Key words
risk factor - subscapularis tear - coracohumeral distance - long head of biceps tendon injury - coracoid overlap
Introduction
For males or females of all ages, shoulder pain and dysfunction could be a sign of
rotator cuff injury. The subscapularis (SSC) muscle is important for the balance,
stability and internal rotation of the shoulder joint. Relevant studies have found
that 12%–50% of patients present with SSC tendon tears
during arthroscopy [1]
[2].
Magnetic resonance imaging (MRI) is a noninvasive method of examining rotator cuff
injuries [3]. Although MRI has a sensitivity of more
than 90% for both supraspinatus and infraspinatus tears, the use of MRI as a
tool for SSC tears is challenging [4]. Furukawa R et
al. showed that when the MRI field strength was 3.0 T, the sensitivity of
diagnosis of SSC tears was 57.9% and 60.5% in the axial and oblique
sagittal positions, respectively [3]. If the field
strength was 1.5 T, the sensitivity of diagnosis of SSC tears was
45.3% [5]. A systematic review showed that MRI
for the diagnosis of SSC tears had an overall sensitivity of 68% [6]. However, more than half of the studies used
magnetic resonance arthrography (MRA) as a diagnostic tool, which increases the
sensitivity of diagnosis. In addition, the thickness and size of SSC tears have a
direct influence on the diagnostic accuracy of MRI. The smaller a torn area is, the
lower the accuracy of the diagnosis [7]
[8]. In addition, some researchers used CT as a
preoperative diagnostic criterion for rotator cuff tears. They looked for
correlations with rotator cuff tears by taking some anatomical measurements on CT
[9]. However, due to regional differences,
differences in research designs and research indicators, differences in
patients’ races, and differences in economic conditions, the indicator has
different research effects in different studies [9].
Although the repair of SSC tears with arthroscopy has achieved good clinical results,
the sensitivity of diagnosis of SSC tears with MRI is not ideal at present [10]. If an SSC tear is missed, it may cause long-term
shoulder pain or dysfunction with muscle atrophy, fat infiltration, and extended
tear areas [11]. During arthroscopic rotator cuff
repair, it was observed that SSC tears were missed in 43.1% of patients, and
the fatty infiltration of SSC tendons, which was initially overlooked, showed
further expansion during revision [12]. SSC tendon
injury is easily missed, has a low sensitivity of diagnosis, has a high degree of
involvement in important functions, and has great clinical significance. Therefore,
it is necessary to improve methods for the early diagnosis of SSC tears. In-depth
studies of SSC tendon injury found that the morphological changes of the coracoid
process in subcoracoid impingement may result in the pathological injury of SSC
tendons [13]. Researchers believe that some imaging
signs may be related to SSC tears [14]
[15]
[16]. These findings
suggest that the sensitivity and accuracy of the diagnosis of SSC tears may be
improved by measuring a number of imaging indicators related to SSC tears.
Relevant studies have shown that age, sex, coracoid overlap (CO), coracohumeral
distance (CHD), long head of the biceps tendon (LHB) injury, and the dominant arm
may be related to SSC tears [6]
[9]
[15]
[17]
[18]
[19]
[20]. The purpose of
our study was to summarize previously demonstrated correlations between the above
indicators and SSC tears through meta-analysis and to identify the most valuable
predictive indicators for SSC tears to help clinicians make early diagnoses and
formulate early treatment plans for SSC injuries. To our knowledge, this is the
first meta-analysis evaluating risk factors for SSC tears.
Materials and Methods
Search strategy
The MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines
were used to guide the meta-analysis [21]. First,
PROSPERO (International prospective register of systematic reviews) searches
revealed that there was no systematic review or meta-analysis related to risk
factors for SSC tears before our research was performed. Second, PROSPERO was
used to register the protocol of the meta-analysis online, as recommended by
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
guidelines, and the registration number is CRD42022332681. EMBASE, PubMed, the
Cochrane Library and Web of Science were searched from inception up to February
1, 2023. The keywords used were “rotator cuff injury”,
“rotator cuff tears”, “rotator cuff tendinosis”,
“rotator cuff tendinitis”, “subscapularis
tears”, and “risk factors”. The search strategy is shown
in [Table 1].
Table 1 Search strategy.
PubMed : 479 results (up to 1 January 2023)
|
((“rotator cuff
injury”[Title/Abstract] OR
“subscapularis
tears”[Title/Abstract] OR “rotator
cuff tears”[Title/Abstract] OR
“rotator cuff tear”[Title/Abstract]
OR “rotator cuff
tendinosis”[Title/Abstract] OR
“rotator cuff
tendinitis”[Title/Abstract] OR
“Rotator Cuff Injuries”[MeSH Terms]) AND
(“Risk Factors”[MeSH Terms] OR
(“Risk Factors”[Title/Abstract] OR
“risk factor”[Title/Abstract])))
|
Embase : 734 results (up to 1 January 2023)
|
#1 'risk factor'/exp
|
#2 'risk factor':ab,ti
|
#3 #1 OR #2
|
#4 'rotator cuff injury'/exp
|
#5 ('rotator cuff injury':ab,ti OR
'rotator cuff tears':ab,ti OR
'rotator cuff tear':ab,ti OR
'rotator cuff tendinosis':ab,ti OR
'rotator cuff tendinitis':ab,ti OR
'subscapularis tears':ab,ti)
|
#6 #4 OR #5
|
#7 #3 AND #6
|
Cochrane library : 35 results (up to 1 January
2023)
|
#1 MeSH descriptor: [Rotator Cuff Injuries] explode all
trees
|
#2 (Rotator Cuff Injury):ti,ab,kw OR (subscapularis
Tears):ti,ab,kw OR (Rotator Cuff Tears):ti,ab,kw OR (Rotator
Cuff Tear):ti,ab,kw OR (Rotator Cuff Tendinosis):ti,ab,kw
|
#3 #1 OR #2
|
#4 MeSH descriptor: [Risk Factors] explode all trees
|
#5 (risk factor):ti,ab,kw OR (risk factors):ti,ab,kw
|
#6 #4 OR #5
|
#7 #3 AND #6
|
Web of science : 400 results (up to 1 January 2023)
|
#1 AB=(Rotator Cuff Injury OR Rotator Cuff Tears OR
Rotator Cuff Tear OR Rotator Cuff Tendinosis OR Rotator Cuff
Tendinitis OR subscapularis tears)
|
#2 AB=(risk factors OR risk factor)
|
#3 #2 AND #1
|
Criteria for inclusion and exclusion
The inclusion criteria were as follows: (1) patients in the experimental group
had SSC tears, and those in the control group did not have SSC tears; (2) the
experimental group and control group were identified with arthroscopy; (3)
studies were case-control, cohort, or cross-sectional studies; (4) there was at
least one evaluation indicator in the included studies; and (5) there was no
language restriction.
Studies with incomplete data, duplicate studies, and studies with patients who
had undergone previous shoulder operations were excluded. Studies that were
unpublished or in progress were excluded.
Study screening and data extraction
Two researchers independently checked the studies and extracted the data. The
third participant resolved any discrepancies through discussion or negotiation.
After duplicates were removed, the abstracts and full texts were read to
determine which studies could be included. The extracted information included
the basic characteristics of the studies, such as the first author, country,
study design, publication dates, sex of the patients, level of evidence, method
of diagnosis, preoperative evaluation method, and blinding procedures. In
addition to the above, for some studies, if there was some unmentioned but
important information that we needed, we contacted the corresponding author of
the original study by email.
After the initial screening, a total of 1 648 records were identified. After
title, abstract and full text filtering, 417 duplicates were deleted, and 1 206
records that did not meet the criteria were excluded. Finally, there were ten
eligible studies that were included in the qualitative and quantitative
analysis. [Fig. 1] provides a PRISMA flowchart
for the screening of studies conducted through this meta-analysis.
Fig. 1 Flow diagram showing the study screening process.
Assessment of study quality
Our meta-analysis included cohort studies, cross-sectional studies, and
case–control studies. The Newcastle–Ottawa Scale (NOS) quality
scoring system was used to assess the risk of bias in case-control studies and
cohort studies [22]. The scale was evaluated in
three aspects: study population selection, comparability between groups, and
measurement of exposure factors. The maximal possible score is 9: 0–3 is
low quality, 4–6 is medium quality, and 7–9 is high quality. Two
researchers met to complete the work.
Statistical analysis
RevMan 5.3 software (Cochrane, London, UK ) and Stata 15.1 software (StataCorp
College Station, Texas, USA) were used for our meta-analysis. The risk ratios
(RRs) and the weighted mean differences (WMDs) were used to evaluate the effect
size of categorical variables and continuous variables, respectively. The
95% confidence interval (95% CI) was calculated for each effect
size. Heterogeneity tests were used to assess heterogeneity among the included
studies. If there was no heterogeneity
(I2<=50%), the overall effect size was
evaluated by a fixed effects model. If there was heterogeneity
(I2>50%), the overall effect size was evaluated by a
random effects model. The Egger test was performed with Stata 15.1 software to
assess publication bias. P<0.05 was considered to be statistically
significant.
Results
Characteristics of included articles
Ten studies from seven countries were included [6]
[9]
[15]
[17]
[18]
[19]
[20]
[23]
[24]
[25]. A total of 2 126 patients and
six predictors were included. A total of seven articles had a level of evidence
of “3”, one article had a level of evidence of
“1”, one article had a level of evidence of “2”,
and two articles had a level of evidence of “4”. Among the 10
references, all arthroscopy was used for the diagnosis in all articles. MRI was
used as the evaluation standard in eight studies, while computed tomography (CT)
was used as the evaluation standard in two studies. Six studies were
single-blinded, and four studies were not blinded. The basic characteristics of
the included studies are shown in [Table 2].
Table 2 The Characteristics of Included
Studies.
Studies(first author)
|
Year
|
Country
|
Experimental group(n)
|
Control group(n)
|
Sex (E: F/M) (C: F/M)
|
Age (y) (E/C)
|
Study design
|
Level of evidence
|
Diagnosis
|
Method of evaluation
|
Blind
|
Adam C. Watson
|
2017
|
Australia
|
53
|
23
|
−
|
−
|
case-control design
|
III
|
arthroscopy
|
MRI
|
no-blinded
|
Eduardo A. Malavolta
|
2016
|
Brazil
|
50
|
43
|
30/20
|
58.54±6.929/
|
case-control design
|
III
|
arthroscopy
|
MRI
|
single-blinded
|
29/14
|
53.49±8.143
|
Joong-Bae Seo
|
2019
|
Korea
|
114
|
57
|
39/75
|
60.3±10.7/
|
case-control design
|
III
|
arthroscopy
|
MRI
|
single-blinded
|
16/41
|
51.3±9.8
|
Jun Kawamata
|
2022
|
Japan
|
53
|
77
|
25/28
|
68.4±10/
|
case-control design
|
III
|
arthroscopy
|
CT
|
single-blinded
|
28/49
|
61.5±11.8
|
Mehmet Çetinkaya
|
2016
|
Turkey
|
141
|
78
|
93/48
|
57.85±10.44/
|
case-control design
|
III
|
arthroscopy
|
MRI
|
no-blinded
|
56/22
|
55.46±11.73
|
Mehmet Çetinkaya
|
2018
|
Turkey
|
28
|
28
|
22/6
|
48.71±9.66/
|
case-control design
|
III
|
arthroscopy
|
MRI
|
no-blinded
|
21/7
|
64.85±6.1
|
Siddhant K. Mehta
|
2020
|
USA
|
49
|
305
|
20/29
|
63.6±9.6/
|
cohort design
|
I
|
arthroscopy
|
MRI
|
no-blinded
|
131/174
|
62±9.1
|
Sizheng Zhu
|
2021
|
China
|
72
|
141
|
38/34
|
64.1±8.7/
|
case-control design
|
II
|
arthroscopy
|
CT
|
single-blinded
|
73/68
|
63.3±9.5
|
Sung-Hyun Yoon
|
2020
|
Korea
|
297
|
57
|
97/200
|
57.8±8.4/
|
case-control design
|
IV
|
arthroscopy
|
MRI
|
single-blinded
|
16/41
|
51.3±9.8
|
Wennan Xu
|
2022
|
China
|
184
|
276
|
126/58
|
62.55±9.03/
|
case-control design
|
III
|
arthroscopy
|
MRI
|
single-blinded
|
184/92
|
59.76±9.42
|
Experimental group: subscapularis tears; Control group: non-subscapularis
tears; E: experimental group; C: control group; F: female; M: male.
Qualitative assessment
Ten studies were included in this meta-analysis, including nine case-control
studies and one cohort study. All ten studies were evaluated by the NOS. Seven
studies received eight points, and three studies received seven points. The
quality of each study is shown in [Table 3].
Table 3 The Newcastle-Ottawa Scale(NOS)for risk of bias
assessment of cohort studies and case-control studies included in
the meta-analysis.
Study
|
Selection
|
Comparability
|
Outcome
|
Quality score
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
Adam C. Watson
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
Eduardo A. Malavolta
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
Joong-Bae Seo
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
7
|
Jun Kawamata
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
Mehmet Çetinkaya2016
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
Mehmet Çetinkaya2018
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
7
|
Siddhant K. Mehta
|
*
|
*
|
*
|
*
|
**
|
*
|
*
|
*
|
8
|
Sizheng Zhu
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
Sung-Hyun Yoon
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
7
|
Wennan Xu
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
*
|
8
|
* = score of 1; ** = score of 2; * = score of 0. Key to items: 1 = representativeness of exposed cohort; 2 = selection of nonexposed; 3 = ascertainment
of exposure; 4 = outcome not present at start; 5 = assessment of outcome; 6 = adequate follow-up length; 7 = adequacy of follow-up.
Assessment of publication bias
Publication bias was evaluated by Egger’s test. There was no publication
bias in any of the seven outcome measures (P>0.05) ([Table 4]).
Table 4 Assessment of publication bias.
Analyzed factor
|
Number of studies
|
Egger test
|
T
|
p value
|
Age in years
|
9
|
–0.92
|
0.389
|
CHD
|
7
|
–1.14
|
0.305
|
CO
|
3
|
–0.51
|
0.701
|
Female sex
|
9
|
1.19
|
0.273
|
Male sex
|
9
|
0.83
|
0.433
|
Dominant arm
|
4
|
1.19
|
0.355
|
LHB injury
|
3
|
–0.25
|
0.842
|
CHD: coracohumeral distance; CO: coracoid overlap; LHB: long head of the
biceps tendon.
Meta-analysis results
Age
Age was reported in nine studies [6]
[9]
[15]
[17]
[18]
[19]
[23]
[25]. There were 1 994 patients included, the
experimental group had 960 patients, and the control group had 1 034 patients.
There was moderate heterogeneity (I2=74%,
P=0.0004). Therefore, the random effects model was selected for
meta-analysis, and the results suggested that the effect size was significantly
larger in the experimental group than in the control group (WMD, 4.23
[95% CI, 2.32–6.15]; P<0.00001). This suggested that age
may be a risk factor for SSC tears: the older the patient was, the more likely
an SSC tendon injury would occur. The forest plot for age is shown in [Fig. 2].
Fig. 2 Meta-analysis forest plot for age. IV, inverse variance
methods.
Female sex
Female sex was evaluated in nine studies [6]
[9]
[15]
[17]
[18]
[19]
[23]
[24]
[25]. There were 2
050 patients included, with 988 patients in the experimental group and 1 062
patients in the control group. There was no heterogeneity
(I2=0%, P=0.81). Therefore, the fixed effects
model was selected for meta-analysis, and the results suggested that there were
no statistically significant differences in the effect size between the
experimental and control groups (RR, 1.02 [95% CI, 0.94–1.12];
P=0.58). This suggested that female sex may not be a risk factor for SSC
tears. The forest plot of age is shown in [Fig.
3].
Fig 3 Meta-analysis forest plot of Sex-female. M-H,
Mantel-Haenszel.
Male sex
Male sex was evaluated in nine studies [6]
[9]
[15]
[17]
[18]
[19]
[23]
[24]
[25]. There were 2
050 patients included, and the experimental group had 988 patients, while the
control group had 1 062 patients. There was no heterogeneity
(I2=0%, P=0.89). Therefore, the fixed effects
model was selected for meta-analysis, and the results suggested that there were
no statistically significant differences in effect size between the experimental
group and the control group (RR, 0.97 [95% CI, 0.88–1.07];
P=0.57). This suggested that male sex may not be a risk factor for SSC
tears. The forest plot for the male sex is shown in [Fig. 4].
Fig. 4 Meta-analysis forest plot of Sex-male. M-H,
Mantel-Haenszel.
Coracohumeral distance (CHD)
A total of 1 586 patients were included in seven studies [6]
[9]
[17]
[19]
[20]
[23]
[25]; the experimental group had 911 patients, and the control group
had 675 patients. There was moderate heterogeneity
(I2=55%, P=0.04). The results suggested that
the effect size was significantly lower in the experimental group than in the
control group (WMD, –1.03 [95% CI, –1.17 –
–0.88]; P<.00001). We reviewed all studies and found that five
studies used single blinding, and two studies did not. Therefore, we conducted a
subgroup analysis of this factor based on the presence or absence of blinding.
The results were as follows: there was low heterogeneity among the five
single-blinded studies (I2=34%, P=0.19); the
experimental group had 717 patients, and the control group had 574 patients. The
effect size was smaller in the experimental group than in the control group.
There was also low heterogeneity between the two studies without blinding
(I2=36%, P=0.21); the experimental group
had 194 patients, and the control group had 101 patients. The effect size was
smaller in the experimental group than in the control group. Therefore, the
overall effect size in the experimental group was smaller than that in the
control group in the fixed effects model meta-analysis. The forest plot for CHD
is shown in [Fig. 5].
Fig. 5 Meta-analysis forest plot for CHD. IV, inverse variance
methods.
Coracoid overlap (CO)
A total of 488 patients were included in three studies [9]
[23]
[24]; the experimental group had 241 patients, and the control group
had 247 patients. There was no heterogeneity (I2=0%,
P=0.48). Therefore, the fixed effects model was selected for
meta-analysis, and the results suggested that the effect size in the
experimental group was significantly larger than that in the control group (WMD,
1.98 [95% CI, 1.55–2.41]; P<.00001). This suggested that
CO may be a risk factor for SSC tears. The forest plot for CO is shown in [Fig. 6].
Fig. 6 Meta-analysis forest plot for CO. IV, inverse variance
methods.
Dominant arm
There were 1 092 patients included in the four studies [9]
[15]
[19]
[25]; the experimental group had 532
patients, and the control group had 560 patients. There was no heterogeneity
(I2=0%, P=0.77). Therefore, the fixed
effects model was selected for meta-analysis, and the results suggested that
there were no statistically significant differences between the effect size in
the experimental group and the effect size in the control group (RR, 1.05
[95% CI, 0.94–1.17]; P=0.38). This suggested that the
dominant arm may not be a risk factor for SSC tears. The forest plot for
dominant arm is shown in [Fig. 7].
Fig. 7 Meta-analysis forest plot of Dominant arm. M-H,
Mantel-Haenszel.
Injury of long head of the biceps (LHB) tendon
LHB injury was reported in three studies [6]
[15]
[17]. There were
907 patients included, with 283 patients in the experimental group and 624
patients in the control group. There was no heterogeneity
(I2=0%, P=0.47). Therefore, the fixed effects
model was selected for meta-analysis, and the results suggested that the effect
size of the experimental group was larger than that of the control group, and it
was statistically significant (RR, 4.98 [95% CI, 3.75–6.61];
P<.00001). This suggested that LHB injury may be a risk factor for SSC
tears. The forest plot of LHB injury is shown in [Fig.
8].
Fig. 8 Meta-analysis forest plot of LHB injury. M-H,
Mantel-Haenszel.
Discussion
Rotator cuff tears are common, significantly reduce people’s quality of life,
and increase the economic burden on people, which increases the pressure on the
social medical insurance system [26]. Rotator cuff
tears are most common in the supraspinatus muscle, followed by the SSC muscle [27]. Many studies have shown that the incidence of SSC
tears is 27.4%–69.1% in rotator cuff tear cases repaired
under arthroscopy [28]
[29]
[30]
[31].
Studies have shown that most SSC tears are associated with degenerative changes
[15]. The diagnosis of SSC tears is still
difficult and there is a risk of missed diagnosis. At present, MRI remains the gold
standard for the preoperative diagnosis of rotator cuff tears, and many researchers
have been committed to finding anatomic risk factors for SSC tears on MRI in an
attempt to increase the sensitivity of the diagnosis of SSC tears by measuring these
anatomical indicators on MRI.
Many studies have shown that the incidence of rotator cuff tears increases with age
[32]
[33]. In older
adults, the amount of microvasculature in the tendon is significantly reduced,
making rotator cuff tissues more prone to fibroangiogenesis, adipose deposition,
atrophy, and calcification, which may contribute to rotator cuff tears [32]
[34]. Studies have
shown that the incidence of rotator cuff tears in patients over 60 years old is 5.07
times higher than in patients under 60 years old [35].
In this study, patients with SSC tears were older than those without SSC tears.
However, there was heterogeneity in the studies included with this indicator
(I2=74%, P<.0001). This may have been related
to the patient’s country, economic status, study design and other factors,
so the random effects model was selected for meta-analysis.
A systematic study showed that males were more prone to supraspinatus tears than
females. Maria J. Leite et al. suggested that males were more prone to SSC injuries
[13]. However, there were no significant
differences in CHD and CO between men and women. Previous studies have shown no
relationship between sex and SSC tears [15]
[36]. In our study, there was no significant difference
in the probability of SSC tears between men and women.
The CHD is a measurement of the shortest distance from the coracoid process cortex to
the humeral cortex, and there are transverse positions and oblique sagittal
positions. Relevant studies have shown that the normal value of the CHD on MRI is
8.7–11 mm [23]
[37]
[38]. Leite et al., suggested that the
optimal sensitivity and specificity for predicting SSC tears was a CHD of
7.95 mm [13]. Xu et al., and Seo et al. showed
that a decreased CHD was closely related to SSC tears and had high predictive value
and diagnostic sensitivity [17]
[25]. According to Zhu et al., there was a significant
difference between affected and contralateral CHD in patients with SSC tears, and
the bilateral discrepancy (ΔCHD) was closely related to SSC tears and
subcoracoid impingement [9]. Other researchers believe
that CHD is not significantly associated with SSC tears [23]
[39]. There are many factors that impact
the measurement of the CHD. For example, there is a difference between the
measurement value when in the neutral position and the internal rotation position of
the upper limb. Some studies have suggested that CHD values should be measured when
the upper limb is in a neutral position [9]
[17]
[19]. Some studies
have suggested that the value of the CHD should be measured when the upper limb is
in an internal rotation position [8]. In addition,
magnetic field strength, scan thickness, and other factors related to the MRI
procedure will also affect the CHD measurement, thus affecting the diagnosis of SSC
tears [3]
[5]
[6]. In our study, there were seven studies related to
CHD, which were all measured in transverse positions with the patient’s
upper limb in a standard neutral position. There was heterogeneity among the seven
studies (I2=55%, P<.00001). We performed a
subgroup analysis depending on whether blinding procedures were used, and this
significantly reduced heterogeneity. We concluded that the CHD was significantly
associated with SSC tears: in particular, the lower the CHD was, the more likely an
SSC tear.
The distance between the glenoid and the tip of the coracoid process was defined as
the CO, which was measured on the axial plane. Leite et al. showed that the CO had a
strong predictive value for SSC tears, and when the CO value was 16.6 mm,
the sensitivity and specificity for the prediction of SSC muscle tears reached the
optimal value [13]. However, Zhu et al. found that a
CO value of 10 mm had the most appropriate sensitivity and specificity for
the prediction of SSC tears, and the predictive value was higher than that of CHD
[9]. Cetinkaya et al. identified the CO as the
most valuable predictor of SSC tears [23]. In our
study, there were three studies related to CO, which were all measured in transverse
positions, and the upper limbs were in the standard neutral position. There was no
heterogeneity among the three studies. We concluded that CO was significantly
associated with SSC tears: in particular, the higher the CO was, the more likely SSC
tears were to occur.
The dominant arm, usually the right one, is the side that is preferred for most
tasks. For example, most people use the right arm, so the right arm is the dominant
arm. Relevant studies have shown that rotator cuff tears of the dominant arm are
more likely to have symptoms [40]. In a study of 20
overhead athletes with no shoulder symptoms, Connor et al. found that 40% of
dominant arms had partial or full-thickness rotator cuff tears [41]. Some studies suggest that rotator cuff tears are
more likely to occur in the dominant arm [42];
however, other studies have found that there is no significant difference in the
chance of supraspinatus tears between the dominant arm and the nondominant arm [27]. Our study included four studies involving the
dominant arm, and there was no heterogeneity among these four studies. We concluded
that there was no significant difference in the dominant arm with regard to SSC
tears.
The LHB originates from supraglenoid tubercle of the scapula and passes through a
skeletal fiber canal formed by the intertubercular sulcus of the humerus and the
transverse ligament. It has anatomical proximity to the SSC and supraspinatus
tendons. The stability of the LHB in the groove is conferred by the sling-like
confluence of fibrous tissue originating from the coracohumeral ligament, superior
glenohumeral ligament, articular capsule, supraspinal muscle, and SSC tendons [43]
[44]. Upper SSC tears
usually cause LHB instability and shoulder pain [45]
[46]. Subluxation and dislocation of the
LHB can cause damage to the SSC muscle [15]
[16]. Studies have found that when the CHD is
7.7 mm and the CO is 18.9 mm, the prediction of LHB injury has good
sensitivity and specificity [13]
[17]. Our study included three studies involving LHB
injury, and there was no heterogeneity among these three studies. We concluded that
LHB injury was significantly associated with SSC tears.
Certainly, there are other risk factors related to SSC tears, such as coracoid distal
length (CLD), coracoid proximal length (CLP), and coracoid angle (CA) [18]
[36]. However, there
are few studies evaluating these indicators. The different study designs, different
evaluation methods, different imaging machine types, different measurement methods,
and other factors caused differences in the measured results for these indicators
among studies. Therefore, the combined meta-analysis could not be carried out, and
the forced merging of data would have produced greater heterogeneity and lost
analytical significance. Therefore, it is expected that an increasing number of
high-quality studies will involve these indicators, which will be evaluated in a
subsequent systematic review.
There are also some limitations in this study: (1) the included studies were
conducted in different social and economic environments, different medical systems,
different countries, and different ethnic groups, which may have led to
heterogeneity among some outcome indicators. However, the results of these
systematic reviews of studies from different countries and ethnic groups have a
universality that might be considered beneficial. (2) This study considered only SSC
tears, not the size or other features of the tears. (3) The number of included
studies for some indicators was small, which may have affected the credibility of
the results. Future studies should include studies with larger sample sizes. (4) The
included studies lacked high-quality research evidence, which may have affected the
credibility of this study. The shortcomings of this study provide an important
direction for future research.
Conclusion
Our study suggested that age, CHD, LHB injury, and CO can be used as predictors of
SSC tears. It is helpful for surgeons to detect SSC tears and implement intervention
measures in the early stages.