What Factors Are Associated with Symptomatic Rotator Cuff Tears: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tears are common. A previous systematic review reported on factors associated with rotator cuff tears; however, it included relatively few studies and few variables, and in addition, it had considerable heterogeneity. To identify the factors associated with symptomatic rotator cuff tears and to help guide clinicians to potentially modifiable factors, we felt a broader and more inclusive meta-analysis would be useful. QUESTIONS/PURPOSES: In this systematic review and meta-analysis, we asked what (1) demographic, (2) disease, and (3) imaging factors are associated with symptomatic rotator cuff tears? METHODS: PubMed, Embase, and Web of Science were searched, and the search period were from the inception of each database through February 2021. The keywords included "risk factor," "rotator cuff injury," "rotator cuff tears," and "rotator cuff tendinitis." All comparative studies on symptomatic rotator cuff tears were included. We considered that the diagnosis of rotator cuff tear could be made by any imaging tool (MRI or ultrasound). We considered either partial- or full-thickness tears to be a rotator cuff tear. No language restrictions were applied. Twenty-six articles from 14 countries involving 9809 individuals, consisting of 3164 patients and 6645 controls, were included. The Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality (AHRQ) scale were used to evaluate the risk of bias of the included studies, and the highest scores were 9 and 11, respectively. The Newcastle-Ottawa Scale was used for retrospective comparative studies, and the AHRQ was used to evaluate prospective comparative studies. The eight retrospective comparative studies we included were scored from 4 to 9. The quality score of the 18 prospective comparative studies ranged from 6 to 9. Publication bias was explored using the Egger test. Heterogeneity was estimated using the I2 value. If there was no heterogeneity (I2 ≤ 50%), a fixed-effects model was used to determine the overall effect size; if there was heterogeneity (I2 > 50%), a random-effects model was used to merge the effect values. A meta-analysis was performed with RevMan 5.3, and the risk ratio (RR) and weighted mean difference of related factors were calculated. RESULTS: Our meta-analysis identified the following demographic factors associated with an increased risk of rotator cuff tears: older age (mean difference 3.1 [95% CI 1.4 to 4.8]; p < 0.001), greater BMI (mean difference 0.77 [95% CI 0.37 to 1.17]; p < 0.001), smoking (RR 1.32 [95% CI 1.17 to 1.49]; p < 0.001), dominant arm (RR 1.15 [95% CI 1.06 to 1.24]; p < 0.001), greater height (mean difference 0.9 [95% CI 0.4 to 1.4]; p < 0.001), and heavier weight (mean difference 2.24 [95% CI 0.82 to 3.66]; p = 0.002). Regarding disease factors, we found that traumatic events (RR 1.91 [95% CI 1.40 to 2.54]; p < 0.001) and hypertension (RR 1.50 [95% CI 1.32 to 1.70]; p < 0.001) were associated with symptomatic rotator cuff tears. Regarding imaging factors, we found that the following three factors were associated with symptomatic rotator cuff tears: greater acromion index (mean difference 0.11 [95% CI 0.06 to 0.16]; p < 0.001), greater critical shoulder angle (mean difference 1.9 [95% CI 1.5 to 2.3]; p < 0.001), and smaller glenoid version angle (mean difference -1.3 [95% CI -1.9 to -0.8]; p < 0.001). We found no association between the patient's sex or the presence or absence of thyroid disease and the likelihood of a rotator cuff tear being present. CONCLUSION: This study identified several factors associated with symptomatic rotator cuff tears, including blood glucose, blood pressure, weight, and smoking. Clinicians may seek to modify these factors, possibly in patients with symptomatic rotator cuff tears, but also in symptomatic patients who have not yet been diagnosed with rotator cuff tears because there would be no harm or risk associated with modifying any of the factors we identified. Future research should further study whether addressing these factors can delay the progression and size of rotator cuff tears.Level of Evidence Level III, prognostic study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.068 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".