An Evaluation of the Effects of Elevated Body Mass Index on Skeletal Age

BackgroundMany treatment decisions in children’s Orthopaedics are based on age. This study determined whether a discrepancy between chronological age (CA) and skeletal age (SA) is dependent on BMI and if overweight or obese children would have an advanced SA.Materials and Methods120 children between ages 8-17 with an adequate hand radiograph and a correlating BMI were enrolled by retrospective chart review. Stratification based on age, sex, ethnicity, and BMI percentile was performed. For each age group, 6 males and 6 females were selected with 50% of each group having an elevated BMI. Two blinded physicians independently evaluated hand radiographs and recorded the SA. Statistical analyses evaluated inter-rater reliability and any discrepancy between groups.ResultsThe final statistical analysis included 96 children. The Intraclass Correlation Coefficient for SA determined by the two reviewers was excellent at 0.95. A difference of 13 months was found between CA and SA in the elevated BMI cohort versus the non-elevated BMI cohort, (p<0.001). No significant difference was seen between CA and SA for the non-elevated cohort (p=0.72), while matching for age and sex. ConclusionChronological age and skeletal age are not always equivalent especially in pediatric patients who are overweight or obese.

Jaccoud's arthropathy- a rare but well known clinical entity

Jaccoud arthropathy is a deforming non-erosive arthropathy characterized by ulnar deviation of the second to fifth fingers with metacarpophalangeal joint subluxation that is correctable with physical manipulation1. It was traditionally described as occurring post-rheumatic fever but also seen in association with systemic lupus erythematosus, psoriatic arthritis, inflammatory bowel disease and malignancy2. It is thought to be related to ligamentous laxity. It typically affects themetacarpophalangeal joints but can also affect the proximal interphalangeal joints of the hands, wrists and knees3. Hand radiographs typically show marked ulnar subluxation and deviation atthe metacarpophalangeal joints with absence of erosions. We present a case in a very youngfemale with no prior history of rheumatic fever or acute arthritis at any stage of illness.

Acro-osteolysis in Hajdu Cheney syndrome: A case report with review of literature

Objectives: We present a case report and review the literature on Hajdu Cheney syndrome (HCS), an extremely rare connective tissue disorder with <100 cases reported in the last 72 years. We have emphasized on the patterns of acro-osteolysis (acrosteolysis) in the literature review to conclude if the syndrome follows any particular pattern like in our case. Material and Methods: All major databases were searched for all cases of HCS. One hundred and eighty-eight hand radiographs were analyzed and detailed analysis of all digits was carried out with emphasis on the pattern of acrosteolysis. Results: Acrosteolysis may not be a mandatory association in HCS as 18.8% did not have acrosteolysis at all. The first finger to be involved in 90/96 (93.7%) of the cases was the index finger, followed by the middle finger and then the thumb. The 4th digit (ring finger) was involved in only 11/96 (11.4%) of the cases, of which 9/11 (81.8%) were above the age of 25. Incidence of acrosteolysis of the 4th digit when in comparison to all other finger has a P < 0.05 and a P < 0.001 with the index finger. Newborn with HCS evaluated for acrosteolysis at birth was negative. Conclusion: There was statistical evidence to conclude that in a majority of the cases the 4th digit was involved the least. A rough timeline of the onset and progression of acrosteolysis was made. An attempt was made to shed light on the possible lesser-known manifestations of the syndrome such as retroflexed odontoid, Arnold Chiari I, middle phalanx osteolysis, and first carpometacarpal joint osteolysis. As very little is known about the disease and awareness about it is pertinent for early management and to differentiate it from other less-lethal causes of acrosteolysis.

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

POS0125 CALCULATING RISK OF HAND OSTEOARTHRITIS PROGRESSION AT TEN YEARS THROUGH A PREDICTION MODEL

Background:Hand osteoarthritis (OA) is a chronic, progressive disease, commonly affecting middle aged women. OA at the interphalangeal joints (IPJs) or the thumb base are considered different disease subsets (1). Few studies have investigated individual risk factors for IPJ OA progression (2). Prediction models can be used to calculate overall disease risk from multiple risk factors. This can guide prevention and treatment options.Objectives:Develop and internally validate a prediction model for IPJ OA progression.Methods:Data from the Chingford 1000 Women Study (Chingford Study), the largest population-based cohort worldwide assessing hand OA, was used. It is representative of the middle-aged female population in the United Kingdom (3). At baseline, 1,003 women aged 45 to 64 years’ old were recruited, and 693 measurements taken. Hand radiographs were taken at baseline and after ten years, read using the Kellgren-Lawrence (KL) atlas (inter-observer correlation: ≥0.7 (4)).For the current study, participants must have had OA (KL ≥2 in ≥1 IPJ) on baseline hand radiographs. Participants with KL 4 in all 16 IPJs at baseline were excluded. Risk factors from the Chingford Study at baseline were selected by biological plausibility, literature evidence (2), and hand surgeons‘ consensus (5): age (years), occupation (manual versus non manual), OA in ≥1 thumb base (KL ≥2 versus KL<2), body mass index (BMI) (kg/m2), family history of hand OA (yes versus no). The outcome was defined on an ordinal scale for the number of IPJs (up to >5 IPJs) with OA progression (increase by KL ≥1), at ten years’.The prediction model was developed using a penalized proportional odds logistic regression. Odds ratios (95% confidence intervals) were reported for each risk factor. The model was internally validated using 2,000 bootstrap iterations. Model performance was assessed for discrimination (C-statistic), and calibration (C-slope). 3.5% of data was missing, and complete case analysis was used.Results:699 women had baseline hand radiographs: 38 were unreadable, 459 had no IPJ OA. Seven participants had missing data (occupation: 5, BMI: 1, family history: 1) and were excluded. 195 participants were included this study. Median age at baseline was 59 (interquartile range: 8) years.181 (92.8%) participants had OA progression at 10 years (Figure 1). Thumb base OA (odds ratio: 1.32 (0.93 to 1.88)) was most strongly associated with IPJ OA progression (Table 1). C-statistic was 0.57, and calibration slope was 1.38 for the optimism-corrected model.Table 1.Odds ratios for risk factorsRisk factorOdds ratio (95% confidence interval)Age (years)1.02 (0.99 to 1.06)Occupation (manual versus non manual)0.88 (0.60 to 1.29)Thumb base OA (Kellgren-Lawrence grade ≥2 versus <2)1.32 (0.93 to 1.88)Family history of hand OA (yes versus no)1.03 (0.72 to 1.45)Body mass index (kg/m2)1.04 (0.99 to 1.09)OA: OsteoarthritisConclusion:More stringent cut-offs for OA progression would be clinically useful. It was only weakly possible to predict which participants with IPJ OA would progress. This suggests that other risk factors, such as gender, ethnicity and genetics, may be predominant.Figure 1.Hand interphalangeal joints with osteoarthritis progression (Kellgren-Lawrence grade ≥1) at 10 years’ follow upReferences:[1]Kloppenburg M, et al. Research in hand osteoarthritis: time for reappraisal and demand for new strategies. Ann Rheum Dis. 2007;66(9):1157-61.[2]Shah K, et al. Risk factors for the progression of finger interphalangeal joint osteoarthritis: a systematic review. Rheumatol Int. 2020;40(11):1781-1792.[3]Hart DJ, Spector TD. The relationship of obesity, fat distribution and osteoarthritis in women in the general population: the Chingford Study. J Rheumatol. 1993;20:331-335.[4]Hart DJ, et al. Reliability and reproducibility of grading radiographs for osteoarthritis of the hand. Br J Rheum. 1993;32:S1.[5]Shah K, et al. Delphi consensus of risk factors for development and progression of finger interphalangeal joint osteoarthritis. J Hand Surg Eur Vol. 2019;44(10):1089-1090.Acknowledgements:We would like to thank all of the participants of The Chingford 1000 Women Study, Professor Tim Spector, Dr Deborah Hart, Dr Alan Hakim, Maxine Daniels, Alison Turner, James van Santen and Julie Damnjanovic for their time and dedication.Disclosure of Interests:Karishma Shah: None declared, Garrett Bullock: None declared, Alan Silman: None declared, Dominic Furniss: None declared, Nigel Arden Consultant of: Receives personal fees from Pfizer/Lily for consultancy outside the scope of this work, Grant/research support from: Receives grant from Merck outside the scope of this work, Gary Collins: None declared

“One Note Higher”: A Unique Pediatric Hand Fracture

Case Presentation: An otherwise healthy, 12-year-old male presented to the emergency department after a fall down the stairs in which he landed on his right hand. Radiographs demonstrated a Salter-Harris II fracture at the base of the proximal phalanx of the fifth digit with ulnar deviation, also known as an “extra-octave“ fracture. Orthopedic surgery was consulted and the fracture was reduced and placed in a short-arm cast. The patient was discharged and scheduled for orthopedic follow-up. Discussion: A Salter-Harris II fracture at the base of the proximal phalanx of the fifth digit with ulnar deviation is referred to as an “extra-octave” fracture due to the advantage a pianist would gain in reach of their fifth phalanx if not reduced. However, reduction is needed if the fracture is displaced and can be achieved by several described methods including the “90-90” or “pencil” methods followed by cast or splint application. Percutaneous pinning is rarely needed. Complications include flexor tendon entrapment, collateral ligament disruption, and malunion leading to a “pseudo-claw” deformity. We recommend that all extra-octave fractures receive orthopedic follow-up in one to two weeks or sooner if severely displaced.

Quality of Hand Radiograph Collimation Determined by Artificial Intelligence Algorithm Correlates with Radiograph Quality Scores Assigned by Radiologists

Providing direct feedback to technologists has become challenging for radiologists due to geographic separation and other reasons. As such, there is a need for automated solutions to solve quality issues in radiography. We evaluated the feasibility of using a computer vision artificial intelligence (AI) algorithm to classify hand radiographs into quality categories in order to automate quality assurance processes in radiology. A bounding box was placed over the hand on 300 hand radiographs. These inputs were employed to train the computational neural network (CNN) to automatically detect hand boundaries. The trained CNN detector was used to place bounding boxes over the hands on an additional 100 radiographs, independently of the training or validation sets. A computer algorithm processed each output image to calculate unused air spaces. The same 100 images were classified by two musculoskeletal radiologists into four quality categories. The correlation between the AI-calculated unused space metric and radiologist-assigned quality scores was determined using the Spearman correlation coefficient. The kappa statistic was used to calculate the inter-reader agreement. The best negative correlation between the AI-assigned metric and the radiologists’ assigned quality scores was achieved using the calculation of the unused space at the top of the image. The Spearman correlation coefficients were −0.7 and −0.6 for the two radiologists. The kappa correlation coefficient for interobserver agreement between the two radiologists was 0.6. Automatic calculation of the percentage of unused space or indirect collimation at the top of hand radiographs correlates moderately well with radiographic collimation quality.