability of data and materials

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study has obtained ethics approval and consent of the ethics committee in our hospital.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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