Rheumatology Unit, Department of Woman's and Child's Health, University of Padova, Via Giustiniani 3, 35128, Padua, Italy.
Pediatric Unit, Sant'Eugenio Hospital, Rome, Italy.
Eur J Pediatr. 2021 Oct;180(10):3229-3235. doi: 10.1007/s00431-021-04058-3. Epub 2021 Apr 8.
Juvenile osteoperiostites (JOP) are a group of inflammatory bone diseases whose differential diagnosis is often difficult. The main conditions are acute osteomyelitis (AOM), chronic non-bacterial osteomyelitis (CNO) and the Goldbloom syndrome (GS). The study was aimed to develop an algorithm to enable an early diagnosis of JOP. Clinical records of patients with AOM, CNO and GS, followed at our Center over the past 10 years, were reviewed. Twelve additional patients with GS were selected from PubMed/MEDLINE literature search. Data collected included demographics, clinical manifestations, laboratory and instrumental investigations at disease onset. The association between categorical variables was investigated, and the segmentation of patients with different diagnoses was analyzed through a classification tree model (CTREE package) in order to build up a diagnostic algorithm. Ninety-two patients (33 CNO, 44 AOM, 15 GS) entered the study. Among 30 variables considered at onset, nine (age at onset, fever, weight loss, symmetry, focality, functional limitation, anemia, elevated ESR, CRP) resulted statistically significant in differentiating the three clinical entities from each other and were chosen to build up a decisional tree. Three variables, symmetry of bone involvement, presence of fever and age at disease onset, resulted significant to discriminate each of the three diseases from the others. The performance of the diagnostic algorithm was validated by comparing the diagnoses provided by the model with the real diagnoses and showed 85.9% accuracy.Conclusion: We propose a diagnostic algorithm, based on simple clinical data, which can help guide a prompt and appropriate diagnosis of JOP. What is Known: • Juvenile osteoperiostitis (JOP) are a group of inflammatory bone diseases followed by various pediatric specialists. • The distinction between these conditions is not easy as clinical and laboratory features often overlap. What is New: • We propose a diagnostic algorithm, based on clinical data of real patients, with high degree accuracy. • This instrument can help guide the prompt and appropriate diagnosis of JOP.
青少年骨皮质增生症（JOP）是一组炎症性骨病，其鉴别诊断通常较为困难。主要疾病包括急性骨髓炎（AOM）、慢性非细菌性骨髓炎（CNO）和 Goldbloom 综合征（GS）。本研究旨在制定一种算法，以实现 JOP 的早期诊断。我们回顾了过去 10 年在本中心就诊的 AOM、CNO 和 GS 患者的临床记录。此外，我们还从 PubMed/MEDLINE 文献检索中选择了 12 例 GS 患者。收集的数据包括发病时的人口统计学、临床表现、实验室和仪器检查。我们研究了分类变量之间的关联，并通过分类树模型（CTREE 包）对不同诊断的患者进行分段分析，以建立诊断算法。92 例患者（33 例 CNO、44 例 AOM、15 例 GS）纳入研究。在发病时考虑的 30 个变量中，9 个（发病年龄、发热、体重减轻、对称性、局灶性、功能受限、贫血、ESR 升高、CRP）在区分三种临床实体方面具有统计学意义，并被选为建立决策树的变量。3 个变量，即骨骼受累的对称性、发热的存在和发病年龄，对鉴别三种疾病具有显著意义。通过比较模型提供的诊断与实际诊断，验证了诊断算法的性能，其准确率为 85.9%。结论：我们提出了一种基于简单临床数据的诊断算法，可以帮助指导 JOP 的及时和适当诊断。已知：•青少年骨皮质增生症（JOP）是一组由各种儿科专家治疗的炎症性骨病。•这些疾病的临床和实验室特征经常重叠，因此区分这些疾病并不容易。新发现：•我们提出了一种基于真实患者临床数据的诊断算法，具有较高的准确性。•该工具可以帮助指导 JOP 的及时和适当诊断。
