Better Safe Than Sorry? A Systematic Review with Meta-analysis on Time to Return to Sport After ACL Reconstruction as a Risk Factor for Second ACL Injury

Injury prevention in Brazilian women's football: Perceptions of physiotherapists and practices within elite clubs

OBJECTIVES

To describe the perceptions of physiotherapists and the injury prevention practices implemented within elite women's football clubs in Brazil.

DESIGN

Cross-sectional study.

SETTING

Online survey.

PARTICIPANTS

Head physiotherapists from 32 Brazilian elite clubs.

MAIN OUTCOME MEASURES

Structured questionnaire.

RESULTS

Physiotherapists identified ACL rupture as the primary target for prevention. The top-five perceived injury risk factors included 'early return to sport after injury', 'workload too high', 'previous injury', 'poor sleep/rest', and 'muscle strength/power deficit'. 'Adoption of return to sport criteria' was almost unanimously recognized as a very important preventive strategy. 'Poor infrastructure' was elected as the main barrier to implementing prevention programs. From a practical standpoint, at least two-third of clubs implemented multi-component exercise interventions for injury prevention. These interventions typically encompassed flexibility/mobility, balance/proprioception, lumbo-pelvic stability, and agility exercises, alongside exposure to sprinting. Strength training routines typically included traditional, functional, and eccentric exercises. Most teams also employed other prevention strategies, including adoption of return to sport criteria, internal workload monitoring, post-exercise recovery modalities, preseason risk factor screening, and application of rigid strapping tapes.

CONCLUSIONS

This study provided unprecedented insights into the physiotherapists' perceptions and injury prevention practices implemented within elite women's football clubs.

A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries

PURPOSE

The purpose of this study was to develop a neural network model for predicting second anterior cruciate ligament (ACL) injury risk following ACL reconstruction using patient features from medical records.

METHODS

Of 486 consecutive patients who underwent primary unilateral ACL reconstruction, 386 patients (198 women, 188 men) with a mean age of 25.1 ± 11.6 years were included in this study. Fifty-eight features, including demographic data, surgical, preoperative and postoperative data, were retrospectively collected from medical records, and features with an incidence of less than 5% were excluded. Finally, 14 features were used for the analysis. The multilayer perceptron was composed of four hidden layers with a rectified linear unit as activation and was trained to maximise the area under the receiver-operating characteristic curve (auROC). Subsequently, validation was carried out through a rigorous threefold cross-validation process. To ascertain the most efficacious combination of features with the highest auROC, a single feature with the least impact on auROC maximisation was systematically eliminated from the comprehensive variable set, ultimately resulting in the retention of a mere two variables.

RESULTS

The median follow-up period was 50.5 (24-142) months. Fifty-seven knees had a second ACL injury, with a graft rupture rate of 7.7% and a contralateral injury rate of 6.9%. The maximum auROC for predicting graft rupture was 0.81 with two features: young age and hamstring graft. Meanwhile, the maximum auROC for predicting contralateral ACL injury was 0.74 with seven features, including young age, presence of medial meniscus tear, small body mass index, hamstring graft, female sex and medial meniscus repair or treatment.

CONCLUSION

A neural network model with patient features from medical records detected graft ruptures and contralateral ACL injuries with acceptable accuracy. This model can serve as a new, useful tool in clinical practice to inform decisions about ACL reconstruction and retuning to sports postoperatively.

LEVEL OF EVIDENCE

Level IV.