Low grade of osteoarthritis development after Latarjet procedure with a minimum 5 years of follow-up: a systematic review and pooled analysis

PURPOSE

The purpose of this systematic review and pooled analysis was to evaluate incidence and risk factors for glenohumeral osteoarthritis (OA) in patients who underwent Latarjet procedure with a minimum of 5-year follow-up.

METHODS

The PRISMA guidelines were followed to perform this systematic review. PubMed and EMBASE were searched up to February 29, 2020 for English, human in vivo studies that evaluated glenohumeral OA in patients undergoing Latarjet procedure at least 5 years after surgery. A pooled analysis on the included databases sent by authors was performed to evaluate the risk factors influencing the development or progression of dislocation arthropathy after the Latarjet procedure.

RESULTS

Four studies, including a total of 280 patients (213 males and 67 females), were analysed. In our study population, the median age at surgery was 25.0 years (range 20.8-32.6 years). and 92.1% were athletes. In 90% of the cases, the number of dislocations before surgery were fewer than 5. The recurrence of instability after Latarjet procedure was observed only in seven patients (2.5%). The position of the bone graft resulted flush to the anterior glenoid rim in 238 cases (85.3%), medial in 8 (2.9%) and overhanging in 33 (11.8%). Radiological signs of development or progression of shoulder OA were observed in 25.8% of the patients, of which 88.6% presented a grade 1 of OA according to Samilson and Prieto classification. The overhanging position of the bone graft resulted statistically significant for onset or worsening of OA. The age at surgery, the number of dislocations before surgery and the Hill-Sachs lesion were not significantly associated with joint degeneration. Instead, hyperlaxity showed a prevention role in the development of OA after open Latarjet procedure.

CONCLUSION

The Latarjet procedure is a valid and safe surgical treatment in recurrent anterior shoulder instability with a low risk of developing moderate or severe OA also at long-term follow-up. The overhanging position of the bone graft represents the principal risk factor of joint degeneration, whereas the hyperlaxity seems to be protective. Finally, age, gender, time between first dislocation and surgery, and number of dislocations do not seem to affect the onset of OA after Latarjet procedure. Therefore, an accurate execution of the Latarjet procedure can be considered a valid treatment even in young and athletes thanks to the low recurrence rates and the low development of major long-term complications.

LEVEL OF EVIDENCE

IV.

Artificial Intelligence Image-Assisted Knee Ligament Trauma Repair Efficacy Analysis and Postoperative Femoral Nerve Block Analgesia Effect Research

OBJECTIVE

To analyze artificial intelligence image-assisted knee ligament injury repair and femoral nerve block analgesia after surgery.

METHODS

Data-driven and artificial intelligence methods were adopted to systematically study magnetic resonance imaging image reconstruction, processing, and analysis. First, knee ligament reconstruction and femoral arteriography images were studied. Using the prior knowledge that the full width at half maximum of the contrast image does not change with the resolution, a constrained data exploration algorithm was proposed combined with the iterative algorithm. The algorithm could reconstruct high-resolution images using the collected low-frequency data of k-space. The experimental data and results were simulated with the enhanced knee ligaments and femoral nerve angiography images. Combining the spatial continuity of knee ligaments and femoral nerve, a multilayer input segmentation network was designed. The multisupervised network was adopted for output and had good segmentation results for the knee ligaments and femoral nerve. On this basis, a multiparametric image input speaker net was proposed to detect knee ligament injuries.

RESULTS

The area under the receiver operating characteristic curve of the constructed model under the test set was 0.824, and the sensitivity and specificity were 0.800 and 0.836, respectively. The image was better than compressed sensing to reconstruct the image, which was more accurate for knee ligament and femoral nerve stenosis. The network also had higher sensitivity for knee joint trauma detection, which could aid clinicians. The postoperative femoral nerve block had a good detection effect, which could provide important information for clinical analgesia.

CONCLUSIONS

The artificial intelligence image-assisted diagnosis system for analysis and processing of multiparametric magnetic resonance images is useful for clinical decision making, reducing physicians' labor intensity, improving efficiency, and lowering the rate of misdiagnosis.