The child with joint pain in primary care

Sen, Ethan S; Clarke, Sarah L N; Ramanan, Athimalaipet V

doi:10.1016/j.berh.2015.04.008

Cited by 17 publications

(23 citation statements)

References 99 publications

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“…Some studies suggest that environmental factors impacting the composition of the microbiota, such as delivery mode and early exposure to antibiotics, affect the risk of chronic inflammatory diseases including JIA [ 16 , 17 ]. For all these reasons, our study reinforces the necessity of improving GP and emergency physician’s awareness and education on paediatric rheumatic diseases [ 8 , 18 ]. In addition, the complexity of JIA patient’s journey to accurate diagnosis and care allows us to highlight the importance of a strong network in paediatric rheumatology to improve patient’s level of care.…”

Section: Discussionsupporting

confidence: 75%

Time to diagnosis in juvenile idiopathic arthritis: a french perspective

Aoust

Rossi‐Semerano

Koné‐Paut

et al. 2017

Orphanet J Rare Dis

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BackgroundJuvenile idiopathic arthritis (JIA) is a rare disease that is not widely known by paediatricians and general practitioner (GP) leading to diagnostic error and delayed care provision. We aimed to analyse patient’s journey and time to diagnosis of JIA (delay from the first symptom to the diagnosis of JIA).We performed a retrospective cohort study of 67 patients diagnosed with JIA and seen in the paediatric rheumatology department of the Kremlin Bicêtre Hospital, between July 2002 and January 2015. Patients were selected for analysis in order to represent an equal distribution of five JIA subtypes: oligoarticular onset (21), polyarticular onset (13), enthesitis-related arthritis (17), and systemic onset (16).ResultsSixty-seven patients were finally analysed (42 girls). Before JIA diagnosis was made, patients had visited a mean of three physicians (3.6 ± 1.4 (mean; SD)). Emergency room physicians (52%) were the first patient’s referral before GP (42%). Paediatric rheumatologists were mostly seen as third referral (52% versus 3% at first referral). Reactive arthritis (34%) and septic arthritis (24%) represented both the most common initial diagnosis. JIA was suspected after an average median time delay of 3 months (0.26–81.2) except for 25 patients (37%): SJIA (n = 9), ERA (n = 7), OAJIA (3) and POJIA (n = 6) for whom diagnosis was suspected straightaway. In most cases (88%), JIA was established by paediatric rheumatologists.Surprisingly, the median total time to diagnosis in our population was rather short (3 months). Paediatric rheumatologist played a major role in making the diagnosis but the journey to reach them was long and complex with multiple referrals. These results reinforce the necessity of improving GP and emergency physician’s awareness and education on paediatric rheumatic diseases as the importance of a strong network in paediatric rheumatology to improve patient’s level of care.ConclusionWe highlighted the complex patient’s journey to diagnosis in children with JIA and made assumptions that reference center might reduce time to diagnosis although not statically proven. Further analysis with a larger number of patients might be needed to better investigate this probability.

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Section: Discussionsupporting

confidence: 75%

Time to diagnosis in juvenile idiopathic arthritis: a french perspective

Aoust

Rossi‐Semerano

Koné‐Paut

et al. 2017

Orphanet J Rare Dis

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“…In principle, these limited parameters could be used to design a simplified immunophenotyping assay for JIA diagnosis. In practice, such an assay would be of relatively limited use: JIA diagnosis in secondary and tertiary referral centres is efficient (although diagnosis during primary care may be delayed due to overlapping early symptoms and the lack of specific biomarkers53), and the machine learning algorithm was derived from comparison with healthy individuals rather than patients with a relevant confounding disease (such as infectious arthritis). Nonetheless, the ability of machine learning to extract the relevant traits validates this approach for larger longitudinal cohorts, with deeper immunophenotyping.…”

Section: Discussionmentioning

confidence: 99%

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

et al. 2019

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ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

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“…Joint insensitive clinical algorithms do exist for the diagnosis of septic arthritis [16, 17]. Sen et al (2015) [7] recommend that patients with an acute joint in the outpatient setting receive routine lab work involving serum WBC, ESR, and CRP. Mathews and Coakley (2008) stated that though serum WBC, ESR, CRP, and temperature were all useful in the evaluation of an acute joint, they were not diagnostic [17].…”

Section: Discussionmentioning

confidence: 99%

Septic Arthritis of the Pediatric Shoulder: From Infancy to Adolescence

Walker

Hennrikus

2016

International Journal of Pediatrics

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Background. Septic arthritis of the glenohumeral joint in the pediatric population (<18 yo) is not commonly described in the literature. There is a corresponding paucity of information regarding its presentation and treatment. Methods. An IRB approved review of patients treated with irrigation and debridement by the Orthopaedic Department was completed. This retrospective study includes four patients, presenting from 2005 to 2015, with septic arthritis of the shoulder. Results. The mean age (M age) at presentation was 5 years, with a range of 1 month to 15 years. Patients presented on average after 7 days with pain and a mean temperature of 39°C, erythrocyte sedimentation rate of 66 mm/hr, a C-reactive protein level of 11.17 g/dL, and a white blood cell count of 20.2 × 103/mcL. Staphylococcus aureus, Candida albicans, and Pseudomonas aeruginosa were cultured from the wounds. All cases were treated operatively with irrigation and debridement and with antimicrobial therapy. Patients received antibiotics for an average of 6 weeks. Conclusion. Septic arthritis of the shoulder occurs in all pediatric ages. Successful treatment of septic arthritis of the shoulder was accomplished in four cases without division of the biceps sheath, with an average follow-up of 8 months.

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The child with joint pain in primary care

Cited by 17 publications

References 99 publications

Time to diagnosis in juvenile idiopathic arthritis: a french perspective

Time to diagnosis in juvenile idiopathic arthritis: a french perspective

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

Septic Arthritis of the Pediatric Shoulder: From Infancy to Adolescence

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