Knee Surgery, Sports Traumatology, Arthroscopy最新文献

Purpose

While traditional high tibial osteotomy (HTO) techniques primarily address malalignment in the coronal plane, the significance of sagittal plane alignment, particularly the posterior tibial slope (PTS), is not to be overlooked in the setting of cruciate ligament insufficiency. Combined deformities involving both the coronal plane and the sagittal plane are less common and present unique surgical challenges. This narrative review summarizes the literature and introduces tips and tricks for managing complex biplanar deformities through a case-based discussion of different techniques.

Methods

This narrative review includes preoperative planning, surgical techniques, clinical outcomes and illustrative clinical cases detailing surgical rationale and technical nuances in the correction of biplanar proximal tibial deformities. Emphasis is placed on the importance of accurate assessment and correction of biplanar deformities to optimize patient outcomes. Four representative technique presentations are included: (1) Hybrid HTO with a posterior opening wedge (POW) and anterior closing wedge (ACW), (2) asymmetrical medial closing wedge (MCW) HTO, (3) medial opening wedge (MOW) HTO with an anterolateral hinge and (4) a double HTO with both an infratuberosity ACW and high MOW.

Conclusion

Biplanar HTO is a knee-preserving surgical option for a small cohort of patients with complex knee deformities involving both the coronal and sagittal planes. Precise preoperative planning and meticulous surgical execution are essential to address these biplanar malalignments effectively. This narrative review serves as a guide for orthopaedic surgeons, highlighting key considerations when planning biplanar HTO and serves as a practical guide for complex cases.

Level of Evidence

Level V.

Purpose

Soft tissue and degenerative knee injuries, including anterior/posterior cruciate ligament (ACL/PCL) injuries, medial knee osteoarthritis (MKOA), and patellofemoral osteoarthritis (PFOA), are common causes of pain and functional decline. Knee bracing is often used as part of non-operative treatment, but its clinical effectiveness remains uncertain. This systematic review aimed to evaluate the role of bracing in improving pain, function, and preventing surgical conversion in adults with non-operatively managed knee injuries.

Methods

A systematic review was conducted following PRISMA guidelines. Embase, Ovid MEDLINE, and Ovid Emcare were searched from inception to March 2025. Studies were included if they reported clinical outcomes of knee bracing in adults with soft tissue or degenerative knee injuries treated non-operatively. Studies focused on biomechanics, prophylaxis, surgery, or paediatric populations were excluded. Data were synthesised narratively with weighted summary statistics.

Results

Seventeen studies (706 patients) were included: six on ACL injuries, three PCL, three MKOA, and five PFOA. MKOA studies showed pooled improvements of +14.6 in KOOS Pain and −1.9 in VAS (SDs 1.0 and 0.5). ACL studies reported a pooled Lysholm gain of +11.8 (SD = 4.3), while PCL outcomes showed large single-study improvements (KOOS Pain +31.0, IKDC + 30.0). WOMAC scores improved in MKOA (−13.7) but declined in PFOA (−6.4). Failure (surgical conversion) was reported in 13 studies, with the highest rates in PCL (16.4%) and ACL (10.6%), and the lowest in MKOA (0%) and PFOA (4.0%). Complications were infrequently reported; skin irritation was the most common adverse event.

Conclusion

Knee bracing demonstrated the most consistent pain and functional improvements in degenerative and PCL injuries. In contrast, outcomes in ACL injuries were more variable and associated with higher failure rates, underscoring the need for injury-specific bracing strategies. These findings emphasise the importance of patient selection, brace design, and early intervention, and support the need for higher-quality studies to guide non-operative management strategies.

Level of Evidence

Level IV.

Purpose

Medial open wedge high tibial osteotomy (HTO) can delay knee arthroplasty (KA) in patients with medial compartment varus knee osteoarthritis (OA). However, prospectively collected long-term outcomes and survival rates are limited. The purpose of this study was to assess the survival rate and the outcome following HTO.

Methods

In this prospective cohort study with initially 120 knees from 112 patients treated from 2008 to 2011 with an HTO, 95 knees from 88 patients (age: 47.0 ± 7.6 years; female: n = 28) were followed-up. The minimum follow-up was 12 years or an earlier conversion to KA. The 5-, 10- and 12-year survival rates were calculated. The Lysholm and IKDC scores were assessed preoperatively and 1.5, 6 and 12 years postoperatively.

Results

At the last follow-up (12.9 ± 0.8,12.0–15.1 years), 67.4% (n = 64) had no conversion to KA. 31 knees (32.6%; 2 unicompartmental KA, 29 total KA) were converted to a KA on average 7.3 ± 3.3 (1.5–13.0) years after the HTO. The 5-, 10- and 12-year survival rates were 88.2%, 76.3% and 69.7%. Knees without conversion to KA had significantly higher scores at the last follow-up compared to preoperatively: The Lysholm score increased from 60.4 ± 21.1 (14.0–91.0) preoperatively to 89.1 ± 12.5 (39.0–100.0), 86.5 ± 13.8 (39.0–100.0) and 82.6 ± 18.3 (30.0–100.0). The IKDC score also increased from 51.8 ± 16.6 (15.0–93.0) preoperatively to 77.7 ± 14.8 (21.0–100.0), 70.9 ± 15.3 (26.0–98.0) and 72.5 ± 18.1 (14.0–95.0) at the corresponding postoperative time points 1.5, 6 and 12-years.

Conclusion

HTO to treat varus medial OA showed good long-term outcomes. Most patients can expect no conversion to KA for more than twelve years and a higher subjective knee function than preoperatively.

Level of Evidence

Level IV.

Purpose

The aim of this study was to describe the anterior cruciate ligament (ACL) injury incidence differences depending on sex and age-related categories in a large cohort of soccer players over six consecutive seasons.

Methods

This study was designed as a retrospective descriptive epidemiological study. All soccer players in a specific geographical area who sustained an ACL injury across six consecutive seasons were included in this analysis. ACL injury incidence was calculated by sex and age-related category as a percentage of all registered soccer players in the region. Additionally, soccer participation evolution, ACL injury incidence evolution, and monthly ACL injury distribution were studied. Group, seasonal, and monthly differences were analysed using chi-square tests.

Results

Between the 2016–2017 and the 2021–2022 seasons, 3381 ACL injuries were registered from a total of 782,856 player-seasons. ACL injury incidence was 0.43%. Female soccer players showed 2.79 times higher injury incidence than male players, with overall rates of 1.06% in females and 0.38% in males (p < 0.001). However, female players only showed higher ACL injury incidence than males in age groups older than 14 years. ACL injury incidence increased over the six seasons studied only in the male group. October and January were the months with the highest number of ACL injuries, with no significant differences in monthly distribution between sexes.

Conclusions

Female soccer players showed higher ACL injury incidence than males, particularly in age groups older than 14 years. October and January were identified as the months with the highest injury incidences regardless of sex. Additionally, a rising injury incidence was observed in male players, a trend not seen in females.

Level of Evidence

Level III, retrospective comparative study.

Purpose

To systematically review the current literature regarding the role of artificial intelligence and machine learning in predicting and optimising clinical outcomes following hip arthroscopy.

Methods

A systematic review of the PubMed, Cochrane, and EMBASE databases was completed in December 2024. Studies were included if they assessed the application of AI/ML to clinical outcomes of hip arthroscopy. Exclusion criteria were imaging-only studies, non-English publications, conference abstracts, review articles and meta-analyses. Extracted data included study characteristics, input features, algorithm types, sample sizes, and model performance. Descriptive statistical analysis was performed due to data heterogeneity.

Results

Sixteen studies met inclusion criteria, covering applications across prediction of intraoperative findings (n = 1), prediction of post-operative outcomes (n = 5), prediction of patient-reported outcomes (n = 7) and prediction of revision (n = 3). Input features commonly utilised included demographics, imaging data, preoperative patient-reported outcomes (PROs), and comorbidities. Supervised learning models were the most widely applied, including logistic regression, random forests, support vector machines (SVMs), and artificial neural networks (ANNs). Performance metrics demonstrated robust predictive ability, with AUC values ranging from 0.66 to 0.94 and accuracy rates exceeding 75% in most studies. Applications included predicting revision surgery risk, prolonged opioid use, postoperative satisfaction, and time to return to sport. Imaging-based algorithms, particularly leveraging MRI data, showed promise for surgical planning and diagnostic precision.

Conclusions

AI and ML show significant promise in enhancing outcome prediction and patient stratification in hip arthroscopy. Future research should prioritise the standardisation of datasets, external validation, and interpretability to facilitate clinical translation.

Level of Evidence

Level V.

Purpose

This analysis evaluated whether logistic regression and machine learning models could predict achievement of the minimal clinically important difference (MCID) for the International Hip Outcome Tool (iHOT-12) and Hip Outcome Score (HOS) at 6 and 12 months following hip arthroscopy.

Methods

Data from the multicenter Femoroacetabular Impingement RandomiSed controlled Trial and its embedded prospective cohort were used. A total of 309 patients (mean ± SD age 34.0 ± 8.7 years, 37.7% female) were included. The MCID thresholds for iHOT-12 and HOS were calculated using a distribution-based method and were 9.0 and 13.0, respectively. Predictive models were trained with demographic, radiographic, and intraoperative variables using a 70:30 training-to-test data split. MCID achievement was defined as a change from preoperative to postoperative scores that surpassed the calculated threshold. Model discrimination was assessed using the area under the curve (AUC), and calibration was evaluated via slope, intercept, and Brier scores.

Results

Achievement rates were 83.3% at 6 months and 81.1% at 12 months for iHOT-12, and 64.3% at 6 months and 75% at 12 months for HOS. Logistic regression performed best at 12 months (AUC = 0.724) for iHOT-12 with poor calibration (slope = 2.19). AUCs for HOS ranged between 0.672–0.715 at 6 months and 0.665–0.699 at 12 months. Best calibration was achieved by Least Absolute Shrinkage and Selection Operator (slope = 1.270, intercept = –0.177) at 6 months and by logistic regression at 12 months (slope = 1.093, intercept = –0.079). Lower baseline patient-reported outcome measures (PROMs) were associated with MCID achievement in most models.

Conclusion

The most robust predictor of MCID achievement for both PROMs were lower baseline scores, and can be used as a prognostic variable for preoperative counselling. Model performance for predicting MCID was superior for HOS relative to iHOT-12. Machine learning models generally had comparable discrimination and calibration scores to traditional logistic regression models.

Level of Evidence

Level III.

Kinematic alignment is increasingly adopted in total knee arthroplasty (TKA) as a patient-specific strategy to restore native joint anatomy. However, its reliance on static radiographic measurements may not adequately reflect real-world functional biomechanics. This editorial underscores the importance of complementing static assessment with kinetic principles. This emerging concept, referred to as kinetic alignment, integrates dynamic parameters such as the dynamic hip-knee-ankle angle, knee adduction moment, ground reaction forces, and muscle forces to better characterise in vivo joint loading. These kinetic variables provide critical insight into joint loading during real-life activities and may offer greater predictive value for implant performance and patient satisfaction. Continued emphasis on static alignment targets may overlook key elements of in vivo knee function. Incorporating kinetic data into preoperative planning could support more tailored surgical decisions, helping to mitigate risks related to malalignment, overloading, and suboptimal outcomes. The editorial advocates for expanding the concept of alignment beyond static geometry, including both motion and load, and encouraging the orthopaedic and biomechanical communities to adopt a more functional and individualised perspective in TKA planning.