We report the incidence of and risk factors for complications after scoliosis surgery in patients with Duchenne muscular dystrophy (DMD) and compare them with those of other neuromuscular conditions.

We identified 110 (64 males, 46 females) consecutive patients with a neuromuscular disorder who underwent correction of the scoliosis at a mean age of 14 years (7 to 19) and had a minimum two-year follow-up. We recorded demographic and peri-operative data, including complications and re-operations.

There were 60 patients with cerebral palsy (54.5%) and 26 with DMD (23.6%). The overall complication rate was 22% (24 patients), the most common of which were deep wound infection (9, 8.1%), gastrointestinal complications (5, 4.5%) and hepatotoxicity (4, 3.6%). The complication rate was higher in patients with DMD (10/26, 38.5%) than in those with other neuromuscular conditions (14/84, 16.7% (p = 0.019). All hepatotoxicity occurred in patients with DMD (p = 0.003), who also had an increased rate of deep wound infection (19% vs 5%) (p = 0.033). In the DMD group, no peri-operative factors were significantly associated with the rate of overall complications or deep wound infection. Increased intra-operative blood loss was associated with hepatotoxicity (p = 0.036).

In our series, correction of a neuromuscular scoliosis had an acceptable rate of complications: patients with DMD had an increased overall rate compared with those with other neuromuscular conditions. These included deep wound infection and hepatotoxicity. Hepatotoxicity was unique to DMD patients, and we recommend peri-operative vigilance after correction of a scoliosis in this group.

Cite this article: Bone Joint J 2014; 96-B:943–9.

1. Three patients with localised periosteal new bone formation associated with periosteal arteritis and other evidence of systemic lupus erythematosus are described.

2. Systemic steroid therapy was valuable in the management of this condition.

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

Aims

The aim of this study was to utilize a national paediatric inpatient database to determine whether obesity influences the operative management and inpatient outcomes of paediatric limb fractures.

The February 2023 Knee Roundup³⁶⁰ looks at: Machine-learning models: are all complications predictable?; Positive cultures can be safely ignored in revision arthroplasty patients that do not meet the 2018 International Consensus Meeting Criteria; Spinal versus general anaesthesia in contemporary primary total knee arthroplasty; Preoperative pain and early arthritis are associated with poor outcomes in total knee arthroplasty; Risk factors for infection and revision surgery following patellar tendon and quadriceps tendon repairs; Supervised versus unsupervised rehabilitation following total knee arthroplasty; Kinematic alignment has similar outcomes to mechanical alignment: a systematic review and meta-analysis; Lifetime risk of revision after knee arthroplasty influenced by age, sex, and indication; Risk factors for knee osteoarthritis after traumatic knee injury.

The October 2023 Hip & Pelvis Roundup³⁶⁰ looks at: Femoroacetabular impingement syndrome at ten years – how do athletes do?; Venous thromboembolism in patients following total joint replacement: are transfusions to blame?; What changes in pelvic sagittal tilt occur 20 years after total hip arthroplasty?; Can stratified care in hip arthroscopy predict successful and unsuccessful outcomes?; Hip replacement into your nineties; Can large language models help with follow-up?; The most taxing of revisions – proximal femoral replacement for periprosthetic joint infection – what’s the benefit of dual mobility?

The October 2023 Children’s orthopaedics Roundup³⁶⁰ looks at: Outcomes of open reduction in children with developmental hip dislocation: a multicentre experience over a decade; A torn discoid lateral meniscus impacts lower-limb alignment regardless of age; Who benefits from allowing the physis to grow in slipped capital femoral epiphysis?; Consensus guidelines on the management of musculoskeletal infection affecting children in the UK; Diagnosis of developmental dysplasia of the hip by ultrasound imaging using deep learning; Outcomes at a mean of 13 years after proximal humeral fracture during adolescence; Clubfeet treated according to Ponseti at four years; Controlled ankle movement boot provides improved outcomes with lower complications than short leg walking cast.

Aims

Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort.

Aims

While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

Aims

Psychological status may be an important predictor of outcome after periacetabular osteotomy (PAO). The aim of this study was to investigate the influence of psychological distress on postoperative health-related quality of life, joint function, self-assessed pain, and sports ability in patients undergoing PAO.

Aims

In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

Acute angulation at the thoracolumbar junction with segmental subluxation of the spine occurring at the level above an anteriorly hypoplastic vertebra in otherwise normal children is a rare condition described as infantile developmental thoracolumbar kyphosis. Three patient series with total of 18 children have been reported in the literature. We report five children who presented with thoracolumbar kyphosis and discuss the treatment algorithm. We reviewed the medical records and spinal imaging at initial clinical presentation and at minimum two-year follow-up. The mean age at presentation was eight months (two to 12). All five children had L2 anterior vertebral body hypoplasia. The kyphosis improved spontaneously in three children kept under monitoring. In contrast, the deformity was progressive in two patients who were treated with bracing. The kyphosis and segmental subluxation corrected at latest follow-up (mean age 52 months; 48 to 60) in all patients with near complete reconstitution of the anomalous vertebra. The deformity and radiological imaging on a young child can cause anxiety to both parents and treating physicians. Diagnostic workup and treatment algorithm in the management of infantile developmental thoracolumbar kyphosis is proposed. Observation is indicated for non-progressive kyphosis and bracing if there is evidence of kyphosis and segmental subluxation deterioration beyond walking age. Surgical stabilisation of the spine can be reserved for severe progressive deformities unresponsive to conservative treatment.

Cite this article: Bone Joint J 2015;97-B:982–7.

Aims

Periacetabular osteotomy (PAO) is an established treatment for acetabular dysplasia. It has also been proposed as a treatment for patients with acetabular retroversion. By reviewing a large cohort, we aimed to test whether outcome is equivalent for both types of morphology and identify factors that influenced outcome.

Segmental vessel ligation during anterior spinal surgery has been associated with paraplegia. However, the incidence and risk factors for this devastating complication are debated.

We reviewed 346 consecutive paediatric and adolescent patients ranging in age from three to 18 years who underwent surgery for anterior spinal deformity through a thoracic or thoracoabdominal approach, during which 2651 segmental vessels were ligated. There were 173 patients with idiopathic scoliosis, 80 with congenital scoliosis or kyphosis, 43 with neuromuscular and 31 with syndromic scoliosis, 12 with a scoliosis associated with intraspinal abnormalities, and seven with a kyphosis.

There was only one neurological complication, which occurred in a patient with a 127° congenital thoracic scoliosis due to a unilateral unsegmented bar with contralateral hemivertebrae at the same level associated with a thoracic diastematomyelia and tethered cord. This patient was operated upon early in the series, when intra-operative spinal cord monitoring was not available.

Intra-operative spinal cord monitoring with the use of somatosensory evoked potentials alone or with motor evoked potentials was performed in 331 patients. This showed no evidence of signal change after ligation of the segmental vessels.

In our experience, unilateral segmental vessel ligation carries no risk of neurological damage to the spinal cord unless performed in patients with complex congenital spinal deformities occurring primarily in the thoracic spine and associated with intraspinal anomalies at the same level, where the vascular supply to the cord may be abnormal.

Aims

The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?