| Literature DB >> 33110924 |
Ashleigh R Elkins1, Patrick H Lam1, George A C Murrell1.
Abstract
BACKGROUND: Arthroscopic rotator cuff repair can be quite complex and time consuming, particularly early in the surgeon's learning curve. HYPOTHESIS: Patients who have undergone rotator cuff repair with shorter operative times will be less likely to have a rotator cuff retear at 6 months postoperatively. STUDYEntities:
Keywords: diagnostic ultrasound; general sports trauma; imaging; rotator cuff; shoulder
Year: 2020 PMID: 33110924 PMCID: PMC7557713 DOI: 10.1177/2325967120954341
Source DB: PubMed Journal: Orthop J Sports Med ISSN: 2325-9671
Patient Characteristics
| Intact (n = 1389) | Retear (n = 211) |
| |
|---|---|---|---|
| Age, y | 58 ± 0.3 (18-91) | 65 ± 0.8 (15-88) | <.0001 |
| Sex, male:female, n | 750:639 | 135:76 | .0074 |
| Tear thickness, partial:full, n | 641:748 | 29:182 | <.0001 |
| Tear size, cm2 | 2.9 ± 1.0 (0.4-5.6) | 7.5 ± 5.7 (2.5-6.4) | <.0001 |
| Operative time, min | 21 ± 0.3 (4-110) | 28 ± 0.4 (5-106) | <.0001 |
| No. of anchors, mean (range) | 2.0 (0-6) | 2.7 (1-6) | <.0001 |
Data are reported as mean ± SEM (range) unless otherwise indicated.
Figure 1.Percentage of patients with intact and retorn rotator cuffs at 6 months postoperatively for each of the tear size categories.
Figure 2.Mean operative time for each of the tear size categories. Error bars indicate SEMs.
Figure 3.Moving average graph for operative time and rotator cuff retears (%), with an increment of 160 patients in a total of 1600 patients.
Figure 4.Moving average analysis for operative time and case number, with an increment of 160 patients in a total of 1600 patients.
Factors With No Significant Independent Effect on Retears on Multiple Logistic Regression Analysis
| Wald Statistic |
| |
|---|---|---|
| Operative time | 1.30 | .25 |
| Tissue quality (surgeon ranked) | 0.32 | .57 |
| Patient sex | 2.62 | .11 |
| Workers’ compensation status | 0.72 | .40 |
| No. of anchors | 0.05 | .82 |
| Tendon mobility (surgeon ranked) | 2.39 | .12 |
| Repair type (undersurface vs bursal sided) | 1.63 | .20 |
Independent Predictors of a Retear on Multiple Logistic Regression Analysis
| Wald Statistic |
| |
|---|---|---|
| Tear size | 36 | <.001 |
| Case number | 28 | <.001 |
| Patient age | 23 | <.001 |
| Tear thickness | 13 | <.001 |
| Repair quality (surgeon ranked) | 8 | .004 |
Figure 5.Moving average for case number and retears, with an increment of 160 patients in a total of 1600 patients.
Factors Predictive of Operative Time on Multiple Linear Regression Analysis
|
| Direction |
| |
|---|---|---|---|
| Case number | 12 | Negative | <.001 |
| Undersurface repair | 10 | Negative | <.001 |
| No. of anchors | 5 | Positive | <.001 |
| Repair quality (surgeon ranked) | 5 | Negative | <.001 |
| Tear size | 3 | Positive | <.001 |