Hand and Upper Limb Clinic

Distal radioulnar joint (DRUJ) instability is a complex pathology that typically occurs due to injury of the triangular fibrocartilage complex. In the acute setting, repair is often successful. However, in the chronic setting or after a prior failed repair, reconstructive methods aim to improve stability using an extrinsic radioulnar soft tissue link. Traditional surgical techniques are technically demanding and require a broad exposure, compromising extrinsic stabilizers. Modifications to these methods using arthroscopy facilitate preservation of ulnocarpal and DRUJ secondary stabilizers, while providing excellent visualization for anatomic passage of the graft and placement of the foveal bone tunnel. We describe a simple and effective arthroscopic reconstruction method for restoring and reinforcing the native anatomy of the critical stabilizers of the DRUJ.

Diabetes mellitus leads to progressive multiorgan involvement including pulmonary, renal, musculoskeletal and orthopedic systems, yet few experimental studies have quantified simultaneous impairments across these domains under controlled settings. The objective of this study was to assess lung function, kidney performance, and bone–muscle parameters in patients with type 2 diabetes compared with matched non-diabetic controls, and to examine correlations with glycemic control and disease duration. In a cross-sectional experimental study of 120 participants (60 with diabetes, 60 controls), forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), estimated glomerular filtration rate (eGFR), 24-h urinary albumin, bone mineral density (BMD) at femoral neck, and muscle strength by grip dynamometer were measured. Supposed results show significantly lower mean FVC (2.8 ± 0.5 vs 3.4 ± 0.4 L, p < 0.001), FEV₁ (2.2 ± 0.4 vs 2.9 ± 0.5 L, p < 0.001), reduced eGFR (68 ± 15 vs 95 ± 12 mL/min/1.73 m², p < 0.001), elevated albuminuria (mean 220 ± 80 vs 30 ± 15 mg/24 h, p < 0.001), lower BMD (femoral neck T-score −1.8 ± 0.7 vs −0.6 ± 0.5, p < 0.001), and decreased grip strength (22 ± 5 vs 30 ± 6 kg, p < 0.001) in diabetic group. Correlations show that longer disease duration (>10 years) and poorer glycemic control (HbA₁c ≥ 8%) associate with worse pulmonary, renal and orthopedic parameters (r between 0.45-0.60, p < 0.01). Discussion highlights that this simultaneous multiorgan impairment confirms that diabetes exerts systemic damage even in patients without overt clinical organ failure, and that combined assessment of pulmonary, renal, and bone-muscle health may allow earlier intervention. Study fills a gap by providing experimental evidence of combined quantitative impairment.

The humeral head resection plane relies on the anatomic neck as a reference for shoulder arthroplasty planning and accurate intraoperative placement of the humeral component. However, osteophytes and deformity associated with osteoarthritis often obscures this landmark increasing preoperative planning time and complicating placement of the humeral component. To date, no automated method for identifying the resection plane in arthritic humeri exists. Two orthopedic surgeons determined the resection plane of 62 3D-models of the humerus derived from computed tomography scans by digitizing along the anatomic neck and then fitting the resection plane to those points. A deep learning model was then trained on 80% of the humeri and their corresponding resections. Testing was performed on the remaining 20% by inputting 3D humerus models into the trained deep learning model and assessing the predicted resection plane's deviation from the surgeon-defined resection plane. Evaluation metrics included the mean Euclidean distance between the centroids of both planes and the angular error between their normal vectors to quantify model accuracy. For arthritic humeri, the predicted resection plane when compared to the surgeon selected plane had mean absolute errors of 1.4 ± 0.7 mm for the centroid and 3.9 ± 1.6° for the normal vector. In nonarthritic humeri the mean absolute errors were 0.3 ± 0.2 mm and 3.1 ± 1.6, respectively. Our deep learning model achieves state-of-the-art accuracy for predicting the location of the resection plane. The model maintains robust performance in arthritic shoulders, enabling further automation of shoulder arthroplasty planning in more complex arthritic cases.

BACKGROUNDThumb replantation following complete traumatic avulsion requires complex techniques to restore function, especially in cases of avulsion at the level of the metacarpophalangeal joint (MCP I) and avulsion of the flexor pollicis longus (FPL) at the musculotendinous junction. Possible treatments include direct tendon suture or tendon transfer, most commonly from the ring finger. To optimize function and avoid donor finger complications, we performed thumb replantation with flexion restoration using brachioradialis (BR) tendon transfer with palmaris longus (PL) tendon graft.CASE SUMMARYA 20-year-old left-handed male was admitted for a complete traumatic left thumb amputation following an accident while sliding from the top of a handrail. The patient presented with skin and bone avulsion at the MCP I, avulsion of the FPL tendon at the musculotendinous junction (zone 5), avulsion of the extensor pollicis longus tendon (zone T3), and avulsion of the thumb’s collateral arteries and nerves. The patient was treated with two stage thumb repair. The first intervention consisted of thumb replantation with MCP I arthrodesis, resection of avulsed FPL tendon and implantation of a silicone tendon prosthesis. The second intervention consisted of PL tendon graft and BR tendon transfer. Follow-up at 10 months showed good outcomes with active interphalangeal flexion of 70°, grip strength of 45 kg, key pinch strength of 15 kg and two-point discrimination threshold of 4 mm.CONCLUSIONFlexion restoration after complete thumb amputation with FPL avulsion at the musculotendinous junction can be achieved using BR tendon transfer with PL tendon graft.

Symptomatic varus malunion after proximal humeral fractures is associated with weakness and painful limitation of shoulder range of motion. When there is conformity of the articular surface and no avascular necrosis, a head-preserving procedure is best indicated. Arthroscopic arthrolysis, subacromial decompression, and tuberoplasty have been described for the treatment of mild deformity. In cases with more severe deformity, corrective extracapsular lateral closing wedge valgus osteotomy has been reported as a reliable treatment option, in terms of both pain relief and improved function. While this procedure adequately restores rotator cuff tensioning, it is associated with a shortening of the lever arm to the deltoid muscle, secondary to a loss of humeral length. We describe our technique and results with a vascular-sparing, medial open-wedge osteotomy, using a structural allograft and lateral locking plate. In our opinion, this procedure is safe and effective, with the potential to improve functional outcomes in young and active patients.

Background Quantum computing and quantum machine learning (QML) are promising experimental technologies that can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this study, we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methodology After obtaining patients' consent and Research Ethics Committee approval, we collected the clinicodemographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥3, Oxford Knee Score (OKS) ≤27, age ≥64 years, and idiopathic aetiology of arthritis) treated over a two-year period with a single injection of microfragmented fat. Gender classes were balanced (76 males and 94 females) to mitigate gender bias. A patient with an improvement ≥7 OKS was considered a responder. We trained our QNN classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 responders and 40 non-responders) in pain and function at one year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN classifier on a randomly selected test subset of 57 patients (34 responders, 23 non-responders) including outliers. The no information rate was 0.59. Our application correctly classified 28 responders out of 34 and 6 non-responders out of 23 (sensitivity = 0.82, specificity = 0.26, F1 Statistic = 0.71). The positive and negative likelihood ratios were 1.11 and 0.68, respectively. The diagnostic odds ratio was 2. Conclusions Preliminary results on a small validation dataset showed that QML applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, as well as clinical validation with an artificial intelligence clinical trial to test model efficacy, safety, clinical significance, and relevance at a public health level.