Multivariate Analysis of the Risk Factors for First-Time Noncontact ACL Injury in High School and College Athletes: A Prospective Cohort Study With a Nested, Matched Case-Control Analysis.

BACKGROUND: Multivariate analysis that identifies the combination of risk factors associated with anterior cruciate ligament (ACL) trauma is important because it provides insight into whether a variable has a direct causal effect on risk or an indirect effect that is mediated by other variables. It can also reveal risk factors that might not be evident in univariate analyses; if a variable's effect is moderated by other variables, its association with risk may be apparent only after adjustment for the other variables. Most important, multivariate analyses can identify combinations of risk factors that are more predictive of risk than individual risk factors. HYPOTHESIS: A diverse combination of risk factors predispose athletes to first-time noncontact ACL injury, and these relationships are different for male and female athletes. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Athletes competing in organized sports at the high school and college levels participated in this study. Data from injured subjects (109 suffering an ACL injury) and matched controls (227 subjects) from the same athletic team were analyzed with multivariate conditional logistic regression to examine the effects of combinations of variables (demographic characteristics, joint laxity, lower extremity alignment, strength, and personality traits) on the risk of suffering their first ACL injury and to construct risk models. RESULTS: For male athletes, increases in anterior-posterior displacement of the tibia relative to the femur (knee laxity), posterior knee stiffness, navicular drop, and a decrease in standing quadriceps angle were jointly predictive of suffering an ACL injury. For female athletes the combined effects of having a parent who had suffered an ACL injury and increases in anterior-posterior knee laxity and body mass index were predictive of ACL injury. CONCLUSION: Multivariate models provided more information about ACL injury risk than individual risk factors. Both male and female risk models included increased anterior-posterior knee laxity as a predictor of ACL injury but were otherwise dissimilar.

Evaluation of an Algorithm to Predict Menstrual-Cycle Phase at the Time of Injury.

CONTEXT: Women are 2 to 8 times more likely to sustain an anterior cruciate ligament (ACL) injury than men, and previous studies indicated an increased risk for injury during the preovulatory phase of the menstrual cycle (MC). However, investigations of risk rely on retrospective classification of MC phase, and no tools for this have been validated. OBJECTIVE: To evaluate the accuracy of an algorithm for retrospectively classifying MC phase at the time of a mock injury based on MC history and salivary progesterone (P4) concentration. DESIGN: Descriptive laboratory study. SETTING: Research laboratory. PARTICIPANTS: Thirty-one healthy female collegiate athletes (age range, 18-24 years) provided serum or saliva (or both) samples at 8 visits over 1 complete MC. MAIN OUTCOME MEASURE(S): Self-reported MC information was obtained on a randomized date (1-45 days) after mock injury, which is the typical timeframe in which researchers have access to ACL-injured study participants. The MC phase was classified using the algorithm as applied in a stand-alone computational fashion and also by 4 clinical experts using the algorithm and additional subjective hormonal history information to help inform their decision. To assess algorithm accuracy, phase classifications were compared with the actual MC phase at the time of mock injury (ascertained using urinary luteinizing hormone tests and serial serum P4 samples). Clinical expert and computed classifications were compared using κ statistics. RESULTS: Fourteen participants (45%) experienced anovulatory cycles. The algorithm correctly classified MC phase for 23 participants (74%): 22 (76%) of 29 who were preovulatory/anovulatory and 1 (50%) of 2 who were postovulatory. Agreement between expert and algorithm classifications ranged from 80.6% (κ = 0.50) to 93% (κ = 0.83). Classifications based on same-day saliva sample and optimal P4 threshold were the same as those based on MC history alone (87.1% correct). Algorithm accuracy varied during the MC but at no time were both sensitivity and specificity levels acceptable. CONCLUSIONS: These findings raise concerns about the accuracy of previous retrospective MC-phase classification systems, particularly in a population with a high occurrence of anovulatory cycles.