Report of two cases with omovertebral bone and Klippel–Feil syndrome with craniovertebral junction instability

Shah, Abhidha; Hawaldar, Akshay; Lunawat, Aditya; Dandpat, Saswat; Goel, Atul

doi:10.4103/jcvjs.jcvjs_7_21

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Also, they exhibited MRI features showing CM-1, increased BI in flexion position, and morphological anomalies on CT-scans such as C2-C3 fusions. Similar findings were observed for cases 5 and 6 with Klippel-Feil syndrome, where segmental fusions of the cervical spine are commonly found [36,37].…”

Section: Insights Of Patient Clustering and Clinical Correlationsupporting

confidence: 85%

Mobility Assessment Using Multi-Positional MRI in Children with Cranio-Vertebral Junction Anomalies

Grenier-Chartrand,

Taverne,

James

et al. 2023

JCM

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Objective: This study aimed to assess the relevance of using multi-positional MRI (mMRI) to identify cranio-vertebral junction (CVJ) instability in pediatric patients with CVJ anomalies while determining objective mMRI criteria to detect this condition. Material and Methods: Data from children with CVJ anomalies who underwent a mMRI between 2017 and 2021 were retrospectively reviewed. Mobility assessment using mMRI involved: (1) morphometric analysis using hierarchical clustering on principal component analysis (HCPCA) to identify clusters of patients by considering their mobility similarities, assessed through delta (Δ) values of occipito-cervical parameters measured on mMRI; and (2) morphological analysis based on dynamic geometric CVJ models and analysis of displacement vectors between flexion and extension. Receiver operating characteristics (ROC) curves were generated for occipito-cervical parameters to establish instability cut-off values. (3) Additionally, an anatomical qualitative analysis of the CVJ was performed to identify morphological criteria of instability. Results: Forty-seven patients with CVJ anomalies were included (26 females, 21 males; mean age: 10.2 years [3–18]). HCPCA identified 2 clusters: cluster №1 (stable patients, n = 39) and cluster №2 (unstable patients, n = 8). ΔpB-C2 (pB-C2 line delta) at ≥2.5 mm (AUC 0.98) and ΔBAI (Basion-axis Interval delta) ≥ 3 mm (AUC 0.97) predicted instability with 88% sensibility and 95% specificity and 88% sensitivity and 85% specificity, respectively. Geometric CVJ shape analysis differentiated patients along a continuum, from a low to a high CVJ motion that was characterized by a subluxation of C1 in the anterior direction. Qualitative analysis found correlations between instability and C2 anomalies, including fusions with C3 (body p = 0.032; posterior arch p = 0.045; inferior articular facets p = 0.012; lateral mass p = 0.029). Conclusions: We identified a cluster of pediatric patients with CVJ instability among a cohort of CVJ anomalies that were characterized by morphometric parameters with corresponding cut-off values that could serve as objective mMRI criteria. These findings warrant further validation through prospective case–control studies.

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Section: Insights Of Patient Clustering and Clinical Correlationsupporting

confidence: 85%