Quantitative sensory testing in painful hand osteoarthritis demonstrates features of peripheral sensitisation.

Hand osteoarthritis (HOA) is a prevalent condition for which treatments are based on analgesia and physical therapies. Our primary objective was to evaluate pain perception in participants with HOA by assessing the characteristics of nodal involvement, pain threshold in each hand joint, and radiological severity. We hypothesised that inflammation in hand osteoarthritis joints enhances sensitivity and firing of peripheral nociceptors, thereby causing chronic pain. Participants with proximal and distal interphalangeal (PIP and DIP) joint HOA and non-OA controls were recruited. Clinical parameters of joint involvement were measured including clinical nodes, VAS (visual analogue score) for pain (0-100 mm scale), HAQ (health assessment questionnaire), and Kellgren-Lawrence scores for radiological severity and pain threshold measurement were performed. The mean VAS in HOA participants was 59.3 mm ± 8.19 compared with 4.0 mm ± 1.89 in the control group (P < 0.0001). Quantitative sensory testing (QST) demonstrated lower pain thresholds in DIP/PIP joints and other subgroups in the OA group including the thumb, metacarpophalangeal (MCPs), joints, and wrists (P < 0.008) but not in controls (P = 0.348). Our data demonstrate that HOA subjects are sensitised to pain due to increased firing of peripheral nociceptors. Future work to evaluate mechanisms of peripheral sensitisation warrants further investigation

CT methods for measuring glenoid bone loss are inaccurate, and not reproducible or interchangeable.

AIMS: Glenoid bone loss is a significant problem in the management of shoulder instability. The threshold at which the bone loss is considered "critical" requiring bony reconstruction has steadily dropped and is now approximately 15%. This necessitates accurate measurement in order that the correct operation is performed. CT scanning is the most commonly used modality and there are a number of techniques described to measure the bone loss however few have been validated. The aim of this study was to assess the accuracy of the most commonly used techniques for measuring glenoid bone loss on CT. METHODS: Anatomically accurate models with known glenoid diameter and degree of bone loss were used to determine the mathematical and statistical accuracy of six of the most commonly described techniques (relative diameter, linear ipsilateral circle of best fit (COBF), linear contralateral COBF, Pico, Sugaya, and circle line methods). The models were prepared at 13.8%, 17.6%, and 22.9% bone loss. Sequential CT scans were taken and randomized. Blinded reviewers made repeated measurements using the different techniques with a threshold for theoretical bone grafting set at 15%. RESULTS: At 13.8%, only the Pico technique measured under the threshold. At 17.6% and 22.9% bone loss all techniques measured above the threshold. The Pico technique was 97.1% accurate, but had a high false-negative rate and poor sensitivity underestimating the need for grafting. The Sugaya technique had 100% specificity but 25% of the measurements were incorrectly above the threshold. A contralateral COBF underestimates the area by 16% and the diameter by 5 to 7%. CONCLUSION: No one method stands out as being truly accurate and clinicians need to be aware of the limitations of their chosen technique. They are not interchangeable, and caution must be used when reading the literature as comparisons are not reliable

Radiological features do not predict failure of two-stage arthroplasty for prosthetic joint infection: a retrospective case-control study.

BACKGROUND: The management of prosthetic joint infection is complex and there is a lack of standardisation of approaches. We evaluated the role of plain film radiography in predicting prosthesis failure after the first stage of a two-stage revision procedure in a retrospective case-control study. METHODS: Plain films for 41 patients aged 46 to 87 years (mean 69) were assessed by two musculoskeletal specialist radiologists for seven features (retained or new metalwork, retained cement or restrictor, new fracture, local antimicrobial delivery system and drain) we hypothesised may predict for failure. Inter-observer agreement was assessed by Kappa score and logistic regression analysis was performed to evaluate the relationship of the seven radiological features adjusting for patient age, gender and number of previous revisions. RESULTS: There was substantial inter-observer agreement, with a Kappa score of 0.73 (95% CI 0.72-0.74) for all data points collected. Concordance was 100% for evaluating the presence or absence of an antimicrobial delivery system or drain, with lower consensus for evaluating cement (Kappa 0.60, 95% CI 0.35-0.84) and fractures (Kappa 0.59, 95% CI 0.31-0.87). None of the variables' conditions significantly predicted failure. CONCLUSIONS: Our findings support the opinion that surgical expertise which maximizes removal of foreign material is sufficient in conjunction with antibiotic therapy

Radiological features do not predict failure of two-stage arthroplasty for prosthetic joint infection: a retrospective case–control study

Background: The management of prosthetic joint infection is complex and there is a lack of standardisation of approaches. We evaluated the role of plain film radiography in predicting prosthesis failure after the first stage of a two-stage revision procedure in a retrospective case–control study. Methods: Plain films for 41 patients aged 46 to 87 years (mean 69) were assessed by two musculoskeletal specialist radiologists for seven features (retained or new metalwork, retained cement or restrictor, new fracture, local antimicrobial delivery system and drain) we hypothesised may predict for failure. Inter-observer agreement was assessed by Kappa score and logistic regression analysis was performed to evaluate the relationship of the seven radiological features adjusting for patient age, gender and number of previous revisions. Results: There was substantial inter-observer agreement, with a Kappa score of 0.73 (95% CI 0.72-0.74) for all data points collected. Concordance was 100% for evaluating the presence or absence of an antimicrobial delivery system or drain, with lower consensus for evaluating cement (Kappa 0.60, 95% CI 0.35-0.84) and fractures (Kappa 0.59, 95% CI 0.31-0.87). None of the variables’ conditions significantly predicted failure. Conclusions: Our findings support the opinion that surgical expertise which maximizes removal of foreign material is sufficient in conjunction with antibiotic therapy. </p

Developing an artificial intelligence diagnostic tool for paediatric distal radius fractures, a proof of concept study

Introduction: In the UK 1 in 50 children will sustain a fractured bone yearly yet studies have shown that 34% of children sustaining an injury do not have a visible fracture on initial radiographs. Wrist fractures are particularly difficult to identify as the growth plate poses diagnostic challenges when interpreting radiographs. Materials and methods: We developed convolutional neural network (CNN) image recognition software to detect fractures in radiographs of children. A consecutive dataset of 5000 radiographs of the distal radius in children aged less than 19 years from 2014-2019 were used to train the CNN. Additionally transfer learning from a VGG16 CNN pre-trained on non-radiological images was applied to improve generalization of the network and classification of radiographs. Hypermeter tuning techniques were used to compare the model to the radiology reports that accompanied the original images to determine diagnostic test accuracy. Results: The training set consisted of 2881 radiographs with a fracture and 1571 without a fracture, 548 radiographs were outliers. With additional augmentation the final dataset consisted of 15,498 images. The dataset was randomly split into three subsets, a training dataset (70%), a validation dataset (10%), and a test dataset (20%). After training for 20 epochs, the diagnostic test accuracy was 85%. Discussion: A CNN model is feasible in diagnosing paediatric wrist fractures. We demonstrated that this application could be utilized as a tool for improving diagnostic accuracy. Future work would involve developing automated treatment pathways for diagnosis, reducing unnecessary hospital visits and allowing staff redeployment to other areas