| Literature DB >> 27928547 |
Alana Waiwaiole1, Ajay Gurbani1, Kambiz Motamedi1, Leanne Seeger1, Myung Shin Sim2, Patricia Nwajuaku1, Sharon L Hame1.
Abstract
BACKGROUND: Posterior tibial slope (PTS) has been proposed as a potential risk factor for anterior cruciate ligament (ACL) injury; however, studies that have examined this relationship have provided inconclusive and sometimes contradictory results. Further characterization of this relationship may enable the medical community to identify individuals at greater risk for ACL injury and possibly characterize an anatomic target during surgical reconstruction.Entities:
Keywords: ACL injury; age; posterior tibial slope; race; sex
Year: 2016 PMID: 27928547 PMCID: PMC5131735 DOI: 10.1177/2325967116672852
Source DB: PubMed Journal: Orthop J Sports Med ISSN: 2325-9671
Figure 1.Medial plateau posterior tibial slope (PTS) measurement. L, magnetic resonance image (MRI) longitudinal axis of tibia; O, line orthogonal to MRI longitudinal axis of tibia; T, line tangent to PTS.
Figure 2.Lateral plateau posterior tibial slope (PTS) measurement. L, magnetic resonance image (MRI) longitudinal axis of tibia; O, line orthogonal to MRI longitudinal axis of tibia; T, line tangent to PTS.
Demographics of ACL-Injured and Control Groups
| ACL-Intact (n = 109) | ACL-Deficient (n = 105) |
| |
|---|---|---|---|
| Age, y, mean ± SD | 36 ± 14 | 27 ± 9 | <.001 |
| Male sex, % | 40 | 50 | .138 |
| Race, % | .002 | ||
| Black | 13 | 4 | |
| Asian | 4 | 15 | |
| White | 58 | 48 | |
| Other | 6 | 13 | |
| Unknown | 19 | 20 |
ACL, anterior cruciate ligament.
Figure 3.Mean tibial slope. ACL, anterior cruciate ligament; PTS, posterior tibial slope.
Univariable Logistic Regression Analysis
| Parameter | Comparison |
| OR (95% CI) | AUC | AIC |
|---|---|---|---|---|---|
| Age, y | As 1 unit goes up | 0.0298 | 0.991 (0.983-0.999) | 0.689 | 293.852 |
| Lateral PTS, deg | As 1 unit goes up | 0.0658 | 1.036 (0.998-1.076) | 0.662 | 295.218 |
| Medial PTS, deg | As 1 unit goes up | 0.1357 | 1.031 (0.991-1.073) | 0.633 | 296.415 |
| Race | Black vs white | 0.0023 | 0.299 (0.104-0.859) | 0.636 | 287.558 |
| Asian vs white | 0.0072 | 4.301 (1.594-11.602) | 0.636 | 287.558 | |
| Other vs white | 0.1613 | 2.267 (0.918-5.596) | 0.636 | 287.558 | |
| Unknown vs white | 0.8434 | 1.215 (0.603-2.448) | 0.636 | 287.558 | |
| Side | Right vs left | 0.2793 | 0.807 (0.547-1.19) | 0.545 | 296.104 |
| Sex | Male vs female | 0.3565 | 1.209 (0.807-1.811) | 0.55 | 297.813 |
AIC, Akaike information criterion; AUC, area under the curve; OR, odds ratio.
Type 3, P = .006.
Predictors of ACL injury: Initial Multivariable Model
| Variable | Odds Ratio |
|
|---|---|---|
| Age | 0.96 | <.001 |
| Sex (male vs female) | 1.88 | .046 |
| Laterality (right vs left) | 0.77 | .406 |
| Race | ||
| Black vs white | 0.37 | .119 |
| Asian vs white | 2.80 | .096 |
| Other vs white | 2.67 | .060 |
| Unknown vs white | 1.21 | .622 |
| Posterior tibial slope | ||
| Medial | 1.05 | .372 |
| Lateral | 1.13 | .012 |
Predictors of ACL injury: Final Multivariable Model
| Variable | Odds Ratio |
|
|---|---|---|
| Age | 0.94 | <.001 |
| Lateral posterior tibial slope | 1.12 | .002 |