Predictors Using Machine Learning of Complete Peroneal Nerve Palsy Recovery After Multiligamentous Knee Injury: A Multicenter Retrospective Cohort Study

Diabetic neuropathy: cutting-edge research and future directions

Diabetic neuropathy (DN) is a prevalent and debilitating complication of diabetes mellitus, significantly impacting patient quality of life and contributing to morbidity and mortality. Affecting approximately 50% of patients with diabetes, DN is predominantly characterized by distal symmetric polyneuropathy, leading to sensory loss, pain, and motor dysfunction, often resulting in diabetic foot ulcers and lower-limb amputations. The pathogenesis of DN is multifaceted, involving hyperglycemia, dyslipidemia, oxidative stress, mitochondrial dysfunction, and inflammation, which collectively damage peripheral nerves. Despite extensive research, disease-modifying treatments remain elusive, with current management primarily focusing on symptom control. This review explores the complex mechanisms underlying DN and highlights recent advances in diagnostic and therapeutic strategies. Emerging insights into the molecular and cellular pathways have unveiled potential targets for intervention, including neuroprotective agents, gene and stem cell therapies, and innovative pharmacological approaches. Additionally, novel diagnostic tools, such as corneal confocal microscopy and biomarker-based tests, have improved early detection and intervention. Lifestyle modifications and multidisciplinary care strategies can enhance patient outcomes. While significant progress has been made, further research is required to develop therapies that can effectively halt or reverse disease progression, ultimately improving the lives of individuals with DN. This review provides a comprehensive overview of current understanding and future directions in DN research and management.

Rethinking the Schenck Classification for Multiligament Knee Injuries: Evaluating Whether the Schenck KD Grade Is Associated With the Presence of Vascular or Neurological Injuries in a Multicenter Study With 144 Patients

Background

Posterolateral corner (PLC) lesions and knee dislocations (KDs) have been recognized as risk factors for vascular and neurological injuries in patients with multiligament knee injury (MLKI), but an association between Schenck KD grade and neurovascular lesions has yet to be established.

Hypothesis

The ligamentous injury pattern in MLKIs with high KD grades will be associated with a higher likelihood of vascular and neurological injuries.

Study Design

Cross-sectional study; Level of evidence, 3.

Methods

Included were 144 patients from a multicenter database with surgically treated MLKI. All patients were skeletally mature, had MLKI lesion identified on magnetic resonance imaging and confirmed intraoperatively, and did not have any previous knee surgery or previous vascular or neurological lesions. Demographic data (sex, age), injury mechanism (high energy, sports injury, low energy), ligaments injured, and neurological and vascular lesions were recorded. A new classification for MLKI based on ligamentous injury pattern, and intended for all MLKIs (with and without KD) was developed, and all patients were categorized according to this classification. Associations were evaluated between the risk of vascular and neurological lesion and demographic data, injury mechanism, and new classification grade.

Results

The mean patient age was 33.9 years (range, 15-64 years), and 72% were male. High-energy trauma was the most common injury mechanism (55.6%). Vascular injury was present in 5 patients (3.5%) and nerve injury in 17 (11.8%), with 1 patient (0.7%) having both. None of the analyzed variables were associated with the presence of vascular lesion. Univariate logistic regression showed that medial collateral ligament (MCL) lesion decreased the probability of neurological injury (odds ratio [OR], 0.29; 95% CI, 0.1-0.87; P = .03) while PLC injury increased that probability (OR, 12.66; 95% CI, 1.63-100; P = .02). Multivariate logistic regression showed that the proposed MLKI grade was significantly associated with the presence of neurological lesions, with a 2.5-fold increase in the odds of having a neurological injury for each increase in grade (OR, 2.47; 95% CI, 1.36-4.50; P = .003).

Conclusion

PLC injuries increased the odds of neurological injury in MLKI, while MCL injuries decreased these odds. MLKI grade and presence of PLC injury was associated with the presence of neurological injury. MLKI grade was not associated with the presence of a vascular lesion.

Functional outcomes of different surgical treatments for common peroneal nerve injuries: a retrospective comparative study

BACKGROUND

This study aims to assess the recovery patterns and factors influencing outcomes in patients with common peroneal nerve (CPN) injury.

METHODS

This retrospective study included 45 patients with CPN injuries treated between 2009 and 2019 in Jing'an District Central Hospital. The surgical interventions were categorized into three groups: neurolysis (group A; n = 34 patients), nerve repair (group B; n = 5 patients) and tendon transfer (group C; n = 6 patients). Preoperative and postoperative sensorimotor functions were evaluated using the British Medical Research Council grading system. The outcome of measures included the numeric rating scale, walking ability, numbness and satisfaction. Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal time interval between injury and surgery for predicting postoperative foot dorsiflexion function, toe dorsiflexion function, and sensory function.

RESULTS

Surgical interventions led to improvements in foot dorsiflexion strength in all patient groups, enabling most to regain independent walking ability. Group A (underwent neurolysis) had significant sensory function restoration (P < 0.001), and three patients in Group B (underwent nerve repair) had sensory improvements. ROC analysis revealed that the optimal time interval for achieving M3 foot dorsiflexion recovery was 9.5 months, with an area under the curve (AUC) of 0.871 (95% CI = 0.661-1.000, P = 0.040). For M4 foot dorsiflexion recovery, the optimal cut-off was 5.5 months, with an AUC of 0.785 (95% CI = 0.575-0.995, P = 0.020). When using M3 toe dorsiflexion recovery or S4 sensory function recovery as the gold standard, the optimal cut-off remained at 5.5 months, with AUCs of 0.768 (95% CI = 0.582-0.953, P = 0.025) and 0.853 (95% CI = 0.693-1.000, P = 0.001), respectively.

CONCLUSIONS

Our study highlights the importance of early surgical intervention in CPN injury recovery, with optimal outcomes achieved when surgery is performed within 5.5 to 9.5 months post-injury. These findings provide guidance for clinicians in tailoring treatment plans to the specific characteristics and requirements of CPN injury patients.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Background

There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation).

Methods

A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external).

Results

A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation.

Conclusion

Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications.

Level of Evidence

Level III, retrospective cohort study.